system

Abstract

An object of a system according to an embodiment is to efficiently select and provide a book that suits a user's reading preference or taste.SOLUTION: A system includes a reading history collection unit, an SNS analysis unit, a question list analysis unit, a main selection unit, and a delivery unit. The reading history collection unit collects a reading history of a user. The SNS analysis unit analyzes the reading history collected by the reading history collection unit. The question list analysis unit analyzes the answer to the question list based on the likes and reviews in the SNS analyzed by the SNS analysis unit. The book selection unit selects a book based on the data analyzed by the question list analysis unit. The delivery unit delivers the book selected by the book selection unit to the user.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 techniques have had the problem of making it difficult to efficiently select and provide books that match a user's reading preferences and tastes.

[0005] The system according to the embodiment aims to efficiently select and provide books that match the reading preferences and tastes of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading history collection unit, an SNS analysis unit, a question list analysis unit, a book selection unit, and a delivery unit. The reading history collection unit collects the user's reading history. The SNS analysis unit analyzes the reading history collected by the reading history collection unit. The question list analysis unit analyzes answers to the question list based on likes and reviews on SNS analyzed by the SNS analysis unit. The book selection unit selects a book based on the data analyzed by the question list analysis unit. The delivery unit delivers the book selected by the book selection unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select and provide books that match the reading preferences and tastes of the user. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A personalized book subscription service according to an embodiment of the present invention is a system that selects the most suitable books based on a user's reading preferences and tastes and delivers them to the user every month. This allows the personalized book subscription service to find books that perfectly match the user's reading preferences and tastes and deliver them to the user every month.

[0029] A personalized book subscription service according to an embodiment includes a reading history collection unit, a social media analysis unit, a question list analysis unit, a book selection unit, and a delivery unit. The reading history collection unit collects a user's reading history. For example, it collects information such as the title, author, genre, and rating of books read by the user. The reading history collection unit can also collect information such as e-book history, paper book history, and reading time. The social media analysis unit analyzes the reading history collected by the reading history collection unit. For example, the social media analysis unit analyzes information about books that the user has liked or reviewed on social media. The social media analysis unit can also analyze information such as Facebook likes, Twitter retweets, and Amazon reviews. The question list analysis unit analyzes answers to questions in the question list based on the social media likes and reviews analyzed by the social media analysis unit. For example, the question list analysis unit analyzes answers to questions such as "What is your favorite genre?" and "What is the most interesting book you've read recently?" The question list analysis unit can also analyze answers collected through online surveys or in-app question functions. The book selection unit selects books based on the data analyzed by the question list analysis unit. For example, the book selection unit comprehensively analyzes the user's reading history, likes on social media, and answers to the question list to select books that the user is likely to be interested in. The book selection unit can also select books based on criteria such as genre, author, and publication year. The delivery unit delivers the books selected by the book selection unit to the user. For example, the delivery unit delivers the books using a courier service. The delivery unit can also deliver the books using methods such as e-book downloads or regional delivery. As a result, the personalized book subscription service according to the embodiment can comprehensively analyze the user's reading history, likes and reviews on social media, and answers to the question list to select and deliver the most suitable books.

[0030] The reading history collection unit can analyze the time period and frequency of reading based on the reading history and suggest the optimal reading time. The reading history collection unit, for example, analyzes the time period and frequency at which the user reads and suggests the optimal reading time. For example, if the user often reads at night, the reading history collection unit can suggest a reading schedule that suits that time period. Furthermore, the reading history collection unit can analyze information such as the time of day (morning, noon, night) and how many times a week the user reads based on the user's reading history. This can improve reading efficiency by suggesting the optimal reading time to the user.

[0031] The reading history collection unit can estimate the user's reading speed from the reading history and suggest an appropriate reading pace. The reading history collection unit, for example, analyzes the user's reading history to estimate the reading speed. For example, it calculates the average time it takes the user to read one book and suggests an appropriate reading pace based on that speed. The reading history collection unit can also analyze information such as the number of characters and pages that can be read per minute based on the user's reading history. This can improve reading efficiency by suggesting an appropriate reading pace to the user.

[0032] The reading history collection unit can analyze the relevance with other media based on the reading history and suggest related content. For example, the reading history collection unit analyzes the relevance with other media, such as movies, music, and games, based on the user's reading history. For example, it can suggest content with the same theme or storyline. The reading history collection unit can also clarify the specific types and analysis methods of other media and analyze information on movies, music, games, etc. This can enhance the reading experience by suggesting content from other media related to the user.

[0033] The reading history collection unit can suggest reading events and circles that the user may be interested in based on the reading history. The reading history collection unit, for example, suggests reading events and circles that the user may be interested in based on the user's reading history. For example, it suggests events related to a specific genre or author. The reading history collection unit can also clarify the specific types and suggestion methods of reading events and circles, and analyze information on online book clubs, local book clubs, etc. This can enhance the reading experience by suggesting reading events and circles that the user may be interested in.

[0034] The SNS analysis unit can analyze the time periods of likes and reviews and identify the time periods when a user is most active. For example, the SNS analysis unit can analyze the time periods of likes and reviews on a user's SNS and identify the most active time periods. For example, if a user leaves many likes and reviews at night, that time period can be identified. The SNS analysis unit can also clarify the specific definition and analysis method of active time periods, and analyze information such as the amount of time spent on SNS and the frequency of posts. This allows the unit to identify the time periods when a user is most active, making it possible to recommend books at the optimal time.

[0035] The SNS analysis unit can analyze the content of likes and reviews to identify themes and topics in which the user is particularly interested. For example, the SNS analysis unit can analyze the content of likes and reviews on a user's SNS to identify themes and topics in which the user is particularly interested. For example, it can analyze likes and reviews for a particular genre or author. The SNS analysis unit can also clarify specific methods and criteria for identifying themes and topics, and analyze information such as the frequency of keyword appearances and co-occurrence network analysis. This allows for more personalized book suggestions by identifying themes and topics in which the user is particularly interested.

[0036] The SNS analysis unit can identify authors and influencers that a user follows based on likes and reviews, and suggest related books. The SNS analysis unit, for example, analyzes likes and reviews on a user's SNS to identify the authors and influencers they follow. For example, it analyzes likes and reviews for a specific author or influencer. The SNS analysis unit can also clarify the specific methods and criteria for identifying authors and influencers, and analyze information such as the number of followers and ratings of influence. This allows the system to identify authors and influencers that a user follows and suggest related books, enabling more personalized book suggestions.

[0037] The SNS analysis unit can identify online communities in which a user participates based on likes and reviews and suggest related books. The SNS analysis unit, for example, analyzes likes and reviews on the user's SNS to identify the online communities in which the user participates. For example, it analyzes likes and reviews related to a specific community. The SNS analysis unit can also clarify specific methods and criteria for identifying online communities and analyze information such as the activities of forums and groups in which the user participates. This allows the unit to identify online communities in which the user participates and suggest related books, making it possible to suggest more personalized books.

[0038] The question list analysis unit can perform a detailed analysis of the user's reading habits and environment based on the answers to the question list and suggest an optimal reading environment. The question list analysis unit can perform a detailed analysis of the user's reading habits and environment based on the answers to the question list. For example, it can analyze the place and time of reading, the device used, etc. The question list analysis unit can also clarify specific analysis methods and criteria for reading habits and environment and analyze information such as reading frequency, reading place, and reading time. This allows for a detailed analysis of the user's reading habits and environment and suggests an optimal reading environment, thereby improving the reading experience.

[0039] The question list analysis unit can identify the user's reading purpose based on the answers to the question list and suggest books that match the purpose. The question list analysis unit can identify the user's reading purpose based on, for example, the answers to the question list. For example, if the purpose is learning, it can suggest reference books or specialized books. The question list analysis unit can also clarify the specific method and criteria for identifying the reading purpose and analyze information such as learning, entertainment, and self-improvement. This makes it possible to identify the user's reading purpose and suggest books that match the purpose, thereby providing a more personalized reading experience.

[0040] The question list analysis unit can identify other hobbies and activities that the user may be interested in based on the answers to the question list and suggest related books. The question list analysis unit can, for example, identify other hobbies and activities that the user may be interested in based on the answers to the question list. For example, it can identify hobbies such as sports, music, and travel and suggest related books. The question list analysis unit can also clarify specific identification methods and criteria for hobbies and activities and analyze information on sports, art, music, etc. This can enhance the reading experience by identifying other hobbies and activities that the user may be interested in and suggesting related books.

[0041] The question list analysis unit can identify travel destinations and tourist spots that the user is likely to be interested in based on the answers to the question list and suggest related books. The question list analysis unit can, for example, identify travel destinations and tourist spots that the user is likely to be interested in based on the answers to the question list. For example, it can suggest books related to a specific country or city. The question list analysis unit can also clarify specific methods and criteria for identifying travel destinations and tourist spots, and analyze information such as popular tourist spots and travel destinations. This can enhance the reading experience by identifying travel destinations and tourist spots that the user is likely to be interested in and suggesting related books.

[0042] The book selection unit can analyze the relevance to previously read books based on reading history, likes, and answers to a question list, and suggest related books. The book selection unit analyzes the relevance to previously read books based, for example, on the user's reading history, likes on social media, and answers to a question list. For example, it can suggest books by the same author or genre. The book selection unit can also clarify the specific analysis method and criteria for relevance, and analyze information such as commonalities in themes and similarities between characters. This enables more personalized book suggestions by suggesting related books based on the user's past reading history, likes on social media, and answers to a question list.

[0043] The book selection unit can identify and suggest new releases and bestsellers that are likely to interest the user based on their reading history, likes, and answers to a question list. The book selection unit can identify new releases and bestsellers that are likely to interest the user based on, for example, the user's reading history, likes on social media, and answers to a question list. For example, it can suggest new releases in a specific genre or by a specific author. The book selection unit can also clarify the specific methods and criteria for identifying new releases and bestsellers, and analyze information such as publication date and sales ranking. This allows the unit to identify and suggest new releases and bestsellers that are likely to interest the user, enabling more personalized book suggestions.

[0044] The book selection unit can analyze the relevance with other media based on the user's reading history, likes, and answers to the question list, and suggest related books. The book selection unit analyzes the relevance with other media based, for example, on the user's reading history, likes on social media, and answers to the question list. For example, it can suggest movies or music with the same theme or storyline. The book selection unit can also clarify the specific types of other media and analysis methods, and analyze information on movies, music, games, etc. This can enhance the reading experience by analyzing the relevance with other media based on the user's reading history, likes on social media, and answers to the question list, and suggesting related books.

[0045] The book selection unit can suggest reading events and circles that may be of interest to the user based on their reading history, likes, and answers to the question list. The book selection unit can suggest reading events and circles that may be of interest to the user based on, for example, their reading history, likes on social media, and answers to the question list. For example, it can suggest events related to a specific genre or author. The book selection unit can also clarify the specific types of reading events and circles and how to suggest them, and analyze information on online book clubs and local book clubs, etc. This can enhance the reading experience by suggesting reading events and circles that may be of interest to the user based on their reading history, likes on social media, and answers to the question list.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reading history collection unit can analyze the user's reading posture based on the user's reading history. For example, if the user often reads while sitting, the unit can suggest a reading environment suited to that posture. Also, if the user often reads while standing, the unit can suggest a reading stand or lighting that is suitable for that posture. This can improve the reading experience by suggesting the user's optimal reading posture.

[0048] The SNS analysis unit can analyze the reading-related posts shared by the user on SNS and identify the reading community to which the user belongs. For example, if the user belongs to a community related to a particular genre or author, the SNS analysis unit can suggest books related to that community. The SNS analysis unit can also analyze the activities of online forums and groups in which the user participates and suggest related books. This makes it possible to suggest books based on the reading community to which the user belongs.

[0049] The question list analysis unit can analyze the user's motivation for reading based on the answers to the question list and suggest books that match that motivation. For example, for a user who prefers reading for the purpose of self-improvement or skill development, related self-improvement books or specialized books can be suggested. Also, for a user who prefers reading for entertainment or relaxation, fiction or highly entertaining books can be suggested. This makes it possible to suggest books that match the user's reading motivation.

[0050] The book selection unit can identify books that made a particularly strong impression on the user among those they have read in the past based on the user's reading history, likes on social media, and answers to questions in the question list, and suggest books related to those books. For example, it can suggest books related to a specific author or series. The book selection unit can also suggest books in themes or genres related to books that the user has given particularly high ratings to. This makes it possible to suggest books based on the user's past reading experiences.

[0051] The delivery unit can suggest the most convenient delivery method for the user based on the user's reading history, likes on social media, and answers to the question list. For example, if the user wants to receive the book at home, the delivery unit can suggest a delivery schedule tailored to that address. Also, if the user wants to receive the book at their workplace or a designated pick-up location, the delivery unit can suggest a delivery method tailored to that location. This can improve the reading experience by suggesting the optimal delivery method for the user.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reading history collection unit collects the user's reading history. For example, it collects information such as the title, author, genre, and rating of the books the user has read. It can also collect information such as e-book history, paper book history, and reading time. Step 2: The SNS analysis unit analyzes the reading history collected by the reading history collection unit. For example, it analyzes information about books that users have liked or reviewed on SNS. It can also analyze information such as Facebook likes, Twitter retweets, and Amazon reviews. Step 3: The Question List Analysis Unit analyzes the answers to the question list based on the likes and reviews on social media analyzed by the SNS Analysis Unit. For example, it analyzes answers to questions such as "What is your favorite genre?" or "What is the most interesting book you have read recently?" It can also analyze answers collected through online surveys or in-app question functions. Step 4: The book selection unit selects books based on the data analyzed by the question list analysis unit. For example, it comprehensively analyzes the user's reading history, likes on social media, and answers to the question list to select books that the user is likely to be interested in. It can also select books based on criteria such as genre, author, and publication year. Step 5: The delivery unit delivers the books selected by the book selection unit to the user. For example, the books may be delivered by courier. Alternatively, the books may be delivered by electronic book download or by regional delivery.

[0054] (Example 2) A personalized book subscription service according to an embodiment of the present invention is a system that selects the most suitable books based on a user's reading preferences and tastes and delivers them to the user every month. This allows the personalized book subscription service to find books that perfectly match the user's reading preferences and tastes and deliver them to the user every month.

[0055] A personalized book subscription service according to an embodiment includes a reading history collection unit, a social media analysis unit, a question list analysis unit, a book selection unit, and a delivery unit. The reading history collection unit collects a user's reading history. For example, it collects information such as the title, author, genre, and rating of books read by the user. The reading history collection unit can also collect information such as e-book history, paper book history, and reading time. The social media analysis unit analyzes the reading history collected by the reading history collection unit. For example, the social media analysis unit analyzes information about books that the user has liked or reviewed on social media. The social media analysis unit can also analyze information such as Facebook likes, Twitter retweets, and Amazon reviews. The question list analysis unit analyzes answers to questions in the question list based on the social media likes and reviews analyzed by the social media analysis unit. For example, the question list analysis unit analyzes answers to questions such as "What is your favorite genre?" and "What is the most interesting book you've read recently?" The question list analysis unit can also analyze answers collected through online surveys or in-app question functions. The book selection unit selects books based on the data analyzed by the question list analysis unit. For example, the book selection unit comprehensively analyzes the user's reading history, likes on social media, and answers to the question list to select books that the user is likely to be interested in. The book selection unit can also select books based on criteria such as genre, author, and publication year. The delivery unit delivers the books selected by the book selection unit to the user. For example, the delivery unit delivers the books using a courier service. The delivery unit can also deliver the books using methods such as e-book downloads or regional delivery. As a result, the personalized book subscription service according to the embodiment can comprehensively analyze the user's reading history, likes and reviews on social media, and answers to the question list to select and deliver the most suitable books.

[0056] The reading history collection unit can analyze the time period and frequency of reading based on the reading history and suggest the optimal reading time. The reading history collection unit, for example, analyzes the time period and frequency at which the user reads and suggests the optimal reading time. For example, if the user often reads at night, the reading history collection unit can suggest a reading schedule that suits that time period. Furthermore, the reading history collection unit can analyze information such as the time of day (morning, noon, night) and how many times a week the user reads based on the user's reading history. This can improve reading efficiency by suggesting the optimal reading time to the user.

[0057] The reading history collection unit can estimate the user's reading speed from the reading history and suggest an appropriate reading pace. The reading history collection unit, for example, analyzes the user's reading history to estimate the reading speed. For example, it calculates the average time it takes the user to read one book and suggests an appropriate reading pace based on that speed. The reading history collection unit can also analyze information such as the number of characters and pages that can be read per minute based on the user's reading history. This can improve reading efficiency by suggesting an appropriate reading pace to the user.

[0058] The reading history collection unit can use the emotion estimation function to identify the book that gave the user the most emotional satisfaction from the reading history and analyze the trend. The reading history collection unit, for example, analyzes the user's reading history and uses the emotion estimation function to identify the book that gave the user the most emotional satisfaction. For example, it calculates an emotion score based on reviews and ratings after reading. The reading history collection unit can also use the emotion estimation function to analyze emotions using technologies such as text analysis, facial expression recognition, and voice analysis. This allows the unit to identify the book that gave the user the most emotional satisfaction and analyze the trend, making it possible to suggest more personalized books.

[0059] The reading history collection unit can analyze the relevance with other media based on the reading history and suggest related content. For example, the reading history collection unit analyzes the relevance with other media, such as movies, music, and games, based on the user's reading history. For example, it can suggest content with the same theme or storyline. The reading history collection unit can also clarify the specific types and analysis methods of other media and analyze information on movies, music, games, etc. This can enhance the reading experience by suggesting content from other media related to the user.

[0060] The reading history collection unit can suggest reading events and circles that the user may be interested in based on the reading history. The reading history collection unit, for example, suggests reading events and circles that the user may be interested in based on the user's reading history. For example, it suggests events related to a specific genre or author. The reading history collection unit can also clarify the specific types and suggestion methods of reading events and circles, and analyze information on online book clubs, local book clubs, etc. This can enhance the reading experience by suggesting reading events and circles that the user may be interested in.

[0061] The reading history collection unit can use the emotion estimation function to analyze changes in the user's emotions from the reading history and suggest books that correspond to those emotions. The reading history collection unit, for example, analyzes the user's reading history and identifies changes in emotions using the emotion estimation function. For example, it calculates an emotion score based on reviews and ratings after reading. The reading history collection unit can also use the emotion estimation function to clarify specific analysis methods and criteria for changes in emotions and analyze information such as emotion scores before and after reading and emotion analysis of reviews. This allows the user's changes in emotions to be analyzed and books suggested according to those emotions, providing a more personalized reading experience.

[0062] The SNS analysis unit can analyze the time periods of likes and reviews and identify the time periods when a user is most active. For example, the SNS analysis unit can analyze the time periods of likes and reviews on a user's SNS and identify the most active time periods. For example, if a user leaves many likes and reviews at night, that time period can be identified. The SNS analysis unit can also clarify the specific definition and analysis method of active time periods, and analyze information such as the amount of time spent on SNS and the frequency of posts. This allows the unit to identify the time periods when a user is most active, making it possible to recommend books at the optimal time.

[0063] The SNS analysis unit can analyze the content of likes and reviews to identify themes and topics in which the user is particularly interested. For example, the SNS analysis unit can analyze the content of likes and reviews on a user's SNS to identify themes and topics in which the user is particularly interested. For example, it can analyze likes and reviews for a particular genre or author. The SNS analysis unit can also clarify specific methods and criteria for identifying themes and topics, and analyze information such as the frequency of keyword appearances and co-occurrence network analysis. This allows for more personalized book suggestions by identifying themes and topics in which the user is particularly interested.

[0064] The SNS analysis unit can use the emotion estimation function to analyze a user's emotions from reviews and suggest books based on those emotions. For example, the SNS analysis unit analyzes a user's SNS reviews and identifies their emotions using the emotion estimation function. For example, it calculates an emotion score from the content of the review and suggests books based on those emotions. The SNS analysis unit can also clarify specific emotion analysis methods and criteria and analyze information such as emotion classifications as positive, negative, and neutral. This allows the SNS analysis unit to provide a more personalized reading experience by analyzing a user's emotions and suggesting books based on those emotions.

[0065] The SNS analysis unit can identify authors and influencers that a user follows based on likes and reviews, and suggest related books. The SNS analysis unit, for example, analyzes likes and reviews on a user's SNS to identify the authors and influencers they follow. For example, it analyzes likes and reviews for a specific author or influencer. The SNS analysis unit can also clarify the specific methods and criteria for identifying authors and influencers, and analyze information such as the number of followers and ratings of influence. This allows the system to identify authors and influencers that a user follows and suggest related books, enabling more personalized book suggestions.

[0066] The SNS analysis unit can identify online communities in which a user participates based on likes and reviews and suggest related books. The SNS analysis unit, for example, analyzes likes and reviews on the user's SNS to identify the online communities in which the user participates. For example, it analyzes likes and reviews related to a specific community. The SNS analysis unit can also clarify specific methods and criteria for identifying online communities and analyze information such as the activities of forums and groups in which the user participates. This allows the unit to identify online communities in which the user participates and suggest related books, making it possible to suggest more personalized books.

[0067] The question list analysis unit can perform a detailed analysis of the user's reading habits and environment based on the answers to the question list and suggest an optimal reading environment. The question list analysis unit can perform a detailed analysis of the user's reading habits and environment based on the answers to the question list. For example, it can analyze the place and time of reading, the device used, etc. The question list analysis unit can also clarify specific analysis methods and criteria for reading habits and environment and analyze information such as reading frequency, reading place, and reading time. This allows for a detailed analysis of the user's reading habits and environment and suggests an optimal reading environment, thereby improving the reading experience.

[0068] The question list analysis unit can identify the user's reading purpose based on the answers to the question list and suggest books that match the purpose. The question list analysis unit can identify the user's reading purpose based on, for example, the answers to the question list. For example, if the purpose is learning, it can suggest reference books or specialized books. The question list analysis unit can also clarify the specific method and criteria for identifying the reading purpose and analyze information such as learning, entertainment, and self-improvement. This makes it possible to identify the user's reading purpose and suggest books that match the purpose, thereby providing a more personalized reading experience.

[0069] The question list analysis unit can use the emotion estimation function to analyze the user's emotions from the answers to the question list and suggest books based on the emotions. The question list analysis unit, for example, analyzes the answers to the question list and identifies the user's emotions using the emotion estimation function. For example, it calculates an emotion score from the content of the answers and suggests books based on the emotions. The question list analysis unit can also clarify specific emotion analysis methods and criteria and analyze information such as emotion classifications as positive, negative, and neutral. This makes it possible to provide a more personalized reading experience by analyzing the user's emotions and suggesting books based on the emotions.

[0070] The question list analysis unit can identify other hobbies and activities that the user may be interested in based on the answers to the question list and suggest related books. The question list analysis unit can, for example, identify other hobbies and activities that the user may be interested in based on the answers to the question list. For example, it can identify hobbies such as sports, music, and travel and suggest related books. The question list analysis unit can also clarify specific identification methods and criteria for hobbies and activities and analyze information on sports, art, music, etc. This can enhance the reading experience by identifying other hobbies and activities that the user may be interested in and suggesting related books.

[0071] The question list analysis unit can identify travel destinations and tourist spots that the user is likely to be interested in based on the answers to the question list and suggest related books. The question list analysis unit can, for example, identify travel destinations and tourist spots that the user is likely to be interested in based on the answers to the question list. For example, it can suggest books related to a specific country or city. The question list analysis unit can also clarify specific methods and criteria for identifying travel destinations and tourist spots, and analyze information such as popular tourist spots and travel destinations. This can enhance the reading experience by identifying travel destinations and tourist spots that the user is likely to be interested in and suggesting related books.

[0072] The question list analysis unit can use the emotion estimation function to analyze changes in the user's emotions from the answers to the question list and suggest books that correspond to the emotions. The question list analysis unit, for example, analyzes the answers to the question list and identifies changes in emotions using the emotion estimation function. For example, it calculates an emotion score from the content of the answers and suggests books that correspond to the emotions. The question list analysis unit can also clarify specific analysis methods and criteria for changes in emotions and analyze information such as the emotion scores of the answers to the question list and the results of the emotion estimation function. This makes it possible to provide a more personalized reading experience by analyzing changes in the user's emotions and suggesting books that correspond to the emotions.

[0073] The book selection unit can analyze the relevance to previously read books based on reading history, likes, and answers to a question list, and suggest related books. The book selection unit analyzes the relevance to previously read books based, for example, on the user's reading history, likes on social media, and answers to a question list. For example, it can suggest books by the same author or genre. The book selection unit can also clarify the specific analysis method and criteria for relevance, and analyze information such as commonalities in themes and similarities between characters. This enables more personalized book suggestions by suggesting related books based on the user's past reading history, likes on social media, and answers to a question list.

[0074] The book selection unit can identify and suggest new releases and bestsellers that are likely to interest the user based on their reading history, likes, and answers to a question list. The book selection unit can identify new releases and bestsellers that are likely to interest the user based on, for example, the user's reading history, likes on social media, and answers to a question list. For example, it can suggest new releases in a specific genre or by a specific author. The book selection unit can also clarify the specific methods and criteria for identifying new releases and bestsellers, and analyze information such as publication date and sales ranking. This allows the unit to identify and suggest new releases and bestsellers that are likely to interest the user, enabling more personalized book suggestions.

[0075] The selection unit can use the emotion estimation function to select and deliver books based on the user's emotions. For example, the selection unit uses the emotion estimation function to select books based on the user's emotions. For example, the selection unit can prioritize the selection of books with high emotion scores. The selection unit can also clarify specific emotion analysis methods and criteria and analyze information such as emotion classifications as positive, negative, and neutral. This allows the selection and delivery of books based on the user's emotions, thereby providing a more personalized reading experience.

[0076] The book selection unit can analyze the relevance with other media based on the user's reading history, likes, and answers to the question list, and suggest related books. The book selection unit analyzes the relevance with other media based, for example, on the user's reading history, likes on social media, and answers to the question list. For example, it can suggest movies or music with the same theme or storyline. The book selection unit can also clarify the specific types of other media and analysis methods, and analyze information on movies, music, games, etc. This can enhance the reading experience by analyzing the relevance with other media based on the user's reading history, likes on social media, and answers to the question list, and suggesting related books.

[0077] The book selection unit can suggest reading events and circles that may be of interest to the user based on their reading history, likes, and answers to the question list. The book selection unit can suggest reading events and circles that may be of interest to the user based on, for example, their reading history, likes on social media, and answers to the question list. For example, it can suggest events related to a specific genre or author. The book selection unit can also clarify the specific types of reading events and circles and how to suggest them, and analyze information on online book clubs and local book clubs, etc. This can enhance the reading experience by suggesting reading events and circles that may be of interest to the user based on their reading history, likes on social media, and answers to the question list.

[0078] The selection unit can use an emotion estimation function to analyze changes in emotions and suggest books that correspond to those emotions. The selection unit can identify changes in emotions using the emotion estimation function, for example, based on the user's reading history, likes on social media, and answers to a question list. For example, it can prioritize the suggestion of books with high emotion scores. The selection unit can also clarify specific analysis methods and criteria for emotional changes and analyze information such as emotion scores before and after reading and emotion analysis of reviews. This allows the unit to analyze changes in the user's emotions and suggest books that correspond to those emotions, providing a more personalized reading experience.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The reading history collection unit can analyze the user's reading posture based on the user's reading history. For example, if the user often reads while sitting, the unit can suggest a reading environment suited to that posture. Also, if the user often reads while standing, the unit can suggest a reading stand or lighting that is suitable for that posture. This can improve the reading experience by suggesting the user's optimal reading posture.

[0081] The SNS analysis unit can analyze the reading-related posts shared by the user on SNS and identify the reading community to which the user belongs. For example, if the user belongs to a community related to a particular genre or author, the SNS analysis unit can suggest books related to that community. The SNS analysis unit can also analyze the activities of online forums and groups in which the user participates and suggest related books. This makes it possible to suggest books based on the reading community to which the user belongs.

[0082] The question list analysis unit can analyze the user's motivation for reading based on the answers to the question list and suggest books that match that motivation. For example, for a user who prefers reading for the purpose of self-improvement or skill development, related self-improvement books or specialized books can be suggested. Also, for a user who prefers reading for entertainment or relaxation, fiction or highly entertaining books can be suggested. This makes it possible to suggest books that match the user's reading motivation.

[0083] The book selection unit can identify books that made a particularly strong impression on the user among those they have read in the past based on the user's reading history, likes on social media, and answers to questions in the question list, and suggest books related to those books. For example, it can suggest books related to a specific author or series. The book selection unit can also suggest books in themes or genres related to books that the user has given particularly high ratings to. This makes it possible to suggest books based on the user's past reading experiences.

[0084] The delivery unit can suggest the most convenient delivery method for the user based on the user's reading history, likes on social media, and answers to the question list. For example, if the user wants to receive the book at home, the delivery unit can suggest a delivery schedule tailored to that address. Also, if the user wants to receive the book at their workplace or a designated pick-up location, the delivery unit can suggest a delivery method tailored to that location. This can improve the reading experience by suggesting the optimal delivery method for the user.

[0085] The reading history collection unit can use the emotion estimation function to identify the book that gave the user the most emotional satisfaction from the reading history and analyze the trend. For example, it can calculate an emotion score based on reviews and ratings after reading. The reading history collection unit can also use the emotion estimation function to analyze emotions using technologies such as text analysis, facial expression recognition, and voice analysis. This allows the unit to identify the book that gave the user the most emotional satisfaction and analyze the trend, making it possible to suggest more personalized books.

[0086] The SNS analysis unit can use the emotion estimation function to analyze a user's emotions from reviews and suggest books based on those emotions. For example, it can analyze a user's SNS reviews and identify their emotions using the emotion estimation function. For example, it can calculate an emotion score from the content of the review and suggest books based on those emotions. The SNS analysis unit can also clarify specific emotion analysis methods and criteria and analyze information such as emotion classifications as positive, negative, and neutral. This allows the system to analyze a user's emotions and suggest books based on those emotions, providing a more personalized reading experience.

[0087] The question list analysis unit can use the emotion estimation function to analyze the user's emotions from the answers to the question list and suggest books based on those emotions. For example, the answer to the question list can be analyzed and the emotion estimation function can be used to identify the user's emotions. For example, an emotion score can be calculated from the content of the answers and books can be suggested based on those emotions. The question list analysis unit can also clarify specific emotion analysis methods and criteria and analyze information such as positive, negative, and neutral emotion classifications. This allows the user's emotions to be analyzed and books suggested based on those emotions, providing a more personalized reading experience.

[0088] The selection unit can use the emotion estimation function to select and deliver books based on the user's emotions. For example, the emotion estimation function can be used to select books based on the user's emotions. For example, books with high emotion scores can be preferentially selected. The selection unit can also clarify specific emotion analysis methods and criteria and analyze information such as positive, negative, and neutral emotion classifications. This allows the selection and delivery of books based on the user's emotions, providing a more personalized reading experience.

[0089] The question list analysis unit can use the emotion estimation function to analyze changes in the user's emotions from the answers to the question list and suggest books that correspond to the emotions. For example, the answer to the question list can be analyzed and the emotion estimation function can be used to identify changes in emotions. For example, an emotion score can be calculated from the content of the answers and books can be suggested that correspond to the emotions. The question list analysis unit can also clarify specific analysis methods and criteria for changes in emotions and analyze information such as the emotion scores of the answers to the question list and the results of the emotion estimation function. This allows the user to analyze changes in emotions and suggest books that correspond to the emotions, thereby providing a more personalized reading experience.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The reading history collection unit collects the user's reading history. For example, it collects information such as the title, author, genre, and rating of the books the user has read. It can also collect information such as e-book history, paper book history, and reading time. Step 2: The SNS analysis unit analyzes the reading history collected by the reading history collection unit. For example, it analyzes information about books that users have liked or reviewed on SNS. It can also analyze information such as Facebook likes, Twitter retweets, and Amazon reviews. Step 3: The Question List Analysis Unit analyzes the answers to the question list based on the likes and reviews on social media analyzed by the SNS Analysis Unit. For example, it analyzes answers to questions such as "What is your favorite genre?" or "What is the most interesting book you have read recently?" It can also analyze answers collected through online surveys or in-app question functions. Step 4: The book selection unit selects books based on the data analyzed by the question list analysis unit. For example, it comprehensively analyzes the user's reading history, likes on social media, and answers to the question list to select books that the user is likely to be interested in. It can also select books based on criteria such as genre, author, and publication year. Step 5: The delivery unit delivers the books selected by the book selection unit to the user. For example, the books may be delivered by courier. Alternatively, the books may be delivered by electronic book download or by regional delivery.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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. [Explanation of symbols]

[0159] 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 reading history collection unit that collects a user's reading history; an SNS analysis unit that analyzes the reading history collected by the reading history collection unit; a question list analysis unit that analyzes answers to a question list based on likes and reviews on the SNS analyzed by the SNS analysis unit; a book selection unit that selects a book based on the data analyzed by the question list analysis unit; a delivery unit that delivers the book selected by the book selection unit to the user. A system characterized by:

2. The reading history collection unit Analyze the relationship with other media based on the reading history and suggest related content 2. The system of claim 1.

3. The SNS analysis unit Analyzing the time periods of the likes and reviews to identify the time periods when users are most active 2. The system of claim 1.

4. The question list analysis unit Based on the answers to the questionnaire, the system analyzes the user's reading habits and environment in detail and suggests the optimal reading environment.

2. The system of claim 1.

5. The main selection unit Select and deliver the book based on emotion 2. The system of claim 1.

6. The reading history collection unit Identifying the book that the user found most emotionally satisfying from the reading history and analyzing the trend 2. The system of claim 1.

Images