System

Abstract

An object of the system according to the embodiment is to support creation of a reading comment, improve academic ability of a child, and reduce a burden on a guardian.SOLUTION: A system includes a basic rule presentation unit, a support unit, a correction unit, and a recommendation unit. A basic rule presentation part presents a basic rule and a writing method for creating a reading comment. The support unit supports creation of a comment based on the basic rule and the writing style presented by the basic rule presentation unit. The correction unit corrects the comment created by the support unit. The recommendation unit provides a recommendation of a writing style according to the content based on the information of the book registered in the database.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] With conventional technology, it was difficult for children to understand the basic rules and writing methods when writing book reports, which placed a heavy burden on parents.

[0005] The system according to the embodiment aims to support the creation of book reports, thereby improving children's academic abilities and reducing the burden on parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a basic rule presentation unit, a support unit, a correction unit, and a recommendation unit. The basic rule presentation unit presents basic rules and writing styles for writing a book review. The support unit supports the writing of a book review based on the basic rules and writing styles presented by the basic rule presentation unit. The correction unit corrects the book review created by the support unit. The recommendation unit provides recommendations for writing styles that are in line with the content of the book based on information about the book registered in the database. [Effects of the Invention]

[0007] The system according to the embodiment can support the creation of book reports, thereby improving children's academic abilities and reducing the burden on parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The book review writing support system according to the embodiment of the present invention is a system that efficiently and effectively supports the writing of book reviews, thereby improving children's academic ability and reducing the workload of parents.

[0029] A book review writing support system according to an embodiment includes a basic rule presentation unit, a support unit, a correction unit, and a recommendation unit. The basic rule presentation unit presents basic rules and writing methods for writing a book review. For example, the generation AI presents the basic rules and writing methods for writing a book review to a user. The generation AI provides advice such as, "It's a good idea to write a book review in a format where you first briefly introduce the outline of the book and then state your impressions and thoughts." The support unit supports the writing of a book review based on the basic rules and writing methods presented by the basic rule presentation unit. For example, the generation AI provides real-time support when a user writes a book review. The generation AI provides feedback such as, "It would be better to write this part more specifically." The correction unit corrects the book review created by the support unit. For example, the generation AI analyzes a book review created by a user, points out grammatical and phrasal errors, and suggests corrections. The generation AI suggests corrections such as, "This expression is unnatural, so it would be better to write it like this." The recommendation unit provides recommendations for writing styles that match the content of a book based on book information registered in the database. For example, the generation AI retrieves book information from the database and generates appropriate recommendations based on that information. For example, the generation AI provides advice such as, "This book is an adventure story, so in your review, it's a good idea to focus on the protagonist's growth and the course of the adventure." This allows the book review writing support system according to the embodiment to efficiently and effectively support the creation of book reviews. For example, children can learn basic rules and writing techniques for writing book reviews, while also cultivating the ability to organize and express their thoughts. Furthermore, parents can reduce the time spent correcting and providing guidance on book reviews.

[0030] The basic rule presentation unit can analyze the user's past essay data and present rules and writing methods optimized for the user. For example, the basic rule presentation unit uses a generation AI to collect the user's past essay data and analyze their writing tendencies and style. For example, the generation AI analyzes the structure and expression methods of past essays and suggests the optimal writing method for the user. The basic rule presentation unit also identifies specific weaknesses and areas for improvement based on the user's past essay data and presents corresponding rules and writing methods. For example, the generation AI provides advice for strengthening areas that lack specificity. The basic rule presentation unit also uses a generation AI to analyze the user's past essay data and provide templates and frameworks tailored to individual users. For example, the generation AI generates templates that reflect the user's frequently used expressions and structures. This allows the user to be provided with rules and writing methods optimized for the user, thereby improving the quality of essay creation.

[0031] The basic rule presentation unit can refer to the user's reading history and provide writing rules specialized for a specific genre or theme. For example, the generation AI analyzes the user's reading history to provide writing rules specialized for a specific genre or theme. The generation AI suggests, for example, how to write a review of a mystery novel. The basic rule presentation unit also generates writing rules that reflect the characteristics and important points of each genre based on the user's reading history. The generation AI provides, for example, how to write a review of a historical novel. The basic rule presentation unit also refers to the user's reading history and provides writing rules specialized for a specific theme. The generation AI suggests, for example, how to write a review of a book about environmental issues. In this way, by providing writing rules specialized for a specific genre or theme, it is possible to assist the user in writing their review.

[0032] The support unit can analyze the user's writing in real time and suggest specific improvements according to the context. For example, the support unit uses a generation AI to analyze the user's writing in real time and suggest specific improvements according to the context. For example, the generation AI points out parts of the writing that flow unnaturally and offers suggested revisions. The support unit also analyzes the user's writing and suggests specific improvements according to the context. For example, the generation AI gives advice on strengthening parts that lack specificity. The support unit also uses a generation AI to analyze the user's writing in real time and suggest specific improvements according to the context. For example, the generation AI gives advice on simplifying redundant expressions. In this way, the quality of impressions can be improved by analyzing the user's writing in real time and suggesting specific improvements.

[0033] The support unit can analyze the style and tone of the user's writing and provide advice to create a sense of unity. For example, the generation AI analyzes the style and tone of the writing written by the user and provides advice to create a sense of unity. For example, the generation AI makes suggestions to unify the writing style when it is inconsistent. The support unit also analyzes the style and tone of the writing written by the user and provides advice to create a sense of unity. For example, the generation AI gives advice to unify formal and casual writing styles. The support unit also analyzes the style and tone of the writing written by the user and provides advice to create a sense of unity. For example, the generation AI makes suggestions to make the tone of the entire writing consistent. This unifies the style and tone of the writing, thereby improving the quality of the impressions.

[0034] The support unit can accept the user's writing as voice input and provide feedback in real time using voice recognition technology. For example, the support unit uses a generation AI to accept the user's written writing as voice input and provide feedback in real time using voice recognition technology. For example, the generation AI converts what the user has said into text and suggests specific areas for improvement. In addition, when the user provides voice input, the generation AI provides feedback in real time. For example, the generation AI points out errors in grammar or expression using voice recognition technology. In addition, the support unit uses a generation AI to accept voice input and provide feedback in real time using voice recognition technology. For example, the generation AI analyzes what the user has said and suggests specific areas for improvement. This allows users to create their impressions more easily by using voice input.

[0035] The support unit can visualize the user's writing and provide visually easy-to-understand feedback. For example, the generation AI in the support unit visualizes the writing written by the user and provides visually easy-to-understand feedback. For example, the generation AI shows the structure of the writing in a diagram or chart. The support unit also visualizes the writing written by the user and provides visually easy-to-understand feedback. For example, the generation AI highlights important points. The support unit also visualizes the writing written by the user and provides visually easy-to-understand feedback. For example, the generation AI shows the flow of the writing in a flowchart. This visualization makes it easier for the user to visually understand the structure of the writing and areas for improvement.

[0036] The correction unit can point out not only grammatical and phrasing errors, but also logical structure and persuasive expressions, and propose corrections. For example, the generation AI analyzes a user's review and points out not only grammatical and phrasing errors, but also logical structure and persuasive expressions. For example, the generation AI points out logical leaps and proposes corrections. The correction unit also analyzes a user's review and not only points out grammatical and phrasing errors, but also suggests areas for improvement in logical structure. For example, the generation AI provides specific correction suggestions to make the review more persuasive. The correction unit also analyzes a user's review and points out not only grammatical and phrasing errors, but also logical structure and persuasive expressions, and proposes corrections. For example, the generation AI provides advice on strengthening the logical flow. This improves the quality of the review by pointing out not only grammatical and phrasing errors, but also logical structure and persuasive expressions.

[0037] The correction unit can compare the user's past impressions with the current one, evaluate the degree of growth, and suggest specific areas for improvement. In the correction unit, for example, the generation AI compares the user's past impressions with the current one and evaluates the degree of growth. The generation AI, for example, compares with the past sentences and suggests specific areas for improvement. The correction unit also analyzes the user's past impressions with the current one and evaluates the degree of growth. The generation AI, for example, evaluates whether past weaknesses have been improved and provides specific feedback. In the correction unit, the generation AI compares the user's past impressions with the current one, evaluates the degree of growth, and suggests specific areas for improvement. The generation AI, for example, compares with past sentences and evaluates improvements in logical structure and expressiveness. In this way, the user's degree of growth can be evaluated and specific areas for improvement can be suggested by comparing with past sentences.

[0038] The recommendation unit can analyze the contents of a book in detail and provide recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI analyzes the contents of a book in detail and provides recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI suggests a way to write a review that focuses on the growth of the main character. The recommendation unit can also analyze the contents of a book and provide recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI suggests a way to write a review that focuses on the climax scene. The recommendation unit can analyze the contents of a book in detail and provide recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI suggests a way to write a review that focuses on a supporting character. This can improve the quality of reviews by providing recommendations for writing styles that focus on specific scenes or characters.

[0039] The recommendation unit can suggest a writing style that will draw deep insights based on the theme and message of a book. For example, the generation AI in the recommendation unit suggests a writing style that will draw deep insights based on the theme and message of a book. For example, the generation AI suggests a writing style for a book on environmental issues. The recommendation unit also analyzes the theme and message of a book and suggests a writing style that will draw deep insights. For example, the generation AI suggests a writing style for a book on friendship. The recommendation unit also suggests a writing style that will draw deep insights based on the theme and message of a book. For example, the generation AI suggests a writing style for a book on social issues. This makes it possible to improve the quality of reviews by suggesting writing styles based on the theme and message of a book.

[0040] The recommendation unit can extract common writing patterns for books of different genres and themes and provide general-purpose recommendations. For example, the generation AI extracts common writing patterns for books of different genres and themes and provides general-purpose recommendations. For example, the generation AI proposes writing patterns common to adventure stories and romance novels. The recommendation unit can also extract common writing patterns for books of different genres and themes and provide general-purpose recommendations. For example, the generation AI proposes writing patterns common to mystery novels and fantasy novels. The recommendation unit can also extract common writing patterns for books of different genres and themes and provide general-purpose recommendations. For example, the generation AI proposes writing patterns common to historical novels and non-fiction. In this way, general-purpose recommendations can be provided by extracting common writing patterns.

[0041] The recommendation unit can visualize the contents of a book and provide recommendations for writing styles that are visually easy to understand. For example, the generation AI in the recommendation unit visualizes the contents of a book and provides recommendations for writing styles that are visually easy to understand. For example, the generation AI shows the flow of the story using diagrams or charts. The recommendation unit can also visualize the contents of a book and provide recommendations for writing styles that are visually easy to understand. For example, the generation AI shows important scenes using illustrations. The recommendation unit can also visualize the contents of a book and provide recommendations for writing styles that are visually easy to understand. For example, the generation AI provides a character relationship diagram. This visualization makes it easier for users to visually understand the contents of the book.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The book review writing support system can further include a break suggestion unit that measures the user's reading speed and suggests breaks at appropriate times. For example, the generation AI monitors the user's reading speed and suggests breaks at regular intervals. The generation AI displays a message such as, "Take a short break here to refresh yourself." The break suggestion unit can also suggest refreshing activities if it determines that the user's concentration is declining. The generation AI provides advice such as, "Try doing some stretching." This allows the user to take breaks at appropriate times and maintain concentration while writing their book review.

[0044] The book review writing support system can further include a related material provision unit that provides related books and materials based on the user's reading history. For example, the generation AI analyzes the user's reading history and recommends related books and materials. The generation AI provides advice such as, "This book is also useful as a reference for this book." In addition, if the user is interested in a particular topic, the related material provision unit can provide materials related to that topic. For example, the generation AI provides information such as, "Here are materials related to environmental issues." This allows the user to refer to related materials and write a book review with deeper insight.

[0045] The book review writing support system can further include an environment adjustment unit to optimize the user's reading environment. For example, the generation AI monitors the user's reading environment and suggests appropriate lighting and music. For example, the generation AI displays a message such as, "Would you like to change the lighting to be more suitable for reading?" The environment adjustment unit can also suggest music to improve the user's concentration. For example, the generation AI provides advice such as, "Would you like to play music to improve concentration?" This allows the user to write their book review in the optimal reading environment.

[0046] The book review writing support system can further include a progress management unit that manages the user's reading progress based on the user's reading history. For example, the generation AI analyzes the user's reading history and manages the user's reading progress. The generation AI displays a message such as, "Your current reading progress is 50%." The progress management unit can also allow the user to set goals and manage the degree of achievement of those goals. The generation AI issues a notification such as, "You have reached your target reading volume." This allows the user to efficiently write a book review while managing their reading progress.

[0047] The book review writing support system can further include a habit formation unit that provides advice for forming a reading habit based on the user's reading history. For example, the generation AI analyzes the user's reading history and provides advice for forming a reading habit. The generation AI provides advice such as, "It's a good idea to continue reading for 30 minutes every day." The habit formation unit can also set goals for the user to form a reading habit and manage the progress of those goals. The generation AI can notify the user, for example, "You have reached your target reading time." This allows the user to efficiently write a book review while forming a reading habit.

[0048] The book review writing support system can further include a motivation maintenance unit that provides advice to maintain the user's reading motivation based on the user's reading history. For example, the generation AI analyzes the user's reading history and provides advice to maintain the user's reading motivation. The generation AI provides advice such as, "Once you finish reading this book, choose the next book." The motivation maintenance unit can also set goals for the user to maintain their reading motivation and manage the progress of those goals. The generation AI can notify the user, for example, "You have reached your target reading volume." This allows the user to efficiently write a book review while maintaining their reading motivation.

[0049] The book review writing support system can further include a progress visualization unit that visualizes the user's reading progress based on their reading history. For example, the generation AI analyzes the user's reading history and visualizes their reading progress in graphs or charts. The generation AI displays a message such as, "Your current reading progress is 50%." The progress visualization unit can also allow the user to set a goal and visualize the degree to which that goal has been achieved. The generation AI notifies the user, for example, by notifying them that "You have reached your target reading volume." This allows the user to efficiently write their book review while visually understanding their reading progress.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The basic rule presentation unit presents the basic rules and writing methods for writing a book report. For example, the generation AI presents the basic rules and writing methods for writing a book report to the user. For example, the generation AI provides advice such as, "It is a good idea to write a book report in a format that first briefly introduces the outline of the book, and then states your impressions and thoughts." Step 2: The support unit supports the user in writing the review based on the basic rules and writing style presented by the basic rule presentation unit. For example, the generation AI provides real-time support when the user is writing the review. For example, the generation AI provides feedback such as, "It would be better if you wrote this part more specifically." Step 3: The correction unit corrects the impressions written by the support unit. For example, the generation AI analyzes the impressions written by the user, points out errors in grammar and expression, and suggests corrections. For example, the generation AI provides suggestions such as, "This expression is unnatural, so it would be better to write it like this." Step 4: The recommendation unit provides recommendations on writing methods that are in line with the content of the book based on the book information registered in the database. For example, the generation AI retrieves book information from the database and generates appropriate recommendations based on that information. For example, the generation AI might provide advice such as, "This book is an adventure story, so in your review, it's a good idea to focus on the protagonist's growth and the course of the adventure."

[0052] (Example 2) The book review writing support system according to the embodiment of the present invention is a system that efficiently and effectively supports the writing of book reviews, thereby improving children's academic ability and reducing the workload of parents.

[0053] A book review writing support system according to an embodiment includes a basic rule presentation unit, a support unit, a correction unit, and a recommendation unit. The basic rule presentation unit presents basic rules and writing methods for writing a book review. For example, the generation AI presents the basic rules and writing methods for writing a book review to a user. The generation AI provides advice such as, "It's a good idea to write a book review in a format where you first briefly introduce the outline of the book and then state your impressions and thoughts." The support unit supports the writing of a book review based on the basic rules and writing methods presented by the basic rule presentation unit. For example, the generation AI provides real-time support when a user writes a book review. The generation AI provides feedback such as, "It would be better to write this part more specifically." The correction unit corrects the book review created by the support unit. For example, the generation AI analyzes a book review created by a user, points out grammatical and phrasal errors, and suggests corrections. The generation AI suggests corrections such as, "This expression is unnatural, so it would be better to write it like this." The recommendation unit provides recommendations for writing styles that match the content of a book based on book information registered in the database. For example, the generation AI retrieves book information from the database and generates appropriate recommendations based on that information. For example, the generation AI provides advice such as, "This book is an adventure story, so in your review, it's a good idea to focus on the protagonist's growth and the course of the adventure." This allows the book review writing support system according to the embodiment to efficiently and effectively support the creation of book reviews. For example, children can learn basic rules and writing techniques for writing book reviews, while also cultivating the ability to organize and express their thoughts. Furthermore, parents can reduce the time spent correcting and providing guidance on book reviews.

[0054] The basic rule presentation unit can analyze the user's past essay data and present rules and writing methods optimized for the user. For example, the basic rule presentation unit uses a generation AI to collect the user's past essay data and analyze their writing tendencies and style. For example, the generation AI analyzes the structure and expression methods of past essays and suggests the optimal writing method for the user. The basic rule presentation unit also identifies specific weaknesses and areas for improvement based on the user's past essay data and presents corresponding rules and writing methods. For example, the generation AI provides advice for strengthening areas that lack specificity. The basic rule presentation unit also uses a generation AI to analyze the user's past essay data and provide templates and frameworks tailored to individual users. For example, the generation AI generates templates that reflect the user's frequently used expressions and structures. This allows the user to be provided with rules and writing methods optimized for the user, thereby improving the quality of essay creation.

[0055] The basic rule presentation unit can refer to the user's reading history and provide writing rules specialized for a specific genre or theme. For example, the generation AI analyzes the user's reading history to provide writing rules specialized for a specific genre or theme. The generation AI suggests, for example, how to write a review of a mystery novel. The basic rule presentation unit also generates writing rules that reflect the characteristics and important points of each genre based on the user's reading history. The generation AI provides, for example, how to write a review of a historical novel. The basic rule presentation unit also refers to the user's reading history and provides writing rules specialized for a specific theme. The generation AI suggests, for example, how to write a review of a book about environmental issues. In this way, by providing writing rules specialized for a specific genre or theme, it is possible to assist the user in writing their review.

[0056] The basic rule presentation unit uses the emotion estimation function to provide writing advice based on the user's emotional state, thereby eliciting positive emotions. The basic rule presentation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provides writing advice based on the results. The generation AI, for example, provides advice that makes it easy to write when the user is relaxed. The basic rule presentation unit also provides writing advice based on the user's emotional state, thereby eliciting positive emotions. The generation AI, for example, suggests a writing style that will help the user relax when the user is feeling stressed. The basic rule presentation unit also analyzes the user's emotional state using the emotion estimation function and provides writing advice that will elicit positive emotions based on the results. The generation AI, for example, provides advice that makes it easy to write when the user is in a happy mood. In this way, by providing advice based on the user's emotional state, it is possible to elicit positive emotions and support the writing of impressions.

[0057] The support unit can analyze the user's writing in real time and suggest specific improvements according to the context. For example, the support unit uses a generation AI to analyze the user's writing in real time and suggest specific improvements according to the context. For example, the generation AI points out parts of the writing that flow unnaturally and offers suggested revisions. The support unit also analyzes the user's writing and suggests specific improvements according to the context. For example, the generation AI gives advice on strengthening parts that lack specificity. The support unit also uses a generation AI to analyze the user's writing in real time and suggest specific improvements according to the context. For example, the generation AI gives advice on simplifying redundant expressions. In this way, the quality of impressions can be improved by analyzing the user's writing in real time and suggesting specific improvements.

[0058] The support unit can analyze the style and tone of the user's writing and provide advice to create a sense of unity. For example, the generation AI analyzes the style and tone of the writing written by the user and provides advice to create a sense of unity. For example, the generation AI makes suggestions to unify the writing style when it is inconsistent. The support unit also analyzes the style and tone of the writing written by the user and provides advice to create a sense of unity. For example, the generation AI gives advice to unify formal and casual writing styles. The support unit also analyzes the style and tone of the writing written by the user and provides advice to create a sense of unity. For example, the generation AI makes suggestions to make the tone of the entire writing consistent. This unifies the style and tone of the writing, thereby improving the quality of the impressions.

[0059] The support unit uses the emotion estimation function to provide feedback according to the user's emotions, thereby maintaining motivation. The support unit, for example, uses the emotion estimation function to provide feedback according to the user's emotions, thereby maintaining motivation. The generation AI, for example, sends an encouraging message when the user is tired. The support unit also analyzes the user's emotions in real time and provides feedback based on the results. The generation AI, for example, gives specific advice when the user is concentrating. The support unit also uses the emotion estimation function to provide feedback according to the user's emotions, thereby maintaining motivation. The generation AI, for example, sends a compliment when the user is feeling positive. In this way, by providing feedback according to the user's emotions, motivation can be maintained and the user's review writing can be supported.

[0060] The support unit can accept the user's writing as voice input and provide feedback in real time using voice recognition technology. For example, the support unit uses a generation AI to accept the user's written writing as voice input and provide feedback in real time using voice recognition technology. For example, the generation AI converts what the user has said into text and suggests specific areas for improvement. In addition, when the user provides voice input, the generation AI provides feedback in real time. For example, the generation AI points out errors in grammar or expression using voice recognition technology. In addition, the support unit uses a generation AI to accept voice input and provide feedback in real time using voice recognition technology. For example, the generation AI analyzes what the user has said and suggests specific areas for improvement. This allows users to create their impressions more easily by using voice input.

[0061] The support unit can visualize the user's writing and provide visually easy-to-understand feedback. For example, the generation AI in the support unit visualizes the writing written by the user and provides visually easy-to-understand feedback. For example, the generation AI shows the structure of the writing in a diagram or chart. The support unit also visualizes the writing written by the user and provides visually easy-to-understand feedback. For example, the generation AI highlights important points. The support unit also visualizes the writing written by the user and provides visually easy-to-understand feedback. For example, the generation AI shows the flow of the writing in a flowchart. This visualization makes it easier for the user to visually understand the structure of the writing and areas for improvement.

[0062] The support unit can use the emotion estimation function to analyze the user's emotional response to the text written by the user and provide specific advice based on the emotion. For example, the support unit uses the emotion estimation function to analyze the user's emotional response to the text written by the user and provides specific advice based on the results. The generation AI, for example, provides advice to elicit positive emotions. The support unit also analyzes the user's emotional response in real time and provides specific advice based on the results. The generation AI, for example, provides advice to alleviate negative emotions. The support unit also uses the emotion estimation function to analyze the user's emotional response to the text written by the user and provides specific advice based on the results. For example, the generation AI provides advice that makes it easier to write when the user is in a happy mood. In this way, by analyzing the emotional response, more appropriate advice can be provided to the user.

[0063] The correction unit can point out not only grammatical and phrasing errors, but also logical structure and persuasive expressions, and propose corrections. For example, the generation AI analyzes a user's review and points out not only grammatical and phrasing errors, but also logical structure and persuasive expressions. For example, the generation AI points out logical leaps and proposes corrections. The correction unit also analyzes a user's review and not only points out grammatical and phrasing errors, but also suggests areas for improvement in logical structure. For example, the generation AI provides specific correction suggestions to make the review more persuasive. The correction unit also analyzes a user's review and points out not only grammatical and phrasing errors, but also logical structure and persuasive expressions, and proposes corrections. For example, the generation AI provides advice on strengthening the logical flow. This improves the quality of the review by pointing out not only grammatical and phrasing errors, but also logical structure and persuasive expressions.

[0064] The correction unit can compare the user's past impressions with the current one, evaluate the degree of growth, and suggest specific areas for improvement. In the correction unit, for example, the generation AI compares the user's past impressions with the current one and evaluates the degree of growth. The generation AI, for example, compares with the past sentences and suggests specific areas for improvement. The correction unit also analyzes the user's past impressions with the current one and evaluates the degree of growth. The generation AI, for example, evaluates whether past weaknesses have been improved and provides specific feedback. In the correction unit, the generation AI compares the user's past impressions with the current one, evaluates the degree of growth, and suggests specific areas for improvement. The generation AI, for example, compares with past sentences and evaluates improvements in logical structure and expressiveness. In this way, the user's degree of growth can be evaluated and specific areas for improvement can be suggested by comparing with past sentences.

[0065] The correction unit can use the emotion estimation function to make corrections that take into consideration the user's emotions and provide positive feedback. The correction unit, for example, uses the emotion estimation function to make corrections that take into consideration the user's emotions and provide positive feedback. The generation AI, for example, sends an encouraging message when the user is feeling down. The correction unit also analyzes the user's emotions in real time and makes corrections based on the results. The generation AI, for example, sends a compliment when the user is feeling positive. The correction unit also uses the emotion estimation function to make corrections that take into consideration the user's emotions and provide positive feedback. The generation AI, for example, gives advice on how to relax when the user is feeling stressed. In this way, by making corrections that take into consideration the user's emotions, positive feedback can be provided and the user's motivation to write their review can be maintained.

[0066] The recommendation unit can analyze the contents of a book in detail and provide recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI analyzes the contents of a book in detail and provides recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI suggests a way to write a review that focuses on the growth of the main character. The recommendation unit can also analyze the contents of a book and provide recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI suggests a way to write a review that focuses on the climax scene. The recommendation unit can analyze the contents of a book in detail and provide recommendations for writing styles that focus on specific scenes or characters. For example, the generation AI suggests a way to write a review that focuses on a supporting character. This can improve the quality of reviews by providing recommendations for writing styles that focus on specific scenes or characters.

[0067] The recommendation unit can suggest a writing style that will draw deep insights based on the theme and message of a book. For example, the generation AI in the recommendation unit suggests a writing style that will draw deep insights based on the theme and message of a book. For example, the generation AI suggests a writing style for a book on environmental issues. The recommendation unit also analyzes the theme and message of a book and suggests a writing style that will draw deep insights. For example, the generation AI suggests a writing style for a book on friendship. The recommendation unit also suggests a writing style that will draw deep insights based on the theme and message of a book. For example, the generation AI suggests a writing style for a book on social issues. This makes it possible to improve the quality of reviews by suggesting writing styles based on the theme and message of a book.

[0068] The recommendation unit can use the emotion estimation function to analyze the user's emotional reaction to the book's contents and provide writing style recommendations based on the emotions. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the book's contents and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on moving scenes. The recommendation unit also analyzes the user's emotional reaction in real time and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on exciting scenes. The recommendation unit also uses the emotion estimation function to analyze the user's emotional reaction to the book's contents and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on sad scenes. In this way, by analyzing emotional reactions, it is possible to provide more appropriate writing style recommendations to the user.

[0069] The recommendation unit can extract common writing patterns for books of different genres and themes and provide general-purpose recommendations. For example, the generation AI extracts common writing patterns for books of different genres and themes and provides general-purpose recommendations. For example, the generation AI proposes writing patterns common to adventure stories and romance novels. The recommendation unit can also extract common writing patterns for books of different genres and themes and provide general-purpose recommendations. For example, the generation AI proposes writing patterns common to mystery novels and fantasy novels. The recommendation unit can also extract common writing patterns for books of different genres and themes and provide general-purpose recommendations. For example, the generation AI proposes writing patterns common to historical novels and non-fiction. In this way, general-purpose recommendations can be provided by extracting common writing patterns.

[0070] The recommendation unit can visualize the contents of a book and provide recommendations for writing styles that are visually easy to understand. For example, the generation AI in the recommendation unit visualizes the contents of a book and provides recommendations for writing styles that are visually easy to understand. For example, the generation AI shows the flow of the story using diagrams or charts. The recommendation unit can also visualize the contents of a book and provide recommendations for writing styles that are visually easy to understand. For example, the generation AI shows important scenes using illustrations. The recommendation unit can also visualize the contents of a book and provide recommendations for writing styles that are visually easy to understand. For example, the generation AI provides a character relationship diagram. This visualization makes it easier for users to visually understand the contents of the book.

[0071] The recommendation unit can use the emotion estimation function to monitor the user's emotions about the book's contents in real time and provide writing style recommendations based on the emotions. The recommendation unit, for example, uses the emotion estimation function to monitor the user's emotions about the book's contents in real time and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on moving scenes. The recommendation unit also monitors the user's emotions in real time and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on exciting scenes. The recommendation unit also uses the emotion estimation function to monitor the user's emotions about the book's contents in real time and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on sad scenes. In this way, by monitoring emotions in real time, it is possible to provide more appropriate writing style recommendations to the user.

[0072] The recommendation unit can use the emotion estimation function to monitor the user's emotions about the book's contents in real time and provide writing style recommendations based on the emotions. The recommendation unit, for example, uses the emotion estimation function to monitor the user's emotions about the book's contents in real time and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on moving scenes. The recommendation unit also monitors the user's emotions in real time and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on exciting scenes. The recommendation unit also uses the emotion estimation function to monitor the user's emotions about the book's contents in real time and provides writing style recommendations based on the results. The generation AI, for example, suggests a way to write a review that focuses on sad scenes. In this way, by monitoring emotions in real time, it is possible to provide more appropriate writing style recommendations to the user.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The book review writing support system can further include a break suggestion unit that measures the user's reading speed and suggests breaks at appropriate times. For example, the generation AI monitors the user's reading speed and suggests breaks at regular intervals. The generation AI displays a message such as, "Take a short break here to refresh yourself." The break suggestion unit can also suggest refreshing activities if it determines that the user's concentration is declining. The generation AI provides advice such as, "Try doing some stretching." This allows the user to take breaks at appropriate times and maintain concentration while writing their book review.

[0075] The book review writing support system can further include a related material provision unit that provides related books and materials based on the user's reading history. For example, the generation AI analyzes the user's reading history and recommends related books and materials. The generation AI provides advice such as, "This book is also useful as a reference for this book." In addition, if the user is interested in a particular topic, the related material provision unit can provide materials related to that topic. For example, the generation AI provides information such as, "Here are materials related to environmental issues." This allows the user to refer to related materials and write a book review with deeper insight.

[0076] The book review writing support system can further include an environment adjustment unit to optimize the user's reading environment. For example, the generation AI monitors the user's reading environment and suggests appropriate lighting and music. For example, the generation AI displays a message such as, "Would you like to change the lighting to be more suitable for reading?" The environment adjustment unit can also suggest music to improve the user's concentration. For example, the generation AI provides advice such as, "Would you like to play music to improve concentration?" This allows the user to write their book review in the optimal reading environment.

[0077] The book review writing support system can further include an emotional experience providing unit that estimates the user's emotions and provides a reading experience based on those emotions. For example, the generation AI analyzes the user's emotions in real time and customizes the reading experience based on the results. For example, the generation AI suggests books with relaxing content when the user is feeling relaxed. The emotional experience providing unit can also provide a reading experience that corresponds to the user's emotions and elicit positive emotions. For example, the generation AI suggests books that will help the user relax when they are feeling stressed. In this way, by providing a reading experience that corresponds to the user's emotions, it is possible to elicit positive emotions and support the writing of a book review.

[0078] The book review writing support system can further include an emotion feedback unit that estimates the user's emotions and provides feedback based on those emotions. For example, the generation AI analyzes the user's emotions in real time and provides feedback based on the results. For example, the generation AI sends a compliment when the user has positive emotions. The emotion feedback unit can also provide feedback according to the user's emotions to maintain motivation. For example, the generation AI sends an encouraging message when the user is tired. In this way, by providing feedback according to the user's emotions, motivation can be maintained and the book review writing can be supported.

[0079] The book review writing support system can further include an emotion advice unit that estimates the user's emotions and provides writing advice based on those emotions. For example, the generation AI analyzes the user's emotions in real time and provides writing advice based on the results. For example, the generation AI provides advice that makes it easier to write when the user is in a happy mood. The emotion advice unit can also provide writing advice based on the user's emotions and elicit positive emotions. For example, the generation AI can suggest a writing style that will help the user relax when they are feeling stressed. In this way, by providing writing advice based on the user's emotions, it is possible to elicit positive emotions and support the writing of the book review.

[0080] The book review writing support system can further include a progress management unit that manages the user's reading progress based on the user's reading history. For example, the generation AI analyzes the user's reading history and manages the user's reading progress. The generation AI displays a message such as, "Your current reading progress is 50%." The progress management unit can also allow the user to set goals and manage the degree of achievement of those goals. The generation AI issues a notification such as, "You have reached your target reading volume." This allows the user to efficiently write a book review while managing their reading progress.

[0081] The book review writing support system can further include a habit formation unit that provides advice for forming a reading habit based on the user's reading history. For example, the generation AI analyzes the user's reading history and provides advice for forming a reading habit. The generation AI provides advice such as, "It's a good idea to continue reading for 30 minutes every day." The habit formation unit can also set goals for the user to form a reading habit and manage the progress of those goals. The generation AI can notify the user, for example, "You have reached your target reading time." This allows the user to efficiently write a book review while forming a reading habit.

[0082] The book review writing support system can further include a motivation maintenance unit that provides advice to maintain the user's reading motivation based on the user's reading history. For example, the generation AI analyzes the user's reading history and provides advice to maintain the user's reading motivation. The generation AI provides advice such as, "Once you finish reading this book, choose the next book." The motivation maintenance unit can also set goals for the user to maintain their reading motivation and manage the progress of those goals. The generation AI can notify the user, for example, "You have reached your target reading volume." This allows the user to efficiently write a book review while maintaining their reading motivation.

[0083] The book review writing support system can further include a progress visualization unit that visualizes the user's reading progress based on their reading history. For example, the generation AI analyzes the user's reading history and visualizes their reading progress in graphs or charts. The generation AI displays a message such as, "Your current reading progress is 50%." The progress visualization unit can also allow the user to set a goal and visualize the degree to which that goal has been achieved. The generation AI notifies the user, for example, by notifying them that "You have reached your target reading volume." This allows the user to efficiently write their book review while visually understanding their reading progress.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The basic rule presentation unit presents the basic rules and writing methods for writing a book report. For example, the generation AI presents the basic rules and writing methods for writing a book report to the user. For example, the generation AI provides advice such as, "It is a good idea to write a book report in a format that first briefly introduces the outline of the book, and then states your impressions and thoughts." Step 2: The support unit supports the user in writing the review based on the basic rules and writing style presented by the basic rule presentation unit. For example, the generation AI provides real-time support when the user is writing the review. For example, the generation AI provides feedback such as, "It would be better if you wrote this part more specifically." Step 3: The correction unit corrects the impressions written by the support unit. For example, the generation AI analyzes the impressions written by the user, points out errors in grammar and expression, and suggests corrections. For example, the generation AI provides suggestions such as, "This expression is unnatural, so it would be better to write it like this." Step 4: The recommendation unit provides recommendations on writing methods that are in line with the content of the book based on the book information registered in the database. For example, the generation AI retrieves book information from the database and generates appropriate recommendations based on that information. For example, the generation AI might provide advice such as, "This book is an adventure story, so in your review, it's a good idea to focus on the protagonist's growth and the course of the adventure."

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] 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]

[0153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A section that explains the basic rules and writing methods for writing a book review, a support unit that supports writing of impressions based on the basic rules and writing style presented by the basic rule presentation unit; a correction unit that corrects the impressions created by the support unit; A recommendation unit that provides recommendations on writing styles that are in line with the contents of books based on information about the books registered in the database. A system characterized by:

2. The basic rule presentation unit Analyzing the user's past impression data and presenting the user with optimized rules and writing methods 2. The system of claim 1.

3. The basic rule presentation unit Refer to the user's reading history and provide writing rules tailored to specific genres or themes 2. The system of claim 1.

4. The basic rule presentation unit Providing advice on how to write according to the user's emotional state, eliciting positive emotions 2. The system of claim 1.

5. The support unit Analyzes user text in real time and suggests specific improvements based on the context 2. The system of claim 1.

6. The support unit Analyze your writing style and tone and provide advice on how to achieve consistency.

2. The system of claim 1.

Images