System

Abstract

An object of the system according to the embodiment is to allow a user to correct a self-made sentence or picture in accordance with a style of a specific writer.SOLUTION: A system includes a writer data learning unit, a user work input unit, a style correction unit, and a corrected work output unit. The writer data learning unit learns writer data. The user work input unit inputs a text or picture created by the user. A style correction part corrects the user's own composition or picture inputted by the user work input part on the basis of the style of the writer learned by the writer data learning part. The modified work output section outputs the modified work.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to modify their own text or images to match the style of a specific author.

[0005] The system according to the embodiment aims to allow users to modify their own writings and drawings to match the style of a particular author. [Means for solving the problem]

[0006] The system according to the embodiment includes an author data learning unit, a user work input unit, a style correction unit, and a corrected work output unit. The author data learning unit learns author data. The user work input unit inputs a user's own writing or drawing. The style correction unit corrects the user's own writing or drawing input by the user work input unit based on the author's style learned by the author data learning unit. The corrected work output unit outputs the corrected work. [Effects of the Invention]

[0007] The system according to the embodiment allows users to modify their own writing and drawings to match the style of a particular author. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The revision system according to the embodiment of the present invention is a system that automatically revise the user's own writings and drawings to match the style of a favorite author. In this way, the revision system can revise the user's work to match the style of the favorite author.

[0029] The modification system according to the embodiment includes an author data learning unit, a user work input unit, a style modification unit, and a modified work output unit. The author data learning unit learns author data. For example, it collects text data, such as novels, essays, and poems, as well as paintings and illustrations, and inputs the data into the generation AI. The generation AI analyzes this data and learns the author's writing style, structure, brushstrokes, and style. The user work input unit inputs the user's own writing or drawings. For example, the user provides the generation AI with parts of novels, poems, essays, or drawings or illustrations they have drawn. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do. The style modification unit modifies the user's own writing or drawings input by the user work input unit based on the author's style learned by the author data learning unit. For example, it rewrites the user's writing to match the author's writing style and structure, or modifies the user's drawing to match the author's brushstrokes and style. The generation AI makes appropriate changes to bring the user's work closer to the author's style. The revised work output unit outputs the revised work. For example, it outputs the rewritten text or revised picture as a text file or an image file. In this way, the revision system according to the embodiment can revise the user's work to match the style of a favorite author. For example, if a novel written by a user is rewritten in the style of a favorite author, it will become a more appealing work. Also, if a picture drawn by a user is revised in the style of a favorite author, it will have a more professional finish.

[0030] The author data learning unit can learn not only the author's data but also the data of other authors who influenced that author, thereby increasing the diversity of styles. For example, in addition to the author's data, the author data learning unit can also collect data on the works of other authors who influenced that author, and have the generative AI learn from this. For example, by simultaneously learning the works of authors B and C who influenced author A, the style of author A can be made more diverse. This makes it possible to increase the diversity of authors' styles.

[0031] The author data learning unit extracts specific themes or motifs contained in an author's work, and the generation AI can then make modifications based on the theme. The author data learning unit, for example, extracts specific themes or motifs contained in an author's work, and the generation AI can then modify the user's work based on those themes. For example, the unit can extract themes such as "love" and "loneliness" that are often found in author A's work and reflect them in the user's work. This allows modifications to be made based on the author's specific themes or motifs.

[0032] The author data learning unit learns data on contemporary culture or historical background in addition to author data, enabling a deeper understanding of the context. For example, the author data learning unit learns data on contemporary culture or historical background in addition to author data. For example, by learning the social situation and cultural background of the era in which author A was active, a deeper understanding of the context of author A's works is achieved. This allows a deeper understanding of the context of the author's works.

[0033] The author data learning unit can also incorporate audio data or video data when learning author data, enabling multimodal learning. For example, when learning author data, the author data learning unit can also incorporate audio data and video data. For example, by learning from audio recordings of author A's readings and video interviews, a deep understanding of the context of author A's work can be achieved. This allows for multifaceted learning of author data.

[0034] The user work input unit allows the generation AI to provide real-time feedback on the text or images entered by the user and suggest directions for correction. The user work input unit allows the generation AI to provide real-time feedback on the text or images entered by the user and suggest directions for correction. For example, the generation AI may suggest areas for improvement in grammar or structure for text written by the user in real time. This allows the user to receive feedback in real time as they enter text.

[0035] The user work input unit also takes in data on the user's past works and can adapt to the author's style while preserving the user's individuality. The user work input unit, for example, takes in data on the user's past works and adapts to the author's style while preserving the user's individuality. For example, it learns novels and poems written by the user in the past and adapts to the style of author A while maintaining the user's writing style. This makes it possible to adapt to the author's style while preserving the user's individuality.

[0036] The user work input unit also supports voice input or handwriting input when the user inputs, thereby providing a more intuitive interface. The user work input unit, for example, supports voice input when the user inputs, thereby providing a more intuitive interface. For example, what the user speaks is converted into text in real time and input to the generation AI. This makes it possible to provide an interface that allows the user to input more intuitively.

[0037] The user work input unit allows the generation AI to automatically suggest works by related authors for the work entered by the user, and use them as a reference for revisions. The user work input unit allows the generation AI to automatically suggest works by related authors for the work entered by the user. For example, for part of a novel written by the user, similar works by author A can be presented and used as a reference for revisions. This makes it possible to suggest works by related authors for the work entered by the user.

[0038] The style correction unit can have the generation AI present multiple revision suggestions and allow the user to select from them when making revisions to match the writer's style. For example, when making revisions to match the writer's style, the generation AI can present multiple revision suggestions and allow the user to select from them. For example, the style correction unit can present multiple rewrite suggestions based on the style of writer A for a piece of text written by the user. This allows the user to select from multiple revision suggestions.

[0039] The style correction unit allows the generation AI to explain the reasons and background for the corrections to the user during the correction process, thereby improving the learning effect. For example, the style correction unit allows the generation AI to explain the reasons and background for the corrections to the user during the correction process, thereby improving the learning effect. For example, the unit explains the reasons for the corrections to match the writing style of writer A for a piece of text written by the user. This allows the user to understand the reasons and background for the corrections, thereby improving the learning effect.

[0040] The style correction unit can propose hybrid revisions that combine the styles of different authors when making revisions that match the author's style. For example, the style correction unit can propose hybrid revisions that combine the styles of different authors when making revisions that match the author's style. For example, the style correction unit can present a rewrite proposal that combines the writing styles of author A and author B. This makes it possible to propose revisions that combine the styles of different authors.

[0041] The style correction unit allows the generation AI to incorporate user feedback during the correction process and continuously improve the correction algorithm. For example, the style correction unit allows the generation AI to incorporate user feedback during the correction process and continuously improve the correction algorithm. For example, the algorithm is adjusted based on comments made by the user on the correction proposal. This allows the generation AI to incorporate user feedback and continuously improve the correction algorithm.

[0042] The revised work output unit allows the generation AI to automatically output the revised work in multiple formats (PDF, ePub, image file). For example, when outputting a revised work, the revised work output unit allows the generation AI to automatically output the revised work in multiple formats. For example, the rewritten text may be output in PDF or ePub format. This allows the revised work to be output in multiple formats.

[0043] The revised work output unit allows the generation AI to automatically proofread the output work and correct typos or grammatical errors. The revised work output unit allows the generation AI to automatically proofread the output work and correct typos or grammatical errors, for example. For example, it detects and corrects typos in rewritten sentences. This makes it possible to automatically correct typos and grammatical errors in the output work.

[0044] When the revised work output unit outputs the revised work, the generation AI can automatically add relevant metadata (author name, genre, theme). When the revised work output unit outputs the revised work, the generation AI can automatically add relevant metadata. For example, the author name, genre, and theme are automatically added to the rewritten text. This makes it possible to automatically add metadata related to the revised work.

[0045] The revised work output unit allows the generation AI to automatically upload the output work to cloud storage or social media, making it easy to share. The revised work output unit allows the generation AI to automatically upload the output work to cloud storage or social media, making it easy to share. For example, the rewritten text can be automatically uploaded to Google Drive or Dropbox. This allows the output work to be automatically uploaded to cloud storage or social media, making it easy to share.

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

[0047] The revision system can further analyze the user's past work data and make revisions to suit the author's style while preserving the user's individuality. For example, the system can learn from the novels and poems the user has written in the past and adapt the work to the style of author A while maintaining the user's writing style. This allows the system to adapt to the author's style while preserving the user's individuality. It can also provide feedback based on the user's past work data to encourage the user's growth. For example, the system can compare the user's past works with their current work and show the user's progress, thereby increasing the user's motivation.

[0048] The correction system also supports voice input or handwriting input when users input text, providing a more intuitive interface. For example, what the user speaks can be converted into text in real time and input to the generation AI. This allows for a more intuitive interface for users to input text. Supporting handwriting input also allows users to freely draw pictures and letters, which can then be input directly to the generation AI. This maximizes the user's creativity.

[0049] Furthermore, the revision system can automatically suggest works by related authors for the work entered by the user using the generation AI, which can be used as a reference for revisions. For example, for part of a novel written by a user, similar works by author A can be presented and used as a reference for revisions. This allows works by related authors to be suggested for the work entered by the user. Furthermore, by suggesting works by other authors in addition to the works of the author selected by the user, the system can broaden the user's perspective.

[0050] Furthermore, the revision system can enhance learning effectiveness by having the generation AI explain the reasons or background for the revisions to the user during the revision process. For example, the system can explain the reasons for revisions to a piece of writing written by the user to match the writing style of author A. This allows the user to understand the reasons and background for the revisions and enhances learning effectiveness. The generation AI can also be equipped with a function to answer questions the user has during the revision process in real time. This allows the user to gain a deeper understanding of the revision process.

[0051] Furthermore, when outputting the revised work, the generation AI can automatically output it in multiple formats (PDF, ePub, image file). For example, the rewritten text can be output in PDF and ePub format. This allows the revised work to be output in multiple formats. It can also automatically apply the optimal layout and design depending on the format selected by the user. This allows the work to be output in the format desired by the user.

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

[0053] Step 1: The author data learning unit learns author data. For example, it collects text data such as novels, essays, and poems by the author, as well as data on paintings and illustrations, and inputs this data into the generation AI. The generation AI analyzes this data and learns the author's writing style, structure, brushstrokes, and style. Step 2: The user's work input section inputs the user's own writing or drawing. For example, the user provides the AI ​​with a portion of a novel, poem, or essay they have written, or a drawing or illustration they have drawn. The input to the AI ​​is a prompt containing instructions on what the user wants the AI ​​to do. Step 3: The style correction unit corrects the user's original text or illustration entered by the user work input unit based on the author's style learned by the author data learning unit. For example, it rewrites the user's written text to match the author's style and structure, or corrects the user's drawn illustration to match the author's brushstrokes and style. The generation AI makes appropriate changes to bring the user's work closer to the author's style. Step 4: The revised work output unit outputs the revised work, for example, the rewritten text or the revised picture as a text file or an image file.

[0054] (Example 2) The revision system according to the embodiment of the present invention is a system that automatically revise the user's own writings and drawings to match the style of a favorite author. In this way, the revision system can revise the user's work to match the style of the favorite author.

[0055] The modification system according to the embodiment includes an author data learning unit, a user work input unit, a style modification unit, and a modified work output unit. The author data learning unit learns author data. For example, it collects text data, such as novels, essays, and poems, as well as paintings and illustrations, and inputs the data into the generation AI. The generation AI analyzes this data and learns the author's writing style, structure, brushstrokes, and style. The user work input unit inputs the user's own writing or drawings. For example, the user provides the generation AI with parts of novels, poems, essays, or drawings or illustrations they have drawn. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do. The style modification unit modifies the user's own writing or drawings input by the user work input unit based on the author's style learned by the author data learning unit. For example, it rewrites the user's writing to match the author's writing style and structure, or modifies the user's drawing to match the author's brushstrokes and style. The generation AI makes appropriate changes to bring the user's work closer to the author's style. The revised work output unit outputs the revised work. For example, it outputs the rewritten text or revised picture as a text file or an image file. In this way, the revision system according to the embodiment can revise the user's work to match the style of a favorite author. For example, if a novel written by a user is rewritten in the style of a favorite author, it will become a more appealing work. Also, if a picture drawn by a user is revised in the style of a favorite author, it will have a more professional finish.

[0056] The author data learning unit can learn not only the author's data but also the data of other authors who influenced that author, thereby increasing the diversity of styles. For example, in addition to the author's data, the author data learning unit can also collect data on the works of other authors who influenced that author, and have the generative AI learn from this. For example, by simultaneously learning the works of authors B and C who influenced author A, the style of author A can be made more diverse. This makes it possible to increase the diversity of authors' styles.

[0057] The author data learning unit extracts specific themes or motifs contained in an author's work, and the generation AI can then make modifications based on the theme. The author data learning unit, for example, extracts specific themes or motifs contained in an author's work, and the generation AI can then modify the user's work based on those themes. For example, the unit can extract themes such as "love" and "loneliness" that are often found in author A's work and reflect them in the user's work. This allows modifications to be made based on the author's specific themes or motifs.

[0058] The author data learning unit uses the emotion estimation function to learn the emotional patterns contained in the author's works and reflect similar emotions in the user's works. The author data learning unit, for example, uses the emotion estimation function to learn the emotional patterns contained in the author's works and reflect similar emotions in the user's works. For example, emotions such as "joy" and "sadness" that are often seen in the works of author A are extracted and reflected in the user's works. This allows the author's emotional patterns to be reflected in the user's works.

[0059] The author data learning unit learns data on contemporary culture or historical background in addition to author data, enabling a deeper understanding of the context. For example, the author data learning unit learns data on contemporary culture or historical background in addition to author data. For example, by learning the social situation and cultural background of the era in which author A was active, a deeper understanding of the context of author A's works is achieved. This allows a deeper understanding of the context of the author's works.

[0060] The author data learning unit can also incorporate audio data or video data when learning author data, enabling multimodal learning. For example, when learning author data, the author data learning unit can also incorporate audio data and video data. For example, by learning from audio recordings of author A's readings and video interviews, a deep understanding of the context of author A's work can be achieved. This allows for multifaceted learning of author data.

[0061] The author data learning unit can use the emotion estimation function to learn readers' emotional reactions to the author's works and add elements that elicit similar emotions to the user's works. The author data learning unit can, for example, use the emotion estimation function to learn readers' emotional reactions to the author's works and add elements that elicit similar emotions to the user's works. For example, it can learn readers' emotions such as "movement" and "empathy" toward author A's works and reflect them in the user's works. This allows readers' emotional reactions to be learned and reflected in the user's works.

[0062] The user work input unit allows the generation AI to provide real-time feedback on the text or images entered by the user and suggest directions for correction. The user work input unit allows the generation AI to provide real-time feedback on the text or images entered by the user and suggest directions for correction. For example, the generation AI may suggest areas for improvement in grammar or structure for text written by the user in real time. This allows the user to receive feedback in real time as they enter text.

[0063] The user work input unit also takes in data on the user's past works and can adapt to the author's style while preserving the user's individuality. The user work input unit, for example, takes in data on the user's past works and adapts to the author's style while preserving the user's individuality. For example, it learns novels and poems written by the user in the past and adapts to the style of author A while maintaining the user's writing style. This makes it possible to adapt to the author's style while preserving the user's individuality.

[0064] The user work input unit can use the emotion estimation function to analyze the emotions imparted to the user's work and modify it to fit the author's style while maintaining the emotions. The user work input unit can, for example, use the emotion estimation function to analyze the emotions imparted to the user's work and modify it to fit the author's style while maintaining the emotions. For example, it can analyze emotions such as "joy" and "sadness" imparted to a piece of writing written by the user and modify it to fit the writing style of author A. This allows the user's work to be modified to fit the author's style while maintaining the emotions imparted to the work.

[0065] The user work input unit also supports voice input or handwriting input when the user inputs, thereby providing a more intuitive interface. The user work input unit, for example, supports voice input when the user inputs, thereby providing a more intuitive interface. For example, what the user speaks is converted into text in real time and input to the generation AI. This makes it possible to provide an interface that allows the user to input more intuitively.

[0066] The user work input unit allows the generation AI to automatically suggest works by related authors for the work entered by the user, and use them as a reference for revisions. The user work input unit allows the generation AI to automatically suggest works by related authors for the work entered by the user. For example, for part of a novel written by the user, similar works by author A can be presented and used as a reference for revisions. This makes it possible to suggest works by related authors for the work entered by the user.

[0067] The user work input unit can use the emotion estimation function to analyze the emotional state of the user when inputting in real time and make suggestions to elicit positive emotions. The user work input unit can, for example, use the emotion estimation function to analyze the emotional state of the user when inputting in real time and make suggestions to elicit positive emotions. For example, if a negative emotion is detected while the user is inputting, an encouraging message is displayed. This makes it possible to make suggestions to elicit positive emotions when the user is inputting.

[0068] The style correction unit can have the generation AI present multiple revision suggestions and allow the user to select from them when making revisions to match the writer's style. For example, when making revisions to match the writer's style, the generation AI can present multiple revision suggestions and allow the user to select from them. For example, the style correction unit can present multiple rewrite suggestions based on the style of writer A for a piece of text written by the user. This allows the user to select from multiple revision suggestions.

[0069] The style correction unit allows the generation AI to explain the reasons and background for the corrections to the user during the correction process, thereby improving the learning effect. For example, the style correction unit allows the generation AI to explain the reasons and background for the corrections to the user during the correction process, thereby improving the learning effect. For example, the unit explains the reasons for the corrections to match the writing style of writer A for a piece of text written by the user. This allows the user to understand the reasons and background for the corrections, thereby improving the learning effect.

[0070] The style correction unit can use the emotion estimation function to check whether the post-correction work accurately reflects the emotion intended by the user. The style correction unit, for example, uses the emotion estimation function to check whether the post-correction work accurately reflects the emotion intended by the user. For example, it checks whether emotions such as "joy" or "sadness" implied in the text written by the user are maintained after correction. This makes it possible to check whether the post-correction work accurately reflects the emotion intended by the user.

[0071] The style correction unit can propose hybrid revisions that combine the styles of different authors when making revisions that match the author's style. For example, the style correction unit can propose hybrid revisions that combine the styles of different authors when making revisions that match the author's style. For example, the style correction unit can present a rewrite proposal that combines the writing styles of author A and author B. This makes it possible to propose revisions that combine the styles of different authors.

[0072] The style correction unit allows the generation AI to incorporate user feedback during the correction process and continuously improve the correction algorithm. For example, the style correction unit allows the generation AI to incorporate user feedback during the correction process and continuously improve the correction algorithm. For example, the algorithm is adjusted based on comments made by the user on the correction proposal. This allows the generation AI to incorporate user feedback and continuously improve the correction algorithm.

[0073] The style correction unit can use the emotion estimation function to predict the reader's emotional response to the revised work and make optimal corrections. The style correction unit can, for example, use the emotion estimation function to predict the reader's emotional response to the revised work and make optimal corrections. For example, it can make corrections that make the reader feel "moved" or "empathetic." This makes it possible to predict the reader's emotional response to the revised work and make optimal corrections.

[0074] The revised work output unit allows the generation AI to automatically output the revised work in multiple formats (PDF, ePub, image file). For example, when outputting a revised work, the revised work output unit allows the generation AI to automatically output the revised work in multiple formats. For example, the rewritten text may be output in PDF or ePub format. This allows the revised work to be output in multiple formats.

[0075] The revised work output unit allows the generation AI to automatically proofread the output work and correct typos or grammatical errors. The revised work output unit allows the generation AI to automatically proofread the output work and correct typos or grammatical errors, for example. For example, it detects and corrects typos in rewritten sentences. This makes it possible to automatically correct typos and grammatical errors in the output work.

[0076] The revised work output unit can use the emotion estimation function to evaluate whether the revised work accurately conveys the emotions expected by the user. The revised work output unit, for example, uses the emotion estimation function to evaluate whether the revised work accurately conveys the emotions expected by the user. For example, it evaluates whether the rewritten text accurately conveys emotions such as "joy" or "sadness." This makes it possible to evaluate whether the revised work accurately conveys the emotions expected by the user.

[0077] When the revised work output unit outputs the revised work, the generation AI can automatically add relevant metadata (author name, genre, theme). When the revised work output unit outputs the revised work, the generation AI can automatically add relevant metadata. For example, the author name, genre, and theme are automatically added to the rewritten text. This makes it possible to automatically add metadata related to the revised work.

[0078] The revised work output unit allows the generation AI to automatically upload the output work to cloud storage or social media, making it easy to share. The revised work output unit allows the generation AI to automatically upload the output work to cloud storage or social media, making it easy to share. For example, the rewritten text can be automatically uploaded to Google Drive or Dropbox. This allows the output work to be automatically uploaded to cloud storage or social media, making it easy to share.

[0079] The revised work output unit can use the emotion estimation function to monitor readers' emotional reactions to the revised work in real time and collect feedback. The revised work output unit can, for example, use the emotion estimation function to monitor readers' emotional reactions to the revised work in real time and collect feedback. For example, it can monitor readers' emotional reactions to the rewritten text, such as "emotion" or "empathy." This makes it possible to monitor readers' emotional reactions to the revised work in real time and collect feedback.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The revision system can further analyze the user's past work data and make revisions to suit the author's style while preserving the user's individuality. For example, the system can learn from the novels and poems the user has written in the past and adapt the work to the style of author A while maintaining the user's writing style. This allows the system to adapt to the author's style while preserving the user's individuality. It can also provide feedback based on the user's past work data to encourage the user's growth. For example, the system can compare the user's past works with their current work and show the user's progress, thereby increasing the user's motivation.

[0082] The revision system can further use its emotion estimation function to analyze the emotions conveyed in the user's work and revise it to fit the author's style while maintaining those emotions. For example, the system can analyze emotions such as "joy" or "sadness" conveyed in the user's writing and revise it to fit the style of author A. This allows the user's work to be revised to fit the author's style while maintaining the emotions conveyed in the user's work. It can also present multiple revision suggestions that reflect the user's emotions and allow the user to choose from them. For example, the system can present multiple rewrite suggestions that reflect different emotions for the user's writing.

[0083] The correction system also supports voice input or handwriting input when users input text, providing a more intuitive interface. For example, what the user speaks can be converted into text in real time and input to the generation AI. This allows for a more intuitive interface for users to input text. Supporting handwriting input also allows users to freely draw pictures and letters, which can then be input directly to the generation AI. This maximizes the user's creativity.

[0084] The correction system also uses emotion estimation functionality to analyze the user's emotional state in real time as they type, and can make suggestions to elicit positive emotions. For example, if the system detects negative emotions while the user is typing, it can display an encouraging message. This allows the system to make suggestions to elicit positive emotions as the user types. It can also improve the user's mood by displaying relaxing music or landscape images according to the user's emotional state.

[0085] Furthermore, the revision system can automatically suggest works by related authors for the work entered by the user using the generation AI, which can be used as a reference for revisions. For example, for part of a novel written by a user, similar works by author A can be presented and used as a reference for revisions. This allows works by related authors to be suggested for the work entered by the user. Furthermore, by suggesting works by other authors in addition to the works of the author selected by the user, the system can broaden the user's perspective.

[0086] The revision system can also use an emotion estimation function to check whether the revised work accurately reflects the user's intended emotions. For example, it can check whether emotions such as "joy" or "sadness" conveyed in the user's writing are maintained after revision. This makes it possible to confirm whether the revised work accurately reflects the user's intended emotions. Furthermore, if the user's intended emotions are not accurately reflected, the system can present revised suggestions and repeat the process until the user is satisfied.

[0087] Furthermore, the revision system can enhance learning effectiveness by having the generation AI explain the reasons or background for the revisions to the user during the revision process. For example, the system can explain the reasons for revisions to a piece of writing written by the user to match the writing style of author A. This allows the user to understand the reasons and background for the revisions and enhances learning effectiveness. The generation AI can also be equipped with a function to answer questions the user has during the revision process in real time. This allows the user to gain a deeper understanding of the revision process.

[0088] The revision system can further use its emotion estimation function to predict readers' emotional reactions to the revised work and make optimal revisions. For example, it can make revisions that will make readers feel "moved" or "empathetic." This allows the system to predict readers' emotional reactions to the revised work and make optimal revisions. It can also present multiple revision suggestions based on the reader's emotional reactions, allowing the user to choose from them. For example, it can present multiple rewrite suggestions that will make the reader feel "moved" or "empathetic."

[0089] Furthermore, when outputting the revised work, the generation AI can automatically output it in multiple formats (PDF, ePub, image file). For example, the rewritten text can be output in PDF and ePub format. This allows the revised work to be output in multiple formats. It can also automatically apply the optimal layout and design depending on the format selected by the user. This allows the work to be output in the format desired by the user.

[0090] The revision system can also use emotion estimation to monitor readers' emotional reactions to revised works in real time and collect feedback. For example, it can monitor readers' emotional reactions to rewritten text, such as "movement" or "empathy." This allows it to monitor readers' emotional reactions to revised works in real time and collect feedback. It can also continuously improve its revision algorithm based on the collected feedback. For example, it can analyze readers' emotional reactions and reflect them in the next revision.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The author data learning unit learns author data. For example, it collects text data such as novels, essays, and poems by the author, as well as data on paintings and illustrations, and inputs this data into the generation AI. The generation AI analyzes this data and learns the author's writing style, structure, brushstrokes, and style. Step 2: The user's work input section inputs the user's own writing or drawing. For example, the user provides the AI ​​with a portion of a novel, poem, or essay they have written, or a drawing or illustration they have drawn. The input to the AI ​​is a prompt containing instructions on what the user wants the AI ​​to do. Step 3: The style correction unit corrects the user's original text or illustration entered by the user work input unit based on the author's style learned by the author data learning unit. For example, it rewrites the user's written text to match the author's style and structure, or corrects the user's drawn illustration to match the author's brushstrokes and style. The generation AI makes appropriate changes to bring the user's work closer to the author's style. Step 4: The revised work output unit outputs the revised work, for example, the rewritten text or the revised picture as a text file or an image file.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an author data learning unit that learns author data; a user work input section for inputting text or pictures created by the user; a style correction unit that corrects the user's original text or picture input by the user work input unit based on the author's style learned by the author data learning unit; a modified work output unit that outputs the modified work; A system characterized by:

2. The author data learning unit Using emotion estimation, the system learns the emotional patterns contained in the artist's work and reflects similar emotions in the user's work.

2. The system of claim 1.

3. The author data learning unit When learning from artist data, audio or video data is also incorporated to perform multimodal learning.

2. The system of claim 1.

4. The user work input unit The generative AI provides real-time feedback to the text or image entered by the user and suggests directions for correction.

2. The system of claim 1.

5. The style correction unit When making revisions to suit the author's style, the generative AI will present multiple revision suggestions and allow the user to choose.

2. The system of claim 1.

6. The revised work output unit When outputting your revised work, the generative AI automatically outputs it in multiple formats (PDF, ePub, image files) 2. The system of claim 1.

7. The user work input unit Using emotion estimation, the system analyzes the emotions conveyed in the user's work and modifies it to fit the artist's style while maintaining that emotion.

2. The system of claim 1.

8. The revised work output unit Using emotion estimation, we evaluate whether the revised work accurately conveys the emotions expected by the user.

2. The system of claim 1.

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