System

Abstract

An object of a system according to an embodiment is to efficiently manage a book for stack reading and propose an optimal handling method.SOLUTION: A system includes an image acquisition unit, a listing unit, an analysis unit, and a proposal unit. The image acquisition unit acquires image data of the spine. The listing unit creates a list of books based on the image data of the spine obtained by the image obtaining unit. The analysis unit analyzes a book market price, a topic trend, and an individual reading tendency on the basis of the book list created by the listing unit. The proposal unit proposes an optimum handling method based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to efficiently manage unread books or propose optimal ways to deal with them.

[0005] The system according to the embodiment aims to efficiently manage unread books and propose optimal ways to deal with them. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a listing unit, an analysis unit, and a suggestion unit. The image acquisition unit acquires image data of the spine. The listing unit creates a book list based on the image data of the spine acquired by the image acquisition unit. The analysis unit analyzes book market prices, topical trends, and personal reading habits based on the book list created by the listing unit. The suggestion unit suggests the optimal response method based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage unread books and propose optimal ways to deal with them. [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) The pile-up processing system according to an embodiment of the present invention is a system for efficiently processing piles of unread books. This system uses AI technology to propose the optimal response method for whether to read, sell, or leave books on the bookshelf, taking into account the current market price of books, popular trends, and personal reading habits. As a result, the pile-up processing system efficiently clears piles of unread books, allowing users to enjoy more books.

[0029] The pile-up processing system according to the embodiment includes an image acquisition unit, a list creation unit, an analysis unit, and a suggestion unit. The image acquisition unit acquires image data of the spines of books. For example, a user can take a photo of a pile of books with a smartphone and upload the image to the system, causing the image acquisition unit to automatically acquire image data of the spines. The image acquisition unit can also acquire high-precision image data using the type of camera and image processing technology. The list creation unit creates a book list based on the image data of the spines acquired by the image acquisition unit. For example, a generation AI uses image recognition technology to list the titles and author names of each book. The list creation unit can also manage information included in the list (e.g., title, author, ISBN) and the storage format of the list (e.g., database, text file). The analysis unit analyzes book market prices, topical trends, and individual reading habits based on the book list created by the list creation unit. For example, the generation AI collects data from online bookstores and auction sites to evaluate the current market value of each book. The generation AI can also analyze data from social media and news sites to identify which books are trending. Furthermore, the analysis unit identifies the genre of books the user prefers and the pace at which the user reads books based on the user's reading history and preferences. The suggestion unit suggests the optimal response based on the data analyzed by the analysis unit. For example, it suggests selling books whose market price has risen, and suggesting reading books that are popular or that suit personal preferences. It also suggests leaving books that do not require special processing on the bookshelf. In this way, the pile-up processing system according to the embodiment efficiently clears piles of books and allows users to enjoy more books. For example, selling books whose market price has risen can earn revenue, and reading books that are popular can help users keep up with the latest trends. Furthermore, reading books that suit personal preferences can increase the enjoyment of reading.

[0030] The listing unit can automatically determine the condition of a book from image data of the spine and reflect this in the list. For example, the listing unit uses a generation AI to analyze image data of the spine and evaluate the condition of the book's cover and pages. For example, it can detect scratches on the cover and creases in the pages and automatically determine whether the book is new, used, or damaged. The listing unit can also use image recognition technology to perform a detailed analysis of the book's condition and reflect this in the list. This allows the automatic determination of the book's condition and reflection in the list to provide more accurate information.

[0031] The list creation unit can automatically classify the genre and theme of a book from image data of the spine and tag the list. For example, the list creation unit uses a generation AI to analyze image data of the spine and automatically classify the genre and theme from the book title and author name. For example, tags such as mystery, science fiction, and business are added. The list creation unit can also tag lists based on genre and theme classification criteria. This makes it easier for users to organize their books by automatically classifying the genre and theme of books and tagging them in lists.

[0032] The listing unit can automatically determine the publication year and edition number of a book from the image data of the spine and add it to the list. For example, the listing unit uses a generation AI to analyze the image data of the spine and automatically determine the publication year and edition number of a book. For example, it reads the publication year and edition number written on the spine and adds them to the list. The listing unit can also use an ISBN database or OCR technology to perform a detailed analysis of the publication year and edition number and reflect this in the list. This allows the automatic determination of the publication year and edition number of a book and adding it to the list, providing more detailed information.

[0033] The listing unit can automatically measure the weight and size of a book from image data of the spine and reflect this in the list. For example, the listing unit uses a generation AI to analyze image data of the spine and automatically measure the weight and size of a book. For example, it estimates the weight and size based on the dimensions and thickness of the spine. The listing unit can also use image analysis technology and existing databases to perform a detailed analysis of the weight and size and reflect this in the list. This makes physical management easier by automatically measuring the weight and size of books and reflecting this in the list.

[0034] The analysis unit can track fluctuations in book prices in real time and suggest the optimal timing to sell. For example, the analysis unit builds a system in which a generation AI tracks fluctuations in book prices in real time and suggests the optimal timing to sell. For example, the analysis unit analyzes data from online bookstores and auction sites to collect price fluctuation data. The analysis unit can also suggest the optimal timing to sell based on peak prices and increased demand. This allows users to maximize their profits by tracking fluctuations in book prices in real time and suggesting the optimal timing to sell.

[0035] When analyzing book topic trends, the analysis unit can prioritize collecting data from specific regions or communities and identify region-specific trends. For example, when the generation AI analyzes book topic trends, the analysis unit prioritizes collecting data from specific regions or communities. For example, it can analyze data from local social media and news sites to identify region-specific trends. The analysis unit can also analyze region-specific trends in detail using region-specific data collection methods and analysis algorithms. This allows for the identification of region-specific trends, enabling more personalized suggestions.

[0036] The analysis unit can use past market data to predict future prices when analyzing book prices. For example, the analysis unit constructs a system in which the generation AI uses past market data to predict future prices when analyzing book prices. For example, it creates a prediction model based on past price fluctuation data. The analysis unit can also make detailed predictions of future prices using past data analysis methods and prediction algorithms. This makes it possible to predict future prices using past market data and suggest more accurate timing for selling.

[0037] When analyzing book topic trends, the analysis unit also collects data in different languages ​​and can identify international trends. For example, when the generative AI analyzes book topic trends, the analysis unit also collects data in different languages ​​and builds a system to identify international trends. For example, it analyzes data in English, French, Chinese, etc. and identifies international trends. The analysis unit can also analyze international trends in detail using data collection methods and analysis algorithms for different languages. This makes it possible to collect data in different languages ​​and identify international trends, making it possible to make proposals from a global perspective.

[0038] When analyzing a user's reading habits, the analysis unit can automatically collect notes and highlights taken while reading and reflect them in the trend analysis. For example, when the generation AI analyzes a user's reading habits, the analysis unit automatically collects notes and highlights taken while reading from an e-book reader or app and reflects them in the trend analysis. For example, it analyzes notes related to specific themes or keywords. The analysis unit can also perform detailed analysis of notes and highlights using OCR analysis of handwritten notes or digital note collection methods. This allows for the automatic collection of notes and highlights taken while reading and their reflection in the trend analysis, enabling a more accurate analysis of reading habits.

[0039] The analysis unit can use the eye-tracking data to improve accuracy when analyzing a user's reading speed. For example, the analysis unit builds a system that uses eye-tracking data to improve accuracy when the generation AI analyzes a user's reading speed. For example, the analysis unit identifies the reading speed by analyzing eye movements. The analysis unit can also analyze the eye-tracking data in detail using the type of eye-tracking device and data analysis algorithms. This makes it possible to more accurately grasp reading speed by improving the analysis accuracy of reading speed using eye-tracking data.

[0040] When analyzing a user's reading habits, the analysis unit analyzes not only past reading history but also the contents of unread books to identify potential interests. For example, when the generation AI analyzes a user's reading habits, the analysis unit builds a system that analyzes not only past reading history but also the contents of unread books to identify potential interests. For example, it analyzes the genres and themes of unread books. The analysis unit can also analyze the contents of unread books in detail using purchase history and unread list management methods. This makes it possible to identify a user's potential interests by analyzing not only past reading history but also the contents of unread books, enabling more appropriate suggestions.

[0041] The analysis unit can integrate reading data from different devices when analyzing a user's reading speed. For example, the analysis unit builds a system that integrates reading data from different devices when the generation AI analyzes a user's reading speed. For example, it integrates data from smartphones, tablets, and e-book readers. The analysis unit can also perform detailed analysis of reading data using different device types and data collection methods. This enables more accurate analysis of reading speed by integrating reading data from different devices.

[0042] The suggestion unit can take the user's current living situation into consideration when proposing the optimal response method. For example, the suggestion unit builds a system in which the generation AI takes the user's current living situation into consideration when proposing the optimal response method. For example, the suggestion content can be adjusted based on how busy the user is at work or how much free time they have. The suggestion unit can also analyze the user's living situation in detail by analyzing survey data and behavioral logs. This makes it possible to make more realistic and feasible suggestions by taking the user's current living situation into consideration.

[0043] When proposing the optimal response method, the suggestion unit can learn the user's past suggestion history and its results to improve the accuracy of the suggestions. For example, when the generation AI proposes the optimal response method, the suggestion unit builds a system that learns the user's past suggestion history and its results to improve the accuracy of the suggestions. For example, the suggestion unit adjusts the content of the suggestions based on past suggestions and their results. The suggestion unit can also improve the accuracy of the suggestions in detail by using a data collection method and analysis algorithm for the suggestion history. In this way, by learning the past suggestion history and its results, the accuracy of the suggestions can be improved and more appropriate response methods can be proposed.

[0044] The suggestion unit can take into account the reading habits of the user's family and friends when proposing the optimal response method and make suggestions for sharing or exchanging. For example, the suggestion unit can build a system in which the generation AI also takes into account the reading habits of the user's family and friends when proposing the optimal response method. For example, it can suggest books that the family and friends might be interested in. The suggestion unit can also perform a detailed analysis of the reading habits of family and friends using data from shared accounts and survey results. This makes it possible to suggest sharing or exchanging by taking into account the reading habits of the user's family and friends, allowing for a shared reading experience.

[0045] The suggestion unit can take into account the user's other hobbies and interests when proposing the optimal response method and suggest related books. For example, the suggestion unit builds a system in which the generation AI also takes into account the user's other hobbies and interests when proposing the optimal response method. For example, it can suggest books related to movies and music. The suggestion unit can also analyze the user's hobbies and interests in detail by analyzing survey data and behavioral logs. This makes it possible to suggest more personalized books by taking into account the user's other hobbies and interests.

[0046] When generating a recommendation, the suggestion unit can refer to the user's past book reviews and impressions to create an individually customized recommendation. For example, when a generation AI generates a recommendation, the suggestion unit builds a system that refers to the user's past book reviews and impressions to create an individually customized recommendation. For example, the suggestion unit generates a recommendation based on the content of past reviews. The suggestion unit can also use book review data collection methods and text analysis to analyze the user's impressions in detail and optimize the recommendation. This makes it possible to provide more personalized recommendations by referring to the user's past book reviews and impressions.

[0047] When generating a recommendation, the suggestion unit can also mention the author of the recommended book and other related works, thereby increasing the amount of information in the recommendation. For example, the suggestion unit can build a system in which, when a generation AI generates a recommendation, it also mentions the author of the recommended book and other related works, thereby increasing the amount of information in the recommendation. For example, it can introduce the author's past works and related series. The suggestion unit can also increase the amount of information in the recommendation by using data collection methods for the author's biography and other works. In this way, by mentioning the author of the recommended book and other related works, the amount of information in the recommendation can be increased, providing useful information to the user.

[0048] When generating a recommendation, the suggestion unit can automatically generate recommendation in different languages, thereby achieving multilingual support. For example, when a generation AI generates a recommendation, the suggestion unit can automatically generate recommendation in different languages, building a system that achieves multilingual support. For example, recommendation in languages ​​such as English, French, and Chinese is generated. The suggestion unit can also use a translation algorithm and supported language types to provide detailed multilingual support for recommendation. This allows recommendation in different languages ​​to be automatically generated, achieving multilingual support and enabling the system to accommodate a greater number of users.

[0049] When generating a recommendation, the suggestion unit can also mention the film adaptations of the recommended book and related media content, thereby increasing the appeal of the recommendation. For example, when the generation AI generates a recommendation, the suggestion unit can also mention the film adaptations of the recommended book and related media content, building a system that increases the appeal of the recommendation. For example, it can introduce works that have been made into movies or dramas. The suggestion unit can also increase the amount of information in the recommendation by using data collection methods for film adaptations and media content. In this way, by mentioning the film adaptations of the recommended book and related media content, the appeal of the recommendation can be increased and the user's interest can be more easily attracted.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The suggestion unit can suggest reading groups or book clubs to join based on the user's reading history. For example, if a user reads many books in a particular genre, the suggestion unit can suggest reading groups specialized in that genre. The suggestion unit can also analyze the user's reading history and encourage interaction with other users who are reading the same books. This allows users to participate in new communities through reading and share their reading experiences.

[0052] When analyzing a user's reading habits, the analysis unit collects data on posture and environment while reading and can suggest the optimal reading environment. For example, if a user has been reading in the same position for a long time, it can suggest an appropriate time to take a break. The analysis unit can also analyze data on lighting and sound environments to provide the optimal reading environment. This allows the user to enjoy reading in a comfortable reading environment.

[0053] The suggestion unit can suggest events and seminars related to reading based on the user's reading history. For example, if the user has read many books by a particular author, it can suggest lectures and book signings by that author. The suggestion unit can also analyze the user's reading history and suggest related workshops and discussion events. This allows the user to participate in events related to reading and deepen their knowledge.

[0054] The suggestion unit can suggest activities and hobbies related to reading based on the user's reading history. For example, if the user reads a lot of historical novels, it can suggest visiting historical tourist spots and museums. The suggestion unit can also analyze the user's reading history and suggest watching related movies and documentaries. This allows the user to discover new activities and hobbies through reading.

[0055] The suggestion unit can suggest craft or DIY projects related to the user's reading based on the user's reading history. For example, if the user reads a lot of fantasy novels, the suggestion unit can suggest craft projects that recreate the worldview of those novels. The suggestion unit can also analyze the user's reading history and suggest related DIY projects. This allows the user to try new craft or DIY projects through reading.

[0056] The suggestion unit can suggest dishes and recipes related to the user's reading based on the user's reading history. For example, if the user reads many cookbooks, it can suggest recipes from those books. The suggestion unit can also analyze the user's reading history and suggest related dishes and recipes. This allows the user to try new dishes and recipes through reading.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The image acquisition unit acquires image data of the spines. For example, a user can take a photo of a stack of books with their smartphone and upload the image to the system, and the image acquisition unit will automatically acquire image data of the spines. The image acquisition unit can also acquire highly accurate image data by using the type of camera and image processing technology. Step 2: The list creation unit creates a list of books based on the image data of the spines acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to list the titles and author names of each book. The list creation unit can also manage the information contained in the list (title, author, ISBN, etc.) and the storage format of the list (database, text file, etc.). Step 3: The analysis unit analyzes book market prices, topical trends, and personal reading habits based on the list of books created by the list creation unit. For example, the generation AI collects data from online bookstores and auction sites to evaluate the current market value of each book. The generation AI can also analyze data from social media and news sites to identify which books are trending. Furthermore, the analysis unit determines the user's preferred genre of books and the pace at which they read based on their reading history and preferences. Step 4: The suggestion section proposes the optimal course of action based on the data analyzed by the analysis section. For example, it suggests selling books whose market prices are high, suggesting reading books that are trending or that suit personal tastes, and suggesting leaving books on the bookshelf that do not require any particular processing.

[0059] (Example 2) The pile-up processing system according to an embodiment of the present invention is a system for efficiently processing piles of unread books. This system uses AI technology to propose the optimal response method for whether to read, sell, or leave books on the bookshelf, taking into account the current market price of books, popular trends, and personal reading habits. As a result, the pile-up processing system efficiently clears piles of unread books, allowing users to enjoy more books.

[0060] The pile-up processing system according to the embodiment includes an image acquisition unit, a list creation unit, an analysis unit, and a suggestion unit. The image acquisition unit acquires image data of the spines of books. For example, a user can take a photo of a pile of books with a smartphone and upload the image to the system, causing the image acquisition unit to automatically acquire image data of the spines. The image acquisition unit can also acquire high-precision image data using the type of camera and image processing technology. The list creation unit creates a book list based on the image data of the spines acquired by the image acquisition unit. For example, a generation AI uses image recognition technology to list the titles and author names of each book. The list creation unit can also manage information included in the list (e.g., title, author, ISBN) and the storage format of the list (e.g., database, text file). The analysis unit analyzes book market prices, topical trends, and individual reading habits based on the book list created by the list creation unit. For example, the generation AI collects data from online bookstores and auction sites to evaluate the current market value of each book. The generation AI can also analyze data from social media and news sites to identify which books are trending. Furthermore, the analysis unit identifies the genre of books the user prefers and the pace at which the user reads books based on the user's reading history and preferences. The suggestion unit suggests the optimal response based on the data analyzed by the analysis unit. For example, it suggests selling books whose market price has risen, and suggesting reading books that are popular or that suit personal preferences. It also suggests leaving books that do not require special processing on the bookshelf. In this way, the pile-up processing system according to the embodiment efficiently clears piles of books and allows users to enjoy more books. For example, selling books whose market price has risen can earn revenue, and reading books that are popular can help users keep up with the latest trends. Furthermore, reading books that suit personal preferences can increase the enjoyment of reading.

[0061] The listing unit can automatically determine the condition of a book from image data of the spine and reflect this in the list. For example, the listing unit uses a generation AI to analyze image data of the spine and evaluate the condition of the book's cover and pages. For example, it can detect scratches on the cover and creases in the pages and automatically determine whether the book is new, used, or damaged. The listing unit can also use image recognition technology to perform a detailed analysis of the book's condition and reflect this in the list. This allows the automatic determination of the book's condition and reflection in the list to provide more accurate information.

[0062] The list creation unit can automatically classify the genre and theme of a book from image data of the spine and tag the list. For example, the list creation unit uses a generation AI to analyze image data of the spine and automatically classify the genre and theme from the book title and author name. For example, tags such as mystery, science fiction, and business are added. The list creation unit can also tag lists based on genre and theme classification criteria. This makes it easier for users to organize their books by automatically classifying the genre and theme of books and tagging them in lists.

[0063] The listing unit can automatically determine the publication year and edition number of a book from the image data of the spine and add it to the list. For example, the listing unit uses a generation AI to analyze the image data of the spine and automatically determine the publication year and edition number of a book. For example, it reads the publication year and edition number written on the spine and adds them to the list. The listing unit can also use an ISBN database or OCR technology to perform a detailed analysis of the publication year and edition number and reflect this in the list. This allows the automatic determination of the publication year and edition number of a book and adding it to the list, providing more detailed information.

[0064] The listing unit can automatically measure the weight and size of a book from image data of the spine and reflect this in the list. For example, the listing unit uses a generation AI to analyze image data of the spine and automatically measure the weight and size of a book. For example, it estimates the weight and size based on the dimensions and thickness of the spine. The listing unit can also use image analysis technology and existing databases to perform a detailed analysis of the weight and size and reflect this in the list. This makes physical management easier by automatically measuring the weight and size of books and reflecting this in the list.

[0065] The analysis unit can track fluctuations in book prices in real time and suggest the optimal timing to sell. For example, the analysis unit builds a system in which a generation AI tracks fluctuations in book prices in real time and suggests the optimal timing to sell. For example, the analysis unit analyzes data from online bookstores and auction sites to collect price fluctuation data. The analysis unit can also suggest the optimal timing to sell based on peak prices and increased demand. This allows users to maximize their profits by tracking fluctuations in book prices in real time and suggesting the optimal timing to sell.

[0066] When analyzing book topic trends, the analysis unit can prioritize collecting data from specific regions or communities and identify region-specific trends. For example, when the generation AI analyzes book topic trends, the analysis unit prioritizes collecting data from specific regions or communities. For example, it can analyze data from local social media and news sites to identify region-specific trends. The analysis unit can also analyze region-specific trends in detail using region-specific data collection methods and analysis algorithms. This allows for the identification of region-specific trends, enabling more personalized suggestions.

[0067] The analysis unit can use past market data to predict future prices when analyzing book prices. For example, the analysis unit constructs a system in which the generation AI uses past market data to predict future prices when analyzing book prices. For example, it creates a prediction model based on past price fluctuation data. The analysis unit can also make detailed predictions of future prices using past data analysis methods and prediction algorithms. This makes it possible to predict future prices using past market data and suggest more accurate timing for selling.

[0068] When analyzing book topic trends, the analysis unit also collects data in different languages ​​and can identify international trends. For example, when the generative AI analyzes book topic trends, the analysis unit also collects data in different languages ​​and builds a system to identify international trends. For example, it analyzes data in English, French, Chinese, etc. and identifies international trends. The analysis unit can also analyze international trends in detail using data collection methods and analysis algorithms for different languages. This makes it possible to collect data in different languages ​​and identify international trends, making it possible to make proposals from a global perspective.

[0069] When analyzing book topic trends, the analysis unit can use the emotion estimation function to monitor users' emotional reactions to topic trends in real time and update the suggestions according to changes in the trend. For example, the analysis unit can use the emotion estimation function to build a system that monitors users' emotional reactions to topic trends in real time and updates the suggestions according to changes in the trend. For example, the analysis unit can adjust the suggestions every time the emotion score changes. The analysis unit can also use emotion analysis algorithms and data collection methods to analyze users' emotional reactions in detail and optimize the suggestions. This enables more personalized suggestions by using the emotion estimation function to monitor users' emotional reactions in real time and updating the suggestions according to changes in the trend.

[0070] When analyzing a user's reading habits, the analysis unit can automatically collect notes and highlights taken while reading and reflect them in the trend analysis. For example, when the generation AI analyzes a user's reading habits, the analysis unit automatically collects notes and highlights taken while reading from an e-book reader or app and reflects them in the trend analysis. For example, it analyzes notes related to specific themes or keywords. The analysis unit can also perform detailed analysis of notes and highlights using OCR analysis of handwritten notes or digital note collection methods. This allows for the automatic collection of notes and highlights taken while reading and their reflection in the trend analysis, enabling a more accurate analysis of reading habits.

[0071] The analysis unit can use the eye-tracking data to improve accuracy when analyzing a user's reading speed. For example, the analysis unit builds a system that uses eye-tracking data to improve accuracy when the generation AI analyzes a user's reading speed. For example, the analysis unit identifies the reading speed by analyzing eye movements. The analysis unit can also analyze the eye-tracking data in detail using the type of eye-tracking device and data analysis algorithms. This makes it possible to more accurately grasp reading speed by improving the analysis accuracy of reading speed using eye-tracking data.

[0072] When analyzing a user's reading habits, the analysis unit analyzes not only past reading history but also the contents of unread books to identify potential interests. For example, when the generation AI analyzes a user's reading habits, the analysis unit builds a system that analyzes not only past reading history but also the contents of unread books to identify potential interests. For example, it analyzes the genres and themes of unread books. The analysis unit can also analyze the contents of unread books in detail using purchase history and unread list management methods. This makes it possible to identify a user's potential interests by analyzing not only past reading history but also the contents of unread books, enabling more appropriate suggestions.

[0073] The analysis unit can integrate reading data from different devices when analyzing a user's reading speed. For example, the analysis unit builds a system that integrates reading data from different devices when the generation AI analyzes a user's reading speed. For example, it integrates data from smartphones, tablets, and e-book readers. The analysis unit can also perform detailed analysis of reading data using different device types and data collection methods. This enables more accurate analysis of reading speed by integrating reading data from different devices.

[0074] When analyzing a user's reading habits, the analysis unit can use an emotion estimation function to monitor the user's emotions while reading in real time and suggest adjusting the reading pace according to the emotions. The analysis unit, for example, uses the emotion estimation function to build a system that monitors the user's emotions while reading in real time and suggests adjusting the reading pace according to the emotions. For example, if the emotion score is low, the analysis unit can suggest slowing down the reading pace. The analysis unit can also use an emotion analysis algorithm and a data collection method to analyze the user's emotions in detail and optimize the adjustment of the reading pace. This allows the user to use the emotion estimation function to monitor the user's emotions while reading in real time and suggest adjusting the reading pace according to the emotions, providing a more comfortable reading experience.

[0075] The suggestion unit can take the user's current living situation into consideration when proposing the optimal response method. For example, the suggestion unit builds a system in which the generation AI takes the user's current living situation into consideration when proposing the optimal response method. For example, the suggestion content can be adjusted based on how busy the user is at work or how much free time they have. The suggestion unit can also analyze the user's living situation in detail by analyzing survey data and behavioral logs. This makes it possible to make more realistic and feasible suggestions by taking the user's current living situation into consideration.

[0076] When proposing the optimal response method, the suggestion unit can learn the user's past suggestion history and its results to improve the accuracy of the suggestions. For example, when the generation AI proposes the optimal response method, the suggestion unit builds a system that learns the user's past suggestion history and its results to improve the accuracy of the suggestions. For example, the suggestion unit adjusts the content of the suggestions based on past suggestions and their results. The suggestion unit can also improve the accuracy of the suggestions in detail by using a data collection method and analysis algorithm for the suggestion history. In this way, by learning the past suggestion history and its results, the accuracy of the suggestions can be improved and more appropriate response methods can be proposed.

[0077] When proposing an optimal response method, the suggestion unit can use the emotion estimation function to analyze the user's emotional reaction to the proposal content and prioritize proposals that are emotionally easy to accept. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the proposal content in real time and build a system that prioritizes proposals that are emotionally easy to accept. For example, it prioritizes proposals that evoke strong positive emotions. The suggestion unit can also analyze the user's emotional reaction in detail and optimize the proposal content using a method for adjusting the proposal content based on the emotion analysis results. In this way, by using the emotion estimation function to analyze the user's emotional reaction to the proposal content and prioritizing proposals that are emotionally easy to accept, user satisfaction is improved.

[0078] The suggestion unit can take into account the reading habits of the user's family and friends when proposing the optimal response method and make suggestions for sharing or exchanging. For example, the suggestion unit can build a system in which the generation AI also takes into account the reading habits of the user's family and friends when proposing the optimal response method. For example, it can suggest books that the family and friends might be interested in. The suggestion unit can also perform a detailed analysis of the reading habits of family and friends using data from shared accounts and survey results. This makes it possible to suggest sharing or exchanging by taking into account the reading habits of the user's family and friends, allowing for a shared reading experience.

[0079] The suggestion unit can take into account the user's other hobbies and interests when proposing the optimal response method and suggest related books. For example, the suggestion unit builds a system in which the generation AI also takes into account the user's other hobbies and interests when proposing the optimal response method. For example, it can suggest books related to movies and music. The suggestion unit can also analyze the user's hobbies and interests in detail by analyzing survey data and behavioral logs. This makes it possible to suggest more personalized books by taking into account the user's other hobbies and interests.

[0080] When proposing an optimal response method, the suggestion unit can use the emotion estimation function to monitor the user's emotional reaction to the proposal content in real time and continuously optimize the proposal content. The suggestion unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the proposal content in real time and builds a system that continuously optimizes the proposal content. For example, the suggestion unit adjusts the proposal content every time the emotion score changes. The suggestion unit can also analyze the user's emotional reaction in detail and optimize the proposal content using a method for adjusting the proposal content based on the emotion analysis result. This enables more appropriate suggestions to be made by using the emotion estimation function to monitor the user's emotional reaction to the proposal content in real time and continuously optimizing the proposal content.

[0081] When generating a recommendation, the suggestion unit can refer to the user's past book reviews and impressions to create an individually customized recommendation. For example, when a generation AI generates a recommendation, the suggestion unit builds a system that refers to the user's past book reviews and impressions to create an individually customized recommendation. For example, the suggestion unit generates a recommendation based on the content of past reviews. The suggestion unit can also use book review data collection methods and text analysis to analyze the user's impressions in detail and optimize the recommendation. This makes it possible to provide more personalized recommendations by referring to the user's past book reviews and impressions.

[0082] When generating a recommendation, the suggestion unit can also mention the author of the recommended book and other related works, thereby increasing the amount of information in the recommendation. For example, the suggestion unit can build a system in which, when a generation AI generates a recommendation, it also mentions the author of the recommended book and other related works, thereby increasing the amount of information in the recommendation. For example, it can introduce the author's past works and related series. The suggestion unit can also increase the amount of information in the recommendation by using data collection methods for the author's biography and other works. In this way, by mentioning the author of the recommended book and other related works, the amount of information in the recommendation can be increased, providing useful information to the user.

[0083] When generating a recommendation, the suggestion unit uses the emotion estimation function to analyze the user's emotional response to the recommendation and prioritizes the use of emotionally resonant expressions. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional response to the recommendation in real time and builds a system that prioritizes the use of emotionally resonant expressions. For example, it prioritizes expressions that evoke strong positive emotions. The suggestion unit can also use an expression selection method based on the emotion analysis results to analyze the user's emotional response in detail and optimize the recommendation. This makes it possible to provide more effective recommendation by using the emotion estimation function to analyze the user's emotional response to the recommendation and prioritize the use of emotionally resonant expressions.

[0084] When generating a recommendation, the suggestion unit can automatically generate recommendation in different languages, thereby achieving multilingual support. For example, when a generation AI generates a recommendation, the suggestion unit can automatically generate recommendation in different languages, building a system that achieves multilingual support. For example, recommendation in languages ​​such as English, French, and Chinese is generated. The suggestion unit can also use a translation algorithm and supported language types to provide detailed multilingual support for recommendation. This allows recommendation in different languages ​​to be automatically generated, achieving multilingual support and enabling the system to accommodate a greater number of users.

[0085] When generating a recommendation, the suggestion unit can also mention the film adaptations of the recommended book and related media content, thereby increasing the appeal of the recommendation. For example, when the generation AI generates a recommendation, the suggestion unit can also mention the film adaptations of the recommended book and related media content, building a system that increases the appeal of the recommendation. For example, it can introduce works that have been made into movies or dramas. The suggestion unit can also increase the amount of information in the recommendation by using data collection methods for film adaptations and media content. In this way, by mentioning the film adaptations of the recommended book and related media content, the appeal of the recommendation can be increased and the user's interest can be more easily attracted.

[0086] When generating a recommendation, the suggestion unit can use an emotion estimation function to monitor the user's emotional response to the recommendation in real time and continuously optimize the content of the recommendation. For example, the suggestion unit uses the emotion estimation function to build a system that monitors the user's emotional response to the recommendation in real time and continuously optimizes the content of the recommendation. For example, the suggestion unit adjusts the recommendation each time the emotion score changes. The suggestion unit can also use a content adjustment method based on the emotion analysis results to analyze the user's emotional response in detail and optimize the recommendation. In this way, by using the emotion estimation function to monitor the user's emotional response to the recommendation in real time and continuously optimizing the content of the recommendation, more effective recommendations can be provided.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The suggestion unit can suggest reading groups or book clubs to join based on the user's reading history. For example, if a user reads many books in a particular genre, the suggestion unit can suggest reading groups specialized in that genre. The suggestion unit can also analyze the user's reading history and encourage interaction with other users who are reading the same books. This allows users to participate in new communities through reading and share their reading experiences.

[0089] When analyzing a user's reading habits, the analysis unit collects data on posture and environment while reading and can suggest the optimal reading environment. For example, if a user has been reading in the same position for a long time, it can suggest an appropriate time to take a break. The analysis unit can also analyze data on lighting and sound environments to provide the optimal reading environment. This allows the user to enjoy reading in a comfortable reading environment.

[0090] The suggestion unit can suggest events and seminars related to reading based on the user's reading history. For example, if the user has read many books by a particular author, it can suggest lectures and book signings by that author. The suggestion unit can also analyze the user's reading history and suggest related workshops and discussion events. This allows the user to participate in events related to reading and deepen their knowledge.

[0091] When analyzing a user's reading habits, the analysis unit can monitor the user's heart rate and stress level while reading and suggest a relaxing reading pace. For example, if the user's heart rate is elevated, the analysis unit can suggest slowing down the reading pace. The analysis unit can also analyze stress level data and provide a relaxing reading environment, allowing the user to enjoy reading in a relaxed state.

[0092] The suggestion unit can suggest activities and hobbies related to reading based on the user's reading history. For example, if the user reads a lot of historical novels, it can suggest visiting historical tourist spots and museums. The suggestion unit can also analyze the user's reading history and suggest watching related movies and documentaries. This allows the user to discover new activities and hobbies through reading.

[0093] When analyzing a user's reading habits, the analysis unit can use an emotion estimation function to monitor the user's emotions while reading in real time and suggest reading genres based on the emotions. For example, if the user is feeling stressed, the analysis unit can suggest books in a relaxing genre. The analysis unit can also use an emotion analysis algorithm to perform a detailed analysis of reading genres based on the user's emotions. This allows the emotion estimation function to monitor the user's emotions in real time and suggest reading genres based on the emotions, providing a more comfortable reading experience.

[0094] The suggestion unit can suggest craft or DIY projects related to the user's reading based on the user's reading history. For example, if the user reads a lot of fantasy novels, the suggestion unit can suggest craft projects that recreate the worldview of those novels. The suggestion unit can also analyze the user's reading history and suggest related DIY projects. This allows the user to try new craft or DIY projects through reading.

[0095] When analyzing a user's reading habits, the analysis unit can use an emotion estimation function to monitor the user's emotions while reading in real time and suggest adjusting the reading time according to the emotions. For example, if the user is tired, the analysis unit can suggest shortening the reading time. The analysis unit can also use an emotion analysis algorithm to perform a detailed analysis of the reading time according to the user's emotions. This allows the emotion estimation function to monitor the user's emotions in real time and suggest adjusting the reading time according to the emotions, providing a more comfortable reading experience.

[0096] The suggestion unit can suggest dishes and recipes related to the user's reading based on the user's reading history. For example, if the user reads many cookbooks, it can suggest recipes from those books. The suggestion unit can also analyze the user's reading history and suggest related dishes and recipes. This allows the user to try new dishes and recipes through reading.

[0097] When analyzing a user's reading habits, the analysis unit can use an emotion estimation function to monitor the user's emotions while reading in real time and suggest adjustments to the reading environment according to the emotions. For example, if the user is relaxed, it can suggest a quiet environment. The analysis unit can also use an emotion analysis algorithm to perform a detailed analysis of the reading environment according to the user's emotions. This allows the emotion estimation function to monitor the user's emotions in real time and suggest adjustments to the reading environment according to the emotions, thereby providing a more comfortable reading experience.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The image acquisition unit acquires image data of the spines. For example, a user can take a photo of a stack of books with their smartphone and upload the image to the system, and the image acquisition unit will automatically acquire image data of the spines. The image acquisition unit can also acquire highly accurate image data by using the type of camera and image processing technology. Step 2: The list creation unit creates a list of books based on the image data of the spines acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to list the titles and author names of each book. The list creation unit can also manage the information contained in the list (title, author, ISBN, etc.) and the storage format of the list (database, text file, etc.). Step 3: The analysis unit analyzes book market prices, topical trends, and personal reading habits based on the list of books created by the list creation unit. For example, the generation AI collects data from online bookstores and auction sites to evaluate the current market value of each book. The generation AI can also analyze data from social media and news sites to identify which books are trending. Furthermore, the analysis unit determines the user's preferred genre of books and the pace at which they read based on their reading history and preferences. Step 4: The suggestion section proposes the optimal course of action based on the data analyzed by the analysis section. For example, it suggests selling books whose market prices are high, suggesting reading books that are trending or that suit personal tastes, and suggesting leaving books on the bookshelf that do not require any particular processing.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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, in order to avoid confusion and to 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.

[0166] 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]

[0167] 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. an image acquisition unit that acquires image data of the spine; a listing unit that creates a book list based on the image data of the spine acquired by the image acquisition unit; an analysis unit that analyzes book market prices, topical trends, and personal reading habits based on the book list created by the listing unit; a proposal unit that proposes an optimal response method based on the data analyzed by the analysis unit. A system characterized by:

2. The listing unit The condition of the book is automatically determined from the image data of the spine and reflected in the list.

2. The system of claim 1.

3. The analysis unit Track fluctuations in the book market in real time and suggest the best time to sell 2. The system of claim 1.

4. The analysis unit When analyzing users' reading habits, automatically collect notes and highlights taken while reading and reflect them in the trend analysis.

2. The system of claim 1.

5. The proposal unit Consider the user's current life situation when suggesting the best course of action 2. The system of claim 1.

6. The analysis unit When analyzing book trending topics, the system monitors users' emotional reactions to the trending topics in real time and updates the recommendations according to the changes in the trending topics.

2. The system of claim 1.

7. The proposal unit When proposing the optimal response method, the system analyzes the user's emotional response to the content of the proposal and prioritizes the proposal that is most emotionally acceptable.

2. The system of claim 1.

8. The proposal unit When generating a recommendation, the emotional response of the user to the recommendation is analyzed, and emotionally resonant expressions are used preferentially.

2. The system of claim 1.

Images