System

Abstract

An object of a system according to an embodiment is to propose an optimal book on the basis of a customer's reading history or preference and to improve a reading experience.SOLUTION: A system includes a collection part, an analysis part, a proposal part, a bulletin board part, a collection part, a generation part, and a provision part. The collection unit collects a reading history of a customer. The analysis unit analyzes the data collected by the collection unit and grasps the preference and interest of the customer. The proposal unit proposes a book on the basis of the analysis result obtained by the analysis unit. The bulletin board section provides a bulletin board on which customers who read the same book can share their impressions. The collection unit collects book information. The generation unit analyzes the information collected by the collection unit and generates a review or a book review. The providing unit provides the information generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest optimal books based on a customer's reading history and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the reading experience by suggesting the most suitable books based on the customer's reading history and preferences. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a bulletin board unit, a collection unit, a generation unit, and a provision unit. The collection unit collects customers' reading histories. The analysis unit analyzes the data collected by the collection unit to understand the customers' preferences and interests. The suggestion unit suggests books based on the analysis results obtained by the analysis unit. The bulletin board unit provides a bulletin board where customers who have read the same book can share their impressions. The collection unit collects book information. The generation unit analyzes the information collected by the collection unit and generates reviews and critiques. The provision unit provides the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable books based on the customer's reading history and preferences, improving the reading experience. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The system according to an embodiment of the present invention provides three main functions: a book matching service, a reading community, and a book information database. This system analyzes customers' reading habits and preferences and suggests recommended books. Unlike conventional recommendation systems, AI interacts with customers to provide a deeper reading experience. For example, it lists recommended books based on the customer's past reading and purchase history. It also recommends books related to the customer's genres and themes, and recommends books that can help with the customer's concerns and problems. Next, the system provides a reading community, a platform where people with similar interests can interact with each other. AI further enhances the customer's reading experience by planning and managing reading groups and events. For example, it provides a bulletin board where people who have read the same book can share their impressions and provides information about upcoming reading groups and events. It also provides a function for participating in reading groups and events online. Finally, the system provides a book information database that comprehensively covers all book-related information. AI automatically generates book reviews and critiques to help customers make purchasing decisions. For example, it provides information such as book summaries, author information, critiques, recommendations for similar books, and links to e-book sales sites. This allows the system to enhance the customer's reading experience, promote interaction with people who share the same interests, and provide comprehensive information about books. For example, customers can find books that match their preferences and interests, and interact with people who share the same interests, thereby enhancing their reading experience. In addition, the system can assist customers in making purchasing decisions by using a comprehensive database of information about books.

[0029] A book matching system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a bulletin board unit, a generation unit, and a provision unit. The collection unit collects a customer's reading history. The customer's reading history may include, but is not limited to, the titles of books read, the date of completion, and the reading time. The collection unit may collect, for example, the customer's past reading history and purchase history. The analysis unit analyzes the data collected by the collection unit to understand the customer's preferences and interests. The analysis is performed, for example, using data mining technology or a machine learning algorithm. The suggestion unit suggests books based on the analysis results obtained by the analysis unit. The suggestion is performed, for example, using a recommendation algorithm based on the customer's preferences and interests. The bulletin board unit provides a bulletin board where customers who have read the same book can share their impressions. The impressions are shared in, for example, text format, image format, audio format, etc. The collection unit collects information such as book summaries, author information, book reviews, reviews, introductions to similar books, and links to sales sites for e-book versions of the book. The generation unit analyzes the information collected by the collection unit and generates reviews and critiques. The generation is performed using, for example, natural language generation technology. The providing unit provides the information generated by the generation unit. The provision is performed, for example, through a website, an application, email, etc. As a result, the book matching system according to the embodiment can deepen the customer's reading experience, promote interaction between people with the same interests, and provide comprehensive information about books.

[0030] The collection unit can collect the customer's past reading history and purchase history. The reading history includes, for example, the titles of books read, the date of completion, and the reading time. The purchase history includes, for example, the titles of books purchased, the purchase date, and the place of purchase. The collection unit, for example, acquires the customer's past reading history from a database and provides it to the analysis unit. The collection unit can also acquire the customer's purchase history from the database of an online bookstore and provide it to the analysis unit. In this way, by collecting the customer's past reading history and purchase history, more accurate suggestions can be made.

[0031] The analysis unit analyzes the collected data to understand the customer's preferences and interests. The analysis unit analyzes the collected data using, for example, data mining technology. For example, the analysis unit understands the customer's preferences and interests based on the customer's past reading history and purchase history. The analysis unit can also predict the customer's reading trends using machine learning algorithms. For example, the analysis unit identifies books that the customer may be interested in based on the genres and themes of books the customer has read in the past. This allows the analysis unit to understand the customer's preferences and interests and suggest more appropriate books.

[0032] The suggestion unit can suggest books based on the analysis results. For example, the suggestion unit suggests books using a recommendation algorithm based on the customer's preferences and interests. For example, the suggestion unit lists books that the customer might be interested in based on the customer's past reading history and purchase history. The suggestion unit can also introduce books related to genres and themes that the customer is interested in. Furthermore, the suggestion unit can also introduce books that will help with the worries and challenges the customer is facing. In this way, by suggesting books based on the analysis results, it is possible to provide books that match the customer's interests.

[0033] The bulletin board unit can provide a bulletin board where people who have read the same book can share their impressions. The bulletin board unit provides a bulletin board where people who have read the same book can share their impressions, for example, in text format, image format, audio format, etc. For example, the bulletin board unit provides a bulletin board where customers who have read the same book can post their impressions. The bulletin board unit can also provide information about book clubs and events. For example, the bulletin board unit provides a function that allows people who have read the same book to participate in book clubs and events online. This allows people who have read the same book to share their impressions, deepening the reading experience.

[0034] The collection unit can collect information such as a book's synopsis, author information, book reviews, reviews, introductions to similar books, and links to sales sites for e-book versions of the book. The collection unit collects information such as a book's synopsis, author information, book reviews, reviews, introductions to similar books, and links to sales sites for e-book versions of the book. For example, the collection unit obtains book information from databases of online bookstores and publishers. The collection unit can also collect reviews and book reviews from book review sites and book critique sites. In this way, collecting a variety of information about books can assist customers in making purchasing decisions.

[0035] The generation unit can analyze the collected information and generate reviews and book reviews. The generation unit can analyze the collected information using, for example, natural language generation technology and generate reviews and book reviews. For example, the generation unit can automatically generate book reviews and book reviews based on the book's synopsis and author information. The generation unit can also analyze the collected reviews and book reviews and generate summaries. In this way, analyzing the collected information and generating reviews and book reviews supports customers in making purchasing decisions.

[0036] The providing unit can provide the generated information. The providing unit provides the generated information, for example, through a website, an application, email, etc. For example, the providing unit posts book reviews and critiques on a website. The providing unit can also provide book information to customers through an application. Furthermore, the providing unit can send book information to customers by email. In this way, providing the generated information supports customers in making purchasing decisions.

[0037] The bulletin board section can provide information about upcoming reading groups and events. For example, the bulletin board section provides a function that allows customers to participate in online reading groups and events. The bulletin board section can also provide a bulletin board where customers who have read the same book can share their impressions. This allows customers to have a deeper reading experience by providing information about upcoming reading groups and events.

[0038] The message board unit can provide a function that allows customers to participate in online reading groups and events. For example, the message board unit can enable customers to participate in online reading groups and events through video conferencing or chat rooms. The message board unit can also support the holding of reading groups and events in a webinar format. This can deepen customers' reading experience by providing a function that allows customers to participate in online reading groups and events.

[0039] The collection unit can analyze the customer's past reading history and select the optimal collection method. The collection unit, for example, analyzes the customer's past reading history and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the customer has frequently used in the past. The collection unit can also set the optimal collection timing based on the time period the customer has used in the past. Furthermore, the collection unit can also prioritize collecting related data based on the genres that the customer has preferred to read in the past. In this way, the optimal collection method can be selected by analyzing the customer's past reading history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The collection unit can filter the reading history based on the customer's current living situation and areas of interest when collecting the reading history. For example, when collecting the reading history, the collection unit filters the reading history based on the customer's current living situation and areas of interest. For example, if the customer is busy in their current living situation, the collection unit can prioritize collecting a history of books that can be read in a short time. Also, if the customer is interested in a particular area of ​​interest, the collection unit can prioritize collecting a history of books related to that area. Furthermore, if the customer has started a new hobby, the collection unit can collect a history of books related to that hobby. In this way, by filtering based on the customer's current living situation and areas of interest, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0041] When collecting a reading history, the collection unit can select the optimal collection means depending on the customer's input method. For example, when collecting a reading history, the collection unit selects the optimal collection means depending on the customer's input method (voice, text, image, etc.). For example, if the customer prefers voice input, the collection unit can collect the reading history using voice recognition technology. Also, if the customer prefers text input, the collection unit can collect the reading history using text analysis technology. Furthermore, if the customer prefers image input, the collection unit can collect the reading history using image recognition technology. In this way, by selecting the optimal collection means depending on the customer's input method, data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0042] When collecting reading histories, the collection unit can prioritize collecting highly relevant histories by taking into account the customer's geographical location information. For example, when collecting reading histories, the collection unit prioritizes collecting highly relevant histories by taking into account the customer's geographical location information. For example, when collecting reading histories, the collection unit prioritizes collecting histories of books related to the customer's travel destination if the customer is traveling. Furthermore, when the customer lives in a specific area, the collection unit can prioritize collecting histories of books related to that area. Furthermore, when the customer is participating in a specific event, the collection unit can prioritize collecting histories of books related to that event. In this way, highly relevant data can be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0043] The collection unit can analyze the customer's social media activities and collect related histories when collecting reading histories. For example, the collection unit analyzes the customer's social media activities and collects related histories when collecting reading histories. For example, the collection unit prioritizes collecting histories of books shared by the customer on social media. The collection unit can also prioritize collecting histories of books by authors the customer follows on social media. Furthermore, the collection unit can prioritize collecting histories of books in reading groups the customer participates in on social media. This allows for efficient collection of related data by analyzing the customer's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0044] The collection unit can customize the collection method by reflecting the customer's past feedback when collecting the reading history. For example, the collection unit customizes the collection method by reflecting the customer's past feedback when collecting the reading history. For example, the collection unit preferentially uses collection methods that the customer has previously preferred. The collection unit can also avoid collection methods that the customer has previously dissatisfied with. Furthermore, the collection unit can suggest new collection methods based on the customer's past feedback. In this way, a more appropriate collection method can be selected by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a concise analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a sentiment analysis algorithm to fiction books. The analysis unit can also apply a fact-checking algorithm to non-fiction books. Furthermore, the analysis unit can apply a terminology analysis algorithm to science books. In this way, applying different analysis algorithms depending on the category of data enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during the analysis. For example, the analysis unit performs a similar analysis based on analysis results that the customer preferred in the past. The analysis unit can also avoid analysis results that the customer was dissatisfied with in the past. Furthermore, the analysis unit can also propose a new analysis method based on the customer's past analysis results. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted recently. Furthermore, the analysis unit can also appropriately prioritize data that was submitted recently. In this way, determining the analysis priority based on the time of data submission enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can also appropriately prioritize data with medium relevance. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise during the analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise during the analysis. For example, the analysis unit can provide an analysis that uses a lot of technical terminology to a customer with high level of expertise. The analysis unit can also provide a concise and easy-to-understand analysis to a customer with low level of expertise. Furthermore, the analysis unit can provide an analysis that uses appropriate technical terminology to a customer with medium level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the customer's level of expertise, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the book when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the book when making a suggestion. For example, the suggestion unit makes detailed suggestions for books with high importance. The suggestion unit can also make brief suggestions for books with low importance. Furthermore, the suggestion unit can also make suggestions with an appropriate level of detail for books with medium importance. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the book. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0052] The suggestion unit can apply different suggestion algorithms depending on the book category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the book category when making a suggestion. For example, the suggestion unit applies a sentiment analysis algorithm to fiction books. The suggestion unit can also apply a fact-checking algorithm to non-fiction books. Furthermore, the suggestion unit can apply a terminology analysis algorithm to science books. In this way, applying different suggestion algorithms depending on the book category enables more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0053] The suggestion unit can improve the accuracy of the suggestion by referring to the customer's past suggestion results when making a suggestion. For example, the suggestion unit makes a similar suggestion based on a suggestion that the customer liked in the past. The suggestion unit can also avoid a suggestion that the customer was dissatisfied with in the past. Furthermore, the suggestion unit can also suggest a new suggestion method based on the customer's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the customer's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0054] The suggestion unit can determine the priority of suggestions based on the submission date of the books when making suggestions. The suggestion unit, for example, determines the priority of suggestions based on the submission date of the books when making suggestions. For example, the suggestion unit preferentially suggests the latest books. The suggestion unit can also postpone books that were submitted earlier. Furthermore, the suggestion unit can moderately prioritize books that were submitted at an intermediate date. This enables efficient suggestions by determining the priority of suggestions based on the submission date of the books. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of books when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of books when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant books. The suggestion unit can also postpone suggesting books with low relevance. Furthermore, the suggestion unit can also moderately prioritize books with medium relevance. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of books. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0056] The suggestion unit can adjust the use of technical terminology in the proposal according to the customer's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the customer's level of expertise when making a proposal. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a customer with high level of expertise. The suggestion unit can also provide a concise and easy-to-understand proposal to a customer with low level of expertise. Furthermore, the suggestion unit can also provide a proposal that uses appropriate technical terminology to a customer with medium level of expertise. In this way, adjusting the use of technical terminology in the proposal according to the customer's level of expertise enables more appropriate proposals. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0057] When displaying a message board, the message board unit can select the optimal display method by referring to the customer's past posting history. When displaying a message board, the message board unit, for example, selects the optimal display method by referring to the customer's past posting history. For example, the message board unit prioritizes using a display method that the customer has previously preferred. The message board unit can also avoid display methods that the customer has previously dissatisfied with. Furthermore, the message board unit can also suggest a new display method based on the customer's past posting history. In this way, the optimal display method can be selected by referring to the customer's past posting history. Some or all of the above-mentioned processing in the message board unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The bulletin board unit can customize the display content according to the customer's current task when displaying the bulletin board. For example, when displaying the bulletin board, the bulletin board unit customizes the display content according to the customer's current task. For example, if the customer is reading, the bulletin board unit can prioritize displaying information about related books. Also, if the customer is participating in an event, the bulletin board unit can prioritize displaying information related to the event. Furthermore, if the customer is looking for a new book, the bulletin board unit can prioritize displaying information about the latest books. In this way, by customizing the display content according to the customer's current task, more appropriate information can be provided. Some or all of the above-mentioned processing in the bulletin board unit may be performed using, for example, AI, or may be performed without using AI.

[0059] The bulletin board unit can improve the display method by reflecting customer feedback when displaying the bulletin board. For example, the bulletin board unit improves the display method by reflecting customer feedback when displaying the bulletin board. For example, the bulletin board unit preferentially uses a display method that the customer has previously preferred. The bulletin board unit can also avoid a display method that the customer has previously dissatisfied with. Furthermore, the bulletin board unit can suggest a new display method based on the customer's past feedback. In this way, the display method can be improved by reflecting customer feedback, and more appropriate information can be provided. Some or all of the above-mentioned processing in the bulletin board unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The bulletin board unit can select the optimal display method by taking into consideration the customer's device information when displaying the bulletin board. For example, when displaying the bulletin board, the bulletin board unit selects the optimal display method by taking into consideration the customer's device information. For example, if the customer is using a smartphone, the bulletin board unit can provide a display method that matches the screen size. Also, if the customer is using a tablet, the bulletin board unit can provide a display method that is optimized for a large screen. Furthermore, if the customer is using a PC, the bulletin board unit can provide a display method that includes detailed information. In this way, the optimal display method can be selected by taking into consideration the customer's device information. Some or all of the above-mentioned processing in the bulletin board unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The bulletin board unit can make the display content multilingual when displaying the bulletin board according to the customer's language setting. For example, the bulletin board unit can automatically set the bulletin board language based on the language setting of the customer's device. The bulletin board unit can also provide a language switching function when a customer uses multiple languages. Furthermore, if a customer selects a specific language, the bulletin board unit can display the bulletin board in that language. This makes it possible to provide more appropriate information by making the display content multilingual according to the customer's language setting. Some or all of the above-mentioned processing in the bulletin board unit may be performed, for example, using AI or without using AI.

[0062] When displaying the message board, the message board unit can prioritize displaying highly relevant information by taking into account the geographical location information of the customer. For example, when displaying the message board, the message board unit prioritizes displaying highly relevant information by taking into account the geographical location information of the customer. For example, if a customer lives in a specific area, the message board unit can prioritize displaying information related to that area. Also, if a customer is traveling, the message board unit can prioritize displaying information related to the travel destination. Furthermore, if a customer is participating in a specific event, the message board unit can prioritize displaying information related to the event. In this way, by taking into account the geographical location information of the customer, highly relevant information can be prioritized and displayed. Some or all of the above-described processing in the message board unit may be performed, for example, using AI, or may be performed without using AI.

[0063] The generation unit can adjust the level of detail of the generation based on the importance of the book at the time of generation. The generation unit, for example, adjusts the level of detail of the generation based on the importance of the book at the time of generation. For example, the generation unit generates detailed reviews and critiques for books with high importance. The generation unit can also generate brief reviews and critiques for books with low importance. Furthermore, the generation unit can also generate reviews and critiques with an appropriate level of detail for books with medium importance. In this way, adjusting the level of detail of the generation based on the importance of the book enables efficient generation. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0064] The generation unit can apply different generation algorithms depending on the book category during generation. For example, the generation unit applies different generation algorithms depending on the book category during generation. For example, the generation unit applies a sentiment analysis algorithm to fiction books. The generation unit can also apply a fact-checking algorithm to non-fiction books. Furthermore, the generation unit can apply a terminology analysis algorithm to science books. In this way, applying different generation algorithms depending on the book category enables more accurate generation. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0065] The generation unit can improve the accuracy of generation by referring to the customer's past generation results during generation. The generation unit can improve the accuracy of generation by referring to the customer's past generation results during generation, for example. For example, the generation unit generates a similar result based on a generation result that the customer liked in the past. The generation unit can also avoid a generation result that the customer was dissatisfied with in the past. Furthermore, the generation unit can propose a new generation method based on the customer's past generation results. In this way, the accuracy of generation is improved by referring to the customer's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0066] The generation unit can determine the generation priority based on the submission date of the book at the time of generation. The generation unit, for example, determines the generation priority based on the submission date of the book at the time of generation. For example, the generation unit generates the most recent books with priority. The generation unit can also postpone books that were submitted earlier. Furthermore, the generation unit can also give moderate priority to books that were submitted at a medium time. In this way, efficient generation is possible by determining the generation priority based on the submission date of the book. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0067] The generation unit can adjust the order of generation based on the relevance of the books during generation. The generation unit, for example, adjusts the order of generation based on the relevance of the books during generation. For example, the generation unit generates books with high relevance with priority. The generation unit can also postpone books with low relevance. Furthermore, the generation unit can also give moderate priority to books with medium relevance. In this way, adjusting the order of generation based on the relevance of the books enables efficient generation. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0068] The generation unit can adjust the use of technical terminology in the generation according to the customer's level of expertise at the time of generation. For example, the generation unit adjusts the use of technical terminology in the generation according to the customer's level of expertise at the time of generation. For example, the generation unit generates reviews and book reviews that use a lot of technical terminology for customers with high levels of expertise. The generation unit can also generate concise and easy-to-understand reviews and book reviews for customers with low levels of expertise. Furthermore, the generation unit can generate reviews and book reviews that use appropriate technical terminology for customers with medium levels of expertise. In this way, by adjusting the use of technical terminology in the generation according to the customer's level of expertise, more appropriate reviews and book reviews can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0069] The providing unit can select the optimal delivery method by referring to the customer's past usage history when providing information. For example, the providing unit selects the optimal delivery method by referring to the customer's past usage history when providing information. For example, the providing unit preferentially uses a delivery method that the customer has preferred in the past. The providing unit can also avoid delivery methods that the customer has been dissatisfied with in the past. Furthermore, the providing unit can also suggest a new delivery method based on the customer's past usage history. In this way, the optimal delivery method can be selected by referring to the customer's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0070] The providing unit can customize the provided content according to the customer's current needs when providing information. The providing unit customizes the provided content according to the customer's current needs when providing information, for example. For example, if a customer is looking for a new book, the providing unit can provide the latest book information. Also, if a customer is interested in a particular genre, the providing unit can provide information related to that genre. Furthermore, if a customer has a particular problem, the providing unit can provide information that is useful for that problem. In this way, by customizing the provided content according to the customer's current needs, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0071] The providing unit can improve the method of providing information by reflecting customer feedback when providing information. For example, the providing unit improves the method of providing information by reflecting customer feedback when providing information. For example, the providing unit preferentially uses a method of providing information that the customer has previously preferred. The providing unit can also avoid a method of providing information that the customer has previously dissatisfied with. Furthermore, the providing unit can propose a new method of providing information based on the customer's past feedback. In this way, the method of providing information can be improved by reflecting customer feedback, and more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0072] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the geographical location information of the customer. For example, when providing information, the providing unit prioritizes providing highly relevant information by taking into account the geographical location information of the customer. For example, if the customer lives in a specific area, the providing unit can prioritize providing information related to that area. Also, if the customer is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, if the customer is participating in a specific event, the providing unit can prioritize providing information related to the event. In this way, by taking into account the geographical location information of the customer, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0073] The providing unit can analyze the customer's social media activity and provide related information when providing information. For example, the providing unit can analyze the customer's social media activity and provide related information when providing information. For example, the providing unit can provide information about books that the customer has shared on social media. The providing unit can also provide information about authors that the customer follows on social media. Furthermore, the providing unit can provide information about reading groups that the customer has joined on social media. This makes it possible to efficiently provide related information by analyzing the customer's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0074] The providing unit can customize the delivery method by reflecting the customer's past feedback when providing information. For example, the providing unit customizes the delivery method by reflecting the customer's past feedback when providing information. For example, the providing unit preferentially uses a delivery method that the customer has previously preferred. The providing unit can also avoid delivery methods that the customer has previously been dissatisfied with. Furthermore, the providing unit can propose a new delivery method based on the customer's past feedback. In this way, a more appropriate delivery method can be selected by reflecting the customer's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The collection unit can also analyze the customer's social media activity and collect related reading history. For example, it can prioritize collection of the history of books that the customer has shared on social media. It can also collect the history of books by authors that the customer follows on social media. It can also collect the history of books in reading groups that the customer has joined on social media. This allows for efficient collection of related data by analyzing the customer's social media activity.

[0077] The bulletin board unit can also prioritize displaying highly relevant information by taking into account the geographical location information of the customer. For example, if a customer lives in a specific area, information related to that area can be prioritized. Also, if a customer is traveling, information related to the travel destination can be prioritized. Furthermore, if a customer is participating in a specific event, information related to that event can be prioritized. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the customer.

[0078] The provision unit can also customize the information provision method by reflecting the customer's past feedback. For example, it can prioritize the use of a provision method that the customer has previously preferred. It can also avoid a provision method that the customer has previously been dissatisfied with. Furthermore, it can also propose a new provision method based on the customer's past feedback. In this way, it is possible to select a more appropriate provision method by reflecting the customer's past feedback.

[0079] The analysis unit can also adjust the use of technical terms in the analysis according to the customer's level of expertise. For example, it can provide an analysis that uses a lot of technical terms to a customer with a high level of expertise. It can also provide a concise and easy-to-understand analysis to a customer with a low level of expertise. It can also provide an analysis that uses appropriate technical terms to a customer with a medium level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the customer's level of expertise, it is possible to provide more appropriate analysis results.

[0080] The proposal unit can also improve the accuracy of proposals by referring to the customer's past proposal results. For example, it can make similar proposals based on proposals that the customer liked in the past. It can also avoid proposals that the customer was dissatisfied with in the past. Furthermore, it can also propose new proposal methods based on the customer's past proposal results. In this way, the accuracy of proposals can be improved by referring to the customer's past proposal results.

[0081] The processing flow of the first embodiment will be briefly explained below.

[0082] Step 1: The collection unit collects the customer's reading history. The customer's reading history includes, but is not limited to, the titles of books read, the date of completion, and the reading time. The collection unit may also collect the customer's past reading history and purchase history. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the customer's preferences and interests. The analysis is performed using, for example, data mining techniques or machine learning algorithms. Step 3: The suggestion unit suggests books based on the analysis results obtained by the analysis unit. The suggestions are made using, for example, a recommendation algorithm based on the customer's preferences and interests. Step 4: The message board section provides a message board where customers who have read the same book can share their impressions with each other. Impressions can be shared in text, image, audio, or other formats, for example. Step 5: The collection department collects information such as the book's synopsis, author information, book reviews, recommendations for similar books, and links to sites selling the e-book version of the book. Step 6: The generator analyzes the information collected by the collector and generates reviews and critiques. Generation is performed using, for example, natural language generation technology. Step 7: The providing unit provides the information generated by the generating unit. The provision is performed, for example, through a website, an application, or email.

[0083] (Example 2) The system according to an embodiment of the present invention provides three main functions: a book matching service, a reading community, and a book information database. This system analyzes customers' reading habits and preferences and suggests recommended books. Unlike conventional recommendation systems, AI interacts with customers to provide a deeper reading experience. For example, it lists recommended books based on the customer's past reading and purchase history. It also recommends books related to the customer's genres and themes, and recommends books that can help with the customer's concerns and problems. Next, the system provides a reading community, a platform where people with similar interests can interact with each other. AI further enhances the customer's reading experience by planning and managing reading groups and events. For example, it provides a bulletin board where people who have read the same book can share their impressions and provides information about upcoming reading groups and events. It also provides a function for participating in reading groups and events online. Finally, the system provides a book information database that comprehensively covers all book-related information. AI automatically generates book reviews and critiques to help customers make purchasing decisions. For example, it provides information such as book summaries, author information, critiques, recommendations for similar books, and links to e-book sales sites. This allows the system to enhance the customer's reading experience, promote interaction with people who share the same interests, and provide comprehensive information about books. For example, customers can find books that match their preferences and interests, and interact with people who share the same interests, thereby enhancing their reading experience. In addition, the system can assist customers in making purchasing decisions by using a comprehensive database of information about books.

[0084] A book matching system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a bulletin board unit, a generation unit, and a provision unit. The collection unit collects a customer's reading history. The customer's reading history may include, but is not limited to, the titles of books read, the date of completion, and the reading time. The collection unit may collect, for example, the customer's past reading history and purchase history. The analysis unit analyzes the data collected by the collection unit to understand the customer's preferences and interests. The analysis is performed, for example, using data mining technology or a machine learning algorithm. The suggestion unit suggests books based on the analysis results obtained by the analysis unit. The suggestion is performed, for example, using a recommendation algorithm based on the customer's preferences and interests. The bulletin board unit provides a bulletin board where customers who have read the same book can share their impressions. The impressions are shared in, for example, text format, image format, audio format, etc. The collection unit collects information such as book summaries, author information, book reviews, reviews, introductions to similar books, and links to sales sites for e-book versions of the book. The generation unit analyzes the information collected by the collection unit and generates reviews and critiques. The generation is performed using, for example, natural language generation technology. The providing unit provides the information generated by the generation unit. The provision is performed, for example, through a website, an application, email, etc. As a result, the book matching system according to the embodiment can deepen the customer's reading experience, promote interaction between people with the same interests, and provide comprehensive information about books.

[0085] The collection unit can collect the customer's past reading history and purchase history. The reading history includes, for example, the titles of books read, the date of completion, and the reading time. The purchase history includes, for example, the titles of books purchased, the purchase date, and the place of purchase. The collection unit, for example, acquires the customer's past reading history from a database and provides it to the analysis unit. The collection unit can also acquire the customer's purchase history from the database of an online bookstore and provide it to the analysis unit. In this way, by collecting the customer's past reading history and purchase history, more accurate suggestions can be made.

[0086] The analysis unit analyzes the collected data to understand the customer's preferences and interests. The analysis unit analyzes the collected data using, for example, data mining technology. For example, the analysis unit understands the customer's preferences and interests based on the customer's past reading history and purchase history. The analysis unit can also predict the customer's reading trends using machine learning algorithms. For example, the analysis unit identifies books that the customer may be interested in based on the genres and themes of books the customer has read in the past. This allows the analysis unit to understand the customer's preferences and interests and suggest more appropriate books.

[0087] The suggestion unit can suggest books based on the analysis results. For example, the suggestion unit suggests books using a recommendation algorithm based on the customer's preferences and interests. For example, the suggestion unit lists books that the customer might be interested in based on the customer's past reading history and purchase history. The suggestion unit can also introduce books related to genres and themes that the customer is interested in. Furthermore, the suggestion unit can also introduce books that will help with the worries and challenges the customer is facing. In this way, by suggesting books based on the analysis results, it is possible to provide books that match the customer's interests.

[0088] The bulletin board unit can provide a bulletin board where people who have read the same book can share their impressions. The bulletin board unit provides a bulletin board where people who have read the same book can share their impressions, for example, in text format, image format, audio format, etc. For example, the bulletin board unit provides a bulletin board where customers who have read the same book can post their impressions. The bulletin board unit can also provide information about book clubs and events. For example, the bulletin board unit provides a function that allows people who have read the same book to participate in book clubs and events online. This allows people who have read the same book to share their impressions, deepening the reading experience.

[0089] The collection unit can collect information such as a book's synopsis, author information, book reviews, reviews, introductions to similar books, and links to sales sites for e-book versions of the book. The collection unit collects information such as a book's synopsis, author information, book reviews, reviews, introductions to similar books, and links to sales sites for e-book versions of the book. For example, the collection unit obtains book information from databases of online bookstores and publishers. The collection unit can also collect reviews and book reviews from book review sites and book critique sites. In this way, collecting a variety of information about books can assist customers in making purchasing decisions.

[0090] The generation unit can analyze the collected information and generate reviews and book reviews. The generation unit can analyze the collected information using, for example, natural language generation technology and generate reviews and book reviews. For example, the generation unit can automatically generate book reviews and book reviews based on the book's synopsis and author information. The generation unit can also analyze the collected reviews and book reviews and generate summaries. In this way, analyzing the collected information and generating reviews and book reviews supports customers in making purchasing decisions.

[0091] The providing unit can provide the generated information. The providing unit provides the generated information, for example, through a website, an application, email, etc. For example, the providing unit posts book reviews and critiques on a website. The providing unit can also provide book information to customers through an application. Furthermore, the providing unit can send book information to customers by email. In this way, providing the generated information supports customers in making purchasing decisions.

[0092] The bulletin board section can provide information about upcoming reading groups and events. For example, the bulletin board section provides a function that allows customers to participate in online reading groups and events. The bulletin board section can also provide a bulletin board where customers who have read the same book can share their impressions. This allows customers to have a deeper reading experience by providing information about upcoming reading groups and events.

[0093] The message board unit can provide a function that allows customers to participate in online reading groups and events. For example, the message board unit can enable customers to participate in online reading groups and events through video conferencing or chat rooms. The message board unit can also support the holding of reading groups and events in a webinar format. This can deepen customers' reading experience by providing a function that allows customers to participate in online reading groups and events.

[0094] The collection unit can estimate a customer's emotions and adjust the timing of collecting the reading history based on the estimated customer emotions. For example, the collection unit estimates a customer's emotions and adjusts the timing of collecting the reading history based on the estimated customer emotions. For example, if the customer is relaxed, the collection unit can immediately collect the reading history and update the data in real time. Furthermore, if the customer is feeling stressed, the collection unit can postpone collecting the reading history and collect it when the customer is calm. Furthermore, if the customer is busy, the collection unit can collect the reading history during off-peak hours, such as at night or on weekends. This allows data to be collected at a more appropriate time by adjusting the timing of collecting the reading history according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI.

[0095] The collection unit can analyze the customer's past reading history and select the optimal collection method. The collection unit, for example, analyzes the customer's past reading history and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the customer has frequently used in the past. The collection unit can also set the optimal collection timing based on the time period the customer has used in the past. Furthermore, the collection unit can also prioritize collecting related data based on the genres that the customer has preferred to read in the past. In this way, the optimal collection method can be selected by analyzing the customer's past reading history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The collection unit can filter the reading history based on the customer's current living situation and areas of interest when collecting the reading history. For example, when collecting the reading history, the collection unit filters the reading history based on the customer's current living situation and areas of interest. For example, if the customer is busy in their current living situation, the collection unit can prioritize collecting a history of books that can be read in a short time. Also, if the customer is interested in a particular area of ​​interest, the collection unit can prioritize collecting a history of books related to that area. Furthermore, if the customer has started a new hobby, the collection unit can collect a history of books related to that hobby. In this way, by filtering based on the customer's current living situation and areas of interest, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0097] When collecting a reading history, the collection unit can select the optimal collection means depending on the customer's input method. For example, when collecting a reading history, the collection unit selects the optimal collection means depending on the customer's input method (voice, text, image, etc.). For example, if the customer prefers voice input, the collection unit can collect the reading history using voice recognition technology. Also, if the customer prefers text input, the collection unit can collect the reading history using text analysis technology. Furthermore, if the customer prefers image input, the collection unit can collect the reading history using image recognition technology. In this way, by selecting the optimal collection means depending on the customer's input method, data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0098] The collection unit can estimate the customer's emotions and determine the priority of the reading histories to be collected based on the estimated customer emotions. The collection unit, for example, estimates the customer's emotions and determines the priority of the reading histories to be collected based on the estimated customer emotions. For example, if the customer is excited, the collection unit can prioritize collecting the most recent reading histories. Also, if the customer is relaxed, the collection unit can prioritize collecting past reading histories. Furthermore, if the customer is stressed, the collection unit can prioritize collecting histories of books that are useful for stress reduction. In this way, by prioritizing the reading histories based on the customer's emotions, more appropriate data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI.

[0099] When collecting reading histories, the collection unit can prioritize collecting highly relevant histories by taking into account the customer's geographical location information. For example, when collecting reading histories, the collection unit prioritizes collecting highly relevant histories by taking into account the customer's geographical location information. For example, when collecting reading histories, the collection unit prioritizes collecting histories of books related to the customer's travel destination if the customer is traveling. Furthermore, when the customer lives in a specific area, the collection unit can prioritize collecting histories of books related to that area. Furthermore, when the customer is participating in a specific event, the collection unit can prioritize collecting histories of books related to that event. In this way, highly relevant data can be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0100] The collection unit can analyze the customer's social media activities and collect related histories when collecting reading histories. For example, the collection unit analyzes the customer's social media activities and collects related histories when collecting reading histories. For example, the collection unit prioritizes collecting histories of books shared by the customer on social media. The collection unit can also prioritize collecting histories of books by authors the customer follows on social media. Furthermore, the collection unit can prioritize collecting histories of books in reading groups the customer participates in on social media. This allows for efficient collection of related data by analyzing the customer's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0101] The collection unit can customize the collection method by reflecting the customer's past feedback when collecting the reading history. For example, the collection unit customizes the collection method by reflecting the customer's past feedback when collecting the reading history. For example, the collection unit preferentially uses collection methods that the customer has previously preferred. The collection unit can also avoid collection methods that the customer has previously dissatisfied with. Furthermore, the collection unit can suggest new collection methods based on the customer's past feedback. In this way, a more appropriate collection method can be selected by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0102] The analysis unit can estimate the customer's emotions and adjust the presentation method of the analysis based on the estimated customer emotions. For example, the analysis unit can estimate the customer's emotions and adjust the presentation method of the analysis based on the estimated customer emotions. For example, the analysis unit can provide detailed analysis results when the customer is relaxed. Furthermore, the analysis unit can provide concise and to-the-point analysis results when the customer is stressed. Furthermore, the analysis unit can provide visually appealing analysis results when the customer is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a concise analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0104] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a sentiment analysis algorithm to fiction books. The analysis unit can also apply a fact-checking algorithm to non-fiction books. Furthermore, the analysis unit can apply a terminology analysis algorithm to science books. In this way, applying different analysis algorithms depending on the category of data enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0105] The analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during the analysis. For example, the analysis unit performs a similar analysis based on analysis results that the customer preferred in the past. The analysis unit can also avoid analysis results that the customer was dissatisfied with in the past. Furthermore, the analysis unit can also propose a new analysis method based on the customer's past analysis results. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0106] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, the analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, if the customer is in a hurry, the analysis unit can provide a short and to-the-point analysis. For example, if the customer is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the customer is excited, the analysis unit can provide a visually appealing analysis. By adjusting the length of the analysis based on the customer's emotions, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI.

[0107] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted recently. Furthermore, the analysis unit can also appropriately prioritize data that was submitted recently. In this way, determining the analysis priority based on the time of data submission enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0108] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can also appropriately prioritize data with medium relevance. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0109] The analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise during the analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise during the analysis. For example, the analysis unit can provide an analysis that uses a lot of technical terminology to a customer with high level of expertise. The analysis unit can also provide a concise and easy-to-understand analysis to a customer with low level of expertise. Furthermore, the analysis unit can provide an analysis that uses appropriate technical terminology to a customer with medium level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the customer's level of expertise, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0110] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. For example, the suggestion unit estimates the customer's emotions and adjusts the way the suggestion is presented based on the estimated customer emotions. For example, if the customer is relaxed, the suggestion unit can provide detailed suggestions. If the customer is stressed, the suggestion unit can provide concise and to-the-point suggestions. Furthermore, if the customer is excited, the suggestion unit can provide visually appealing suggestions. This enables more appropriate suggestions by adjusting the way the suggestion is presented based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI.

[0111] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the book when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the book when making a suggestion. For example, the suggestion unit makes detailed suggestions for books with high importance. The suggestion unit can also make brief suggestions for books with low importance. Furthermore, the suggestion unit can also make suggestions with an appropriate level of detail for books with medium importance. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the book. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0112] The suggestion unit can apply different suggestion algorithms depending on the book category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the book category when making a suggestion. For example, the suggestion unit applies a sentiment analysis algorithm to fiction books. The suggestion unit can also apply a fact-checking algorithm to non-fiction books. Furthermore, the suggestion unit can apply a terminology analysis algorithm to science books. In this way, applying different suggestion algorithms depending on the book category enables more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0113] The suggestion unit can improve the accuracy of the suggestion by referring to the customer's past suggestion results when making a suggestion. For example, the suggestion unit makes a similar suggestion based on a suggestion that the customer liked in the past. The suggestion unit can also avoid a suggestion that the customer was dissatisfied with in the past. Furthermore, the suggestion unit can also suggest a new suggestion method based on the customer's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the customer's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0114] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. The suggestion unit, for example, estimates the customer's emotions and adjusts the length of the suggestion based on the estimated customer emotions. For example, if the customer is in a hurry, the suggestion unit can make a short and to-the-point suggestion. If the customer is relaxed, the suggestion unit can also make a detailed suggestion. Furthermore, if the customer is excited, the suggestion unit can also make a visually appealing suggestion. This enables more appropriate suggestions by adjusting the length of the suggestion based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI.

[0115] The suggestion unit can determine the priority of suggestions based on the submission date of the books when making suggestions. The suggestion unit, for example, determines the priority of suggestions based on the submission date of the books when making suggestions. For example, the suggestion unit preferentially suggests the latest books. The suggestion unit can also postpone books that were submitted earlier. Furthermore, the suggestion unit can moderately prioritize books that were submitted at an intermediate date. This enables efficient suggestions by determining the priority of suggestions based on the submission date of the books. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0116] The suggestion unit can adjust the order of suggestions based on the relevance of books when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of books when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant books. The suggestion unit can also postpone suggesting books with low relevance. Furthermore, the suggestion unit can also moderately prioritize books with medium relevance. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of books. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0117] The suggestion unit can adjust the use of technical terminology in the proposal according to the customer's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the customer's level of expertise when making a proposal. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a customer with high level of expertise. The suggestion unit can also provide a concise and easy-to-understand proposal to a customer with low level of expertise. Furthermore, the suggestion unit can also provide a proposal that uses appropriate technical terminology to a customer with medium level of expertise. In this way, adjusting the use of technical terminology in the proposal according to the customer's level of expertise enables more appropriate proposals. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0118] The bulletin board unit can estimate a customer's emotions and adjust the display method of the bulletin board based on the estimated customer emotions. For example, the bulletin board unit estimates a customer's emotions and adjusts the display method of the bulletin board based on the estimated customer emotions. For example, if a customer is relaxed, the bulletin board unit can display a bulletin board containing detailed information. Also, if a customer is stressed, the bulletin board unit can display a concise and to-the-point bulletin board. Furthermore, if a customer is excited, the bulletin board unit can display a visually appealing bulletin board. This allows for providing more appropriate information by adjusting the display method of the bulletin board according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the bulletin board unit can be performed using, for example, AI, or without AI.

[0119] When displaying a message board, the message board unit can select the optimal display method by referring to the customer's past posting history. When displaying a message board, the message board unit, for example, selects the optimal display method by referring to the customer's past posting history. For example, the message board unit prioritizes using a display method that the customer has previously preferred. The message board unit can also avoid display methods that the customer has previously dissatisfied with. Furthermore, the message board unit can also suggest a new display method based on the customer's past posting history. In this way, the optimal display method can be selected by referring to the customer's past posting history. Some or all of the above-mentioned processing in the message board unit may be performed, for example, using AI, or may be performed without using AI.

[0120] The bulletin board unit can customize the display content according to the customer's current task when displaying the bulletin board. For example, when displaying the bulletin board, the bulletin board unit customizes the display content according to the customer's current task. For example, if the customer is reading, the bulletin board unit can prioritize displaying information about related books. Also, if the customer is participating in an event, the bulletin board unit can prioritize displaying information related to the event. Furthermore, if the customer is looking for a new book, the bulletin board unit can prioritize displaying information about the latest books. In this way, by customizing the display content according to the customer's current task, more appropriate information can be provided. Some or all of the above-mentioned processing in the bulletin board unit may be performed using, for example, AI, or may be performed without using AI.

[0121] The bulletin board unit can improve the display method by reflecting customer feedback when displaying the bulletin board. For example, the bulletin board unit improves the display method by reflecting customer feedback when displaying the bulletin board. For example, the bulletin board unit preferentially uses a display method that the customer has previously preferred. The bulletin board unit can also avoid a display method that the customer has previously dissatisfied with. Furthermore, the bulletin board unit can suggest a new display method based on the customer's past feedback. In this way, the display method can be improved by reflecting customer feedback, and more appropriate information can be provided. Some or all of the above-mentioned processing in the bulletin board unit may be performed, for example, using AI, or may be performed without using AI.

[0122] The bulletin board unit can estimate a customer's emotions and adjust the bulletin board operation procedures based on the estimated customer emotions. For example, the bulletin board unit can estimate a customer's emotions and adjust the bulletin board operation procedures based on the estimated customer emotions. For example, the bulletin board unit can provide detailed operation procedures when the customer is relaxed. Furthermore, the bulletin board unit can provide concise and to-the-point operation procedures when the customer is stressed. Furthermore, the bulletin board unit can provide visually appealing operation procedures when the customer is excited. This allows the bulletin board operation procedures to be adjusted according to the customer's emotions, thereby providing more appropriate operation procedures. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the bulletin board unit can be performed, for example, using AI or without AI.

[0123] The bulletin board unit can select the optimal display method by taking into consideration the customer's device information when displaying the bulletin board. For example, when displaying the bulletin board, the bulletin board unit selects the optimal display method by taking into consideration the customer's device information. For example, if the customer is using a smartphone, the bulletin board unit can provide a display method that matches the screen size. Also, if the customer is using a tablet, the bulletin board unit can provide a display method that is optimized for a large screen. Furthermore, if the customer is using a PC, the bulletin board unit can provide a display method that includes detailed information. In this way, the optimal display method can be selected by taking into consideration the customer's device information. Some or all of the above-mentioned processing in the bulletin board unit may be performed, for example, using AI, or may be performed without using AI.

[0124] The bulletin board unit can make the display content multilingual when displaying the bulletin board according to the customer's language setting. For example, the bulletin board unit can automatically set the bulletin board language based on the language setting of the customer's device. The bulletin board unit can also provide a language switching function when a customer uses multiple languages. Furthermore, if a customer selects a specific language, the bulletin board unit can display the bulletin board in that language. This makes it possible to provide more appropriate information by making the display content multilingual according to the customer's language setting. Some or all of the above-mentioned processing in the bulletin board unit may be performed, for example, using AI or without using AI.

[0125] When displaying the message board, the message board unit can prioritize displaying highly relevant information by taking into account the geographical location information of the customer. For example, when displaying the message board, the message board unit prioritizes displaying highly relevant information by taking into account the geographical location information of the customer. For example, if a customer lives in a specific area, the message board unit can prioritize displaying information related to that area. Also, if a customer is traveling, the message board unit can prioritize displaying information related to the travel destination. Furthermore, if a customer is participating in a specific event, the message board unit can prioritize displaying information related to the event. In this way, by taking into account the geographical location information of the customer, highly relevant information can be prioritized and displayed. Some or all of the above-described processing in the message board unit may be performed, for example, using AI, or may be performed without using AI.

[0126] The generation unit can estimate a customer's emotions and adjust the method for generating reviews and book reviews based on the estimated customer emotions. For example, the generation unit can estimate a customer's emotions and adjust the method for generating reviews and book reviews based on the estimated customer emotions. For example, if a customer is relaxed, the generation unit can generate a detailed review or book review. If a customer is stressed, the generation unit can also generate a concise and to-the-point review or book review. Furthermore, if a customer is excited, the generation unit can also generate a visually appealing review or book review. This allows for adjusting the method for generating reviews and book reviews according to the customer's emotions, thereby providing more appropriate reviews and book reviews. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0127] The generation unit can adjust the level of detail of the generation based on the importance of the book at the time of generation. The generation unit, for example, adjusts the level of detail of the generation based on the importance of the book at the time of generation. For example, the generation unit generates detailed reviews and critiques for books with high importance. The generation unit can also generate brief reviews and critiques for books with low importance. Furthermore, the generation unit can also generate reviews and critiques with an appropriate level of detail for books with medium importance. In this way, adjusting the level of detail of the generation based on the importance of the book enables efficient generation. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0128] The generation unit can apply different generation algorithms depending on the book category during generation. For example, the generation unit applies different generation algorithms depending on the book category during generation. For example, the generation unit applies a sentiment analysis algorithm to fiction books. The generation unit can also apply a fact-checking algorithm to non-fiction books. Furthermore, the generation unit can apply a terminology analysis algorithm to science books. In this way, applying different generation algorithms depending on the book category enables more accurate generation. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0129] The generation unit can improve the accuracy of generation by referring to the customer's past generation results during generation. The generation unit can improve the accuracy of generation by referring to the customer's past generation results during generation, for example. For example, the generation unit generates a similar result based on a generation result that the customer liked in the past. The generation unit can also avoid a generation result that the customer was dissatisfied with in the past. Furthermore, the generation unit can propose a new generation method based on the customer's past generation results. In this way, the accuracy of generation is improved by referring to the customer's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0130] The generation unit can estimate a customer's emotions and adjust the length of the generated review or book review based on the estimated customer emotions. For example, the generation unit can estimate a customer's emotions and adjust the length of the generated review or book review based on the estimated customer emotions. For example, if a customer is in a hurry, the generation unit can generate a short and to-the-point review or book review. If a customer is relaxed, the generation unit can also generate a detailed review or book review. Furthermore, if a customer is excited, the generation unit can also generate a visually appealing review or book review. This allows for adjusting the length of the review or book review according to the customer's emotions, thereby providing more appropriate reviews or book reviews. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0131] The generation unit can determine the generation priority based on the submission date of the book at the time of generation. The generation unit, for example, determines the generation priority based on the submission date of the book at the time of generation. For example, the generation unit generates the most recent books with priority. The generation unit can also postpone books that were submitted earlier. Furthermore, the generation unit can also give moderate priority to books that were submitted at a medium time. In this way, efficient generation is possible by determining the generation priority based on the submission date of the book. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0132] The generation unit can adjust the order of generation based on the relevance of the books during generation. The generation unit, for example, adjusts the order of generation based on the relevance of the books during generation. For example, the generation unit generates books with high relevance with priority. The generation unit can also postpone books with low relevance. Furthermore, the generation unit can also give moderate priority to books with medium relevance. In this way, adjusting the order of generation based on the relevance of the books enables efficient generation. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0133] The generation unit can adjust the use of technical terminology in the generation according to the customer's level of expertise at the time of generation. For example, the generation unit adjusts the use of technical terminology in the generation according to the customer's level of expertise at the time of generation. For example, the generation unit generates reviews and book reviews that use a lot of technical terminology for customers with high levels of expertise. The generation unit can also generate concise and easy-to-understand reviews and book reviews for customers with low levels of expertise. Furthermore, the generation unit can generate reviews and book reviews that use appropriate technical terminology for customers with medium levels of expertise. In this way, by adjusting the use of technical terminology in the generation according to the customer's level of expertise, more appropriate reviews and book reviews can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0134] The providing unit can estimate the customer's emotions and adjust the method of providing information based on the estimated customer emotions. For example, the providing unit can estimate the customer's emotions and adjust the method of providing information based on the estimated customer emotions. For example, the providing unit can provide detailed information when the customer is relaxed. Furthermore, the providing unit can provide concise and to-the-point information when the customer is stressed. Furthermore, the providing unit can provide visually appealing information when the customer is excited. This allows for adjusting the method of providing information according to the customer's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0135] The providing unit can select the optimal delivery method by referring to the customer's past usage history when providing information. For example, the providing unit selects the optimal delivery method by referring to the customer's past usage history when providing information. For example, the providing unit preferentially uses a delivery method that the customer has preferred in the past. The providing unit can also avoid delivery methods that the customer has been dissatisfied with in the past. Furthermore, the providing unit can also suggest a new delivery method based on the customer's past usage history. In this way, the optimal delivery method can be selected by referring to the customer's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0136] The providing unit can customize the provided content according to the customer's current needs when providing information. The providing unit customizes the provided content according to the customer's current needs when providing information, for example. For example, if a customer is looking for a new book, the providing unit can provide the latest book information. Also, if a customer is interested in a particular genre, the providing unit can provide information related to that genre. Furthermore, if a customer has a particular problem, the providing unit can provide information that is useful for that problem. In this way, by customizing the provided content according to the customer's current needs, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0137] The providing unit can improve the method of providing information by reflecting customer feedback when providing information. For example, the providing unit improves the method of providing information by reflecting customer feedback when providing information. For example, the providing unit preferentially uses a method of providing information that the customer has previously preferred. The providing unit can also avoid a method of providing information that the customer has previously dissatisfied with. Furthermore, the providing unit can propose a new method of providing information based on the customer's past feedback. In this way, the method of providing information can be improved by reflecting customer feedback, and more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0138] The providing unit can estimate the customer's emotions and determine the priority of information provision based on the estimated customer emotions. The providing unit, for example, estimates the customer's emotions and determines the priority of information provision based on the estimated customer emotions. For example, if the customer is excited, the providing unit can prioritize providing the latest information. Furthermore, if the customer is relaxed, the providing unit can prioritize providing older information. Furthermore, if the customer is stressed, the providing unit can prioritize providing information that is useful for stress reduction. In this way, by determining the priority of information provision based on the customer's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0139] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the geographical location information of the customer. For example, when providing information, the providing unit prioritizes providing highly relevant information by taking into account the geographical location information of the customer. For example, if the customer lives in a specific area, the providing unit can prioritize providing information related to that area. Also, if the customer is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, if the customer is participating in a specific event, the providing unit can prioritize providing information related to the event. In this way, by taking into account the geographical location information of the customer, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0140] The providing unit can analyze the customer's social media activity and provide related information when providing information. For example, the providing unit can analyze the customer's social media activity and provide related information when providing information. For example, the providing unit can provide information about books that the customer has shared on social media. The providing unit can also provide information about authors that the customer follows on social media. Furthermore, the providing unit can provide information about reading groups that the customer has joined on social media. This makes it possible to efficiently provide related information by analyzing the customer's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0141] The providing unit can customize the delivery method by reflecting the customer's past feedback when providing information. For example, the providing unit customizes the delivery method by reflecting the customer's past feedback when providing information. For example, the providing unit preferentially uses a delivery method that the customer has previously preferred. The providing unit can also avoid delivery methods that the customer has previously been dissatisfied with. Furthermore, the providing unit can propose a new delivery method based on the customer's past feedback. In this way, a more appropriate delivery method can be selected by reflecting the customer's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, bulletin board unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect a customer's reading history using the camera 42 or microphone 38B of the smart device 14 and transmit the collected history to the data processing device 12 via the control unit 46A. The analysis unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the customer's preferences and interests. The suggestion unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and suggests books based on the analysis results. The bulletin board unit, for example, is implemented by the control unit 46A of the smart device 14 and provides a bulletin board where customers who have read the same book can share their impressions. The generation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate reviews and critiques. The provision unit, for example, is implemented by the control unit 46A of the smart device 14 and provides the generated information via a website or application. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, bulletin board unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect a customer's reading history using the camera 42 and microphone 238 of the smart glasses 214 and transmit the collected history to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the customer's preferences and interests. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests books based on the analysis results. The bulletin board unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides a bulletin board where customers who have read the same book can share their impressions. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate reviews and critiques. The provision unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides the generated information via a website or application. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, bulletin board unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect a customer's reading history using the camera 42 or microphone 238 of the headset terminal 314 and transmit the collected history to the data processing device 12 via the control unit 46A. The analysis unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the customer's preferences and interests. The suggestion unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and suggests books based on the analysis results. The bulletin board unit, for example, is implemented by the control unit 46A of the headset terminal 314 and provides a bulletin board where customers who have read the same book can share their impressions. The generation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate reviews and critiques. The provision unit, for example, is implemented by the control unit 46A of the headset terminal 314 and provides the generated information via a website or application. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, bulletin board unit, generation unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect customer reading history using the camera 42 and microphone 238 of the robot 414 and transmit the collected data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the customer's preferences and interests. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests books based on the analysis results. The bulletin board unit, for example, is realized by the control unit 46A of the robot 414 and provides a bulletin board where customers who have read the same book can share their impressions. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate reviews and critiques. The provision unit, for example, is realized by the control unit 46A of the robot 414 and provides the generated information via a website or application.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] The analysis unit can also estimate the customer's emotions and determine the priority of analysis based on the estimated customer emotions. For example, if the customer is excited, the latest data is analyzed first, and if the customer is relaxed, older data is analyzed first. Also, if the customer is feeling stressed, data that is useful for reducing stress can be analyzed first. In this way, by determining the priority of analysis based on the customer's emotions, more appropriate analysis results can be provided.

[0144] The collection unit can also analyze the customer's social media activity and collect related reading history. For example, it can prioritize collection of the history of books that the customer has shared on social media. It can also collect the history of books by authors that the customer follows on social media. It can also collect the history of books in reading groups that the customer has joined on social media. This allows for efficient collection of related data by analyzing the customer's social media activity.

[0145] The suggestion unit can also estimate the customer's emotions and adjust the timing of the suggestion based on the estimated customer emotions. For example, if the customer is relaxed, the suggestion can be made immediately, and if the customer is stressed, the suggestion can be postponed. Also, if the customer is busy, the suggestion can be made at a time when the customer is less busy, such as at night or on weekends. In this way, by adjusting the timing of the suggestion according to the customer's emotions, the suggestion can be made at a more appropriate time.

[0146] The bulletin board unit can also prioritize displaying highly relevant information by taking into account the geographical location information of the customer. For example, if a customer lives in a specific area, information related to that area can be prioritized. Also, if a customer is traveling, information related to the travel destination can be prioritized. Furthermore, if a customer is participating in a specific event, information related to that event can be prioritized. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the customer.

[0147] The generation unit can also estimate the customer's emotions and adjust the tone of the reviews and book reviews based on the estimated customer emotions. For example, if the customer is relaxed, the generation unit can generate reviews and book reviews in a calm tone. If the customer is excited, the generation unit can generate reviews and book reviews in an energetic tone. Furthermore, if the customer is stressed, the generation unit can generate reviews and book reviews in a calm tone. This makes it possible to provide more appropriate content by adjusting the tone of the reviews and book reviews according to the customer's emotions.

[0148] The provision unit can also customize the information provision method by reflecting the customer's past feedback. For example, it can prioritize the use of a provision method that the customer has previously preferred. It can also avoid a provision method that the customer has previously been dissatisfied with. Furthermore, it can also propose a new provision method based on the customer's past feedback. In this way, it is possible to select a more appropriate provision method by reflecting the customer's past feedback.

[0149] The analysis unit can also adjust the use of technical terms in the analysis according to the customer's level of expertise. For example, it can provide an analysis that uses a lot of technical terms to a customer with a high level of expertise. It can also provide a concise and easy-to-understand analysis to a customer with a low level of expertise. It can also provide an analysis that uses appropriate technical terms to a customer with a medium level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the customer's level of expertise, it is possible to provide more appropriate analysis results.

[0150] The collection unit can also estimate the customer's emotions and adjust the type of data to be collected based on the estimated customer emotions. For example, if the customer is relaxed, detailed data can be collected. If the customer is stressed, concise data can be collected. Furthermore, if the customer is excited, visually appealing data can be collected. In this way, by adjusting the type of data to be collected according to the customer's emotions, more appropriate data can be collected.

[0151] The proposal unit can also improve the accuracy of proposals by referring to the customer's past proposal results. For example, it can make similar proposals based on proposals that the customer liked in the past. It can also avoid proposals that the customer was dissatisfied with in the past. Furthermore, it can also propose new proposal methods based on the customer's past proposal results. In this way, the accuracy of proposals can be improved by referring to the customer's past proposal results.

[0152] The message board unit can also estimate the customer's emotions and adjust the message board display content based on the estimated customer emotions. For example, if the customer is relaxed, a message board containing detailed information can be displayed. If the customer is stressed, a message board that is concise and to the point can be displayed. Furthermore, if the customer is excited, a visually appealing message board can be displayed. In this way, by adjusting the message board display content according to the customer's emotions, more appropriate information can be provided.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The collection unit collects the customer's reading history. The customer's reading history includes, but is not limited to, the titles of books read, the date of completion, and the reading time. The collection unit may also collect the customer's past reading history and purchase history. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the customer's preferences and interests. The analysis is performed using, for example, data mining techniques or machine learning algorithms. Step 3: The suggestion unit suggests books based on the analysis results obtained by the analysis unit. The suggestions are made using, for example, a recommendation algorithm based on the customer's preferences and interests. Step 4: The message board section provides a message board where customers who have read the same book can share their impressions with each other. Impressions can be shared in text, image, audio, or other formats, for example. Step 5: The collection department collects information such as the book's synopsis, author information, book reviews, recommendations for similar books, and links to sites selling the e-book version of the book. Step 6: The generator analyzes the information collected by the collector and generates reviews and critiques. Generation is performed using, for example, natural language generation technology. Step 7: The providing unit provides the information generated by the generating unit. The provision is performed, for example, through a website, an application, or email.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0157] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0176] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0183] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0184] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0185] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0186] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0187] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 7, a 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.

[0193] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0194] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0195] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0197] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0198] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0199] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0200] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0202] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0203] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0204] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0205] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0206] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0209] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0210] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0211] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0212] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0213] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0214] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0215] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0216] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0217] 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.

[0218] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0219] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0220] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0221] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0222] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0223] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0224] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0225] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0226] [Explanation of symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects customer reading histories; an analysis unit that analyzes the data collected by the collection unit to understand customer preferences and interests; a suggestion unit that suggests books based on the analysis results obtained by the analysis unit; A message board section that provides a message board where customers who have read the same book can share their impressions with each other, a collection department that collects book information; a generating unit that analyzes the information collected by the collecting unit and generates reviews and critiques; a providing unit that provides the information generated by the generating unit; Equipped with A system characterized by:

2. The collecting unit Collecting customer reading and purchasing history 2. The system of claim 1.

3. The analysis unit Analyzing collected data to understand customer preferences and interests 2. The system of claim 1.

4. The proposal unit Suggest books based on analysis results 2. The system of claim 1.

5. The bulletin board section includes: Provide a bulletin board where people who have read the same book can share their impressions 2. The system of claim 1.

6. The collecting unit Collect information such as book synopsis, author information, book reviews, recommendations for similar books, and links to websites selling the e-book version of the book.

2. The system of claim 1.

7. The generation unit Analyze the collected information and generate reviews and critiques 2. The system of claim 1.

8. The providing unit Providing generated information 2. The system of claim 1.

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