system

Abstract

The system according to this embodiment aims to support individuals in easily publishing their own works. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, an editing unit, a selection unit, and a support unit. The analysis unit analyzes market data. The proposal unit proposes theme selections based on the analysis results obtained by the analysis unit. The editing unit performs manuscript editing and proofreading, and proposes designs. The selection unit selects the optimal publishing platform. The support unit assists with marketing strategies and promotional activities.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, when an individual publishes their own work easily, there are problems that many processes such as theme selection, editing, selection of a publishing platform, and formulation of a marketing strategy are complicated and the burden is large.

[0005] The system according to the embodiment aims to support an individual to easily publish their own work.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, an editing unit, a selection unit, and a support unit. The analysis unit analyzes market data. The proposal unit proposes theme selections based on the analysis results obtained by the analysis unit. The editing unit handles manuscript editing, proofreading, and design proposals. The selection unit selects the optimal publishing platform. The support unit assists with marketing strategies and promotional activities. [Effects of the Invention]

[0007] The system according to this embodiment can support individuals in easily publishing their own works. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The publishing support system according to an embodiment of the present invention is a system that supports individuals in easily publishing their own works. This publishing support system analyzes market data and proposes theme selections based on popularity and trends. Furthermore, it provides manuscript editing and proofreading, design suggestions, and selects the optimal publishing platform. It also supports marketing strategies and promotional activities, reducing the burden on authors. For example, the publishing support system first analyzes market data. In this process, the publishing support system analyzes past sales data, reader preferences, trends, etc., to identify popular themes and genres. For example, it analyzes recent bestsellers and popular genres and proposes themes to authors. This allows authors to select themes that meet market needs. Next, the publishing support system provides manuscript editing and proofreading, and design suggestions. The publishing support system analyzes the manuscript created by the author and suggests corrections to grammar and expression. It also provides suggestions for cover and layout design. For example, the publishing support system points out grammatical errors in the manuscript and presents appropriate correction suggestions. Regarding cover design, the publishing support system generates multiple design options and proposes them to the author. This allows authors to create high-quality manuscripts and designs. Furthermore, the publishing support system selects the optimal publishing platform. Based on the author's work content and target audience, the system suggests the most suitable publishing platform. For example, it selects a platform that meets the author's needs, such as an e-book platform or an on-demand printing service. This allows authors to publish their works in the most optimal way. Finally, the publishing support system assists with marketing strategies and promotional activities. The system analyzes market data and proposes effective marketing strategies. For example, it suggests promotions utilizing social media or advertising campaigns targeting the audience. This allows authors to conduct effective marketing activities and increase awareness of their works. In this way, the publishing support system is a service that uses AI to support individuals in easily publishing their works, providing an environment where anyone, from beginners to experienced writers, can easily achieve high-quality publishing.This allows publishing support systems to reduce the burden on authors and provide an environment where anyone can easily achieve high-quality publishing.

[0029] The publishing support system according to this embodiment comprises an analysis unit, a proposal unit, an editorial unit, a selection unit, and a support unit. The analysis unit analyzes market data. Market data includes, but is not limited to, sales data, consumer survey data, and competitor analysis data. The analysis unit analyzes market data using methods such as statistical analysis, data mining, and machine learning algorithms. The proposal unit proposes theme selections based on the analysis results obtained by the analysis unit. The proposal unit identifies, for example, popular themes, trending themes, and niche themes, and proposes these themes to authors. The editorial unit performs editing and proofreading of manuscripts and proposes designs. The editorial unit performs, for example, grammar checks, applies style guides, and adjusts designs. The editorial unit analyzes the manuscript created by the author and proposes corrections to grammar and expression. The editorial unit identifies grammatical errors in the manuscript using a grammar checking tool and presents appropriate corrections. The editorial unit also generates cover and layout design proposals and proposes them to authors. The editorial department, for example, generates multiple design options using design templates and proposes them to the author. The selection department selects the optimal publishing platform. The selection department proposes the most suitable platform from among, for example, e-book platforms, print publishing platforms, and online publishing platforms, based on the content of the author's work and the target audience. The support department provides support for marketing strategies and promotional activities. The support department proposes, for example, targeted marketing, promotional campaigns, and advertising strategies. The support department proposes promotions utilizing social media and advertising campaigns aimed at the target audience. As a result, the publishing support system according to this embodiment can consistently handle everything from market data analysis and theme selection to manuscript editing, publishing platform selection, and marketing strategy support.

[0030] The analytics department analyzes market data. Market data includes, but is not limited to, sales data, consumer survey data, and competitor analysis data. Specifically, sales data includes past sales performance, sales trends, and identification of best-selling products and genres. Consumer survey data includes reader preferences and purchasing behavior, survey results, reviews, and feedback. Competitive analysis data includes sales data, marketing strategies, pricing, and promotional activities of competitors' publications. The analytics department analyzes this data in detail using methods such as statistical analysis, data mining, and machine learning algorithms. For example, statistical analysis reveals the distribution and correlation of data and identifies trends and patterns. Data mining extracts useful information and insights from large amounts of data, and machine learning algorithms build predictive models based on historical data to forecast future trends and demand. This allows the analytics department to accurately grasp market trends and reader needs and use this information to formulate publishing strategies.

[0031] The Proposal Department proposes theme selections based on the analysis results obtained by the Analysis Department. Specifically, it proposes specific themes to authors based on popular themes, trending themes, and niche themes identified by the Analysis Department. For example, popular themes include those related to current bestsellers and trending topics. Trending themes include social concerns, new technologies, and cultural movements. Niche themes include those focusing on specific specialized fields or minor genres. The Proposal Department proposes these themes to authors and provides information to help them make their selections. Furthermore, the Proposal Department provides detailed information such as the reasons for theme selection, market demand forecasts, and competitive landscape, providing authors with information to help them make informed decisions. This allows the Proposal Department to help authors select themes that meet market needs and increase their chances of success.

[0032] The editorial team handles manuscript editing, proofreading, and design proposals. Specifically, this includes grammar checks, application of style guides, and design adjustments. The editorial team analyzes the manuscript written by the author and proposes corrections to grammar and expression. For example, they use grammar checking tools to identify grammatical errors in the manuscript and provide appropriate correction suggestions. They also apply style guides to improve the consistency and readability of the writing. Furthermore, the editorial team generates cover and layout design proposals and proposes them to the author. For example, they generate multiple design options using design templates and propose them to the author. This allows the editorial team to improve the quality of the manuscript and create an attractive publication for readers. In addition, the editorial team maintains close communication with the author, and proceeds with the editing work while reflecting the author's intentions and feedback. This allows them to finalize the manuscript in the best possible way while respecting the author's intentions.

[0033] The selection committee will choose the most suitable publishing platform. Specifically, they will propose the most appropriate platform from among e-book platforms, print publishing platforms, and online publishing platforms, based on the content of the author's work and target audience. For example, e-book platforms include large platforms that can access a wide range of readers and platforms that specialize in specific genres. Print publishing platforms are suitable for those aiming for sales in traditional bookstores or those who want to provide high-quality printed materials. Online publishing platforms offer methods of reaching readers directly through websites and blogs. The selection committee will compare the characteristics and advantages of these platforms and select the platform that is most suitable for the author's work. The selection committee will also explain in detail how to use the publishing platform and the procedures involved, supporting authors to ensure a smooth publishing process. In this way, the selection committee will enable authors to choose the optimal platform and effectively publish their works.

[0034] The support department assists with marketing strategies and promotional activities. Specifically, it proposes targeted marketing, promotional campaigns, and advertising strategies. The support department proposes promotions utilizing social media and advertising campaigns aimed at target readers. For example, in social media promotions, it regularly posts content related to the author's work to increase engagement with readers. In advertising campaigns aimed at target readers, it deploys advertisements targeting specific reader segments to effectively promote the work. Furthermore, the support department provides the tools and resources necessary for planning and executing marketing strategies, supporting authors in conducting effective promotional activities. This allows the support department to increase awareness of the author's work and boost sales. In addition, the support department regularly evaluates the effectiveness of promotional activities and revise strategies as needed to achieve continuously effective marketing.

[0035] The analysis department can analyze past sales data, reader preferences, trends, and more. For example, the analysis department can collect past sales data and analyze sales data, sales channel data, customer purchase history, etc. It can also collect reader preferences and conduct surveys, purchase history analysis, social media analysis, etc. Furthermore, the analysis department can analyze trends, including search trends, social media trends, and industry trends. By analyzing past sales data, reader preferences, and trends, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input past sales data into an AI, which can then analyze the data and identify trends.

[0036] The suggestion department can identify popular themes and genres and propose them to authors. For example, the suggestion department identifies popular themes and genres based on sales rankings, reader survey results, and social media mentions. The suggestion department can then propose these identified themes and genres to authors. This allows authors to select themes that meet market needs by identifying and proposing popular themes and genres. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input sales ranking data into an AI, which can then analyze the data to identify popular themes.

[0037] The editorial department can analyze the manuscript written by the author and suggest corrections to grammar and expression. For example, the editorial department can use a grammar checking tool to identify grammatical errors in the manuscript and suggest appropriate corrections. The editorial department can also apply a style guide to suggest corrections to expression. This allows authors to create high-quality manuscripts by suggesting corrections to grammar and expression. Some or all of the above processes performed by the editorial department may be carried out using AI, for example, or not. For example, the editorial department can input manuscript data into an AI, which can detect grammatical errors and suggest corrections.

[0038] The editorial department can generate cover and layout design proposals and propose them to the author. For example, the editorial department can generate multiple design proposals using design templates and propose them to the author. Alternatively, the editorial department can create customized design proposals using design software. This allows the author to create an attractive design by presenting cover and layout design proposals. Some or all of the above processes by the editorial department may be performed using AI, or not. For example, the editorial department can input a design template into an AI, which can then generate and propose design proposals.

[0039] The selection unit can propose the most suitable publishing platform based on the content of the author's work and the target audience. For example, the selection unit selects a platform that meets the author's needs from among e-book platforms, print publishing platforms, and online publishing platforms. This allows authors to publish their works in the most optimal way by proposing the most suitable publishing platform based on the content of their work and the target audience. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the author's work data into an AI, which can then propose the most suitable publishing platform.

[0040] The support department can propose promotions utilizing social media and advertising campaigns targeting specific audiences. For example, the support department can propose social media advertising campaigns, influencer marketing, and content marketing. Furthermore, the support department can propose targeted advertising, retargeting advertising, and cross-media campaigns. This allows authors to conduct effective marketing activities by proposing social media-based promotions and advertising campaigns. Some or all of the above processes performed by the support department may be carried out using AI, or not. For example, the support department can input social media data into an AI, which can then propose the optimal promotion strategy.

[0041] The analysis department can perform analyses that consider not only historical sales data but also real-time market trends. For example, the analysis department can obtain real-time best-seller lists and analyze them in comparison to historical sales data. The analysis department can also collect social media trends in real time and analyze them in combination with sales data. Furthermore, the analysis department can obtain real-time sales data from online bookstores and integrate it with historical data for analysis. This allows for more accurate analysis results by considering both historical sales data and real-time market trends. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input real-time market data into an AI, which can then analyze the data to identify market trends.

[0042] The analytics department can analyze in detail readers' responses to specific genres or themes. For example, it can collect reader reviews on a particular genre and analyze positive and negative responses. It can also analyze readers' social media posts on a particular theme to identify popular topics. Furthermore, it can analyze readers' purchase history on a particular genre to understand their purchasing trends. This allows for more appropriate theme selection by analyzing readers' responses to specific genres or themes in detail. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input reader reviews into an AI, which can analyze the data to identify reader responses.

[0043] The analysis unit can analyze regional market data while taking into account the user's geographical location. For example, the analysis unit can analyze regional best-seller lists based on the user's location. The analysis unit can also analyze regional reader preferences based on the user's geographical location. Furthermore, the analysis unit can analyze regional sales data based on the user's location. By analyzing regional market data while taking into account the user's geographical location, the analysis unit can provide analysis results that meet the needs of each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI, which can then analyze regional market data.

[0044] The analysis department can incorporate and analyze social media trend data. For example, the analysis department can collect social media trend data and combine it with sales data for analysis. The analysis department can also collect social media hashtag data and analyze reader interest. Furthermore, the analysis department can collect social media post data and analyze responses to specific themes. By incorporating social media trend data, it becomes possible to obtain analysis results that reflect the latest market trends. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input social media trend data into an AI, which can then analyze the data to identify market trends.

[0045] The proposal department can improve the accuracy of theme selection by referring to past success stories. For example, the proposal department can analyze themes of past bestsellers and suggest similar themes. It can also improve the accuracy of theme selection by referring to data from past successful publishing projects. Furthermore, the proposal department can analyze past reader responses and suggest successful themes. In this way, the accuracy of theme selection is improved by referring to past success stories. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on past success stories into AI, and the AI ​​can analyze the data to improve the accuracy of theme selection.

[0046] The suggestion unit can propose customized themes that take into account the user's past works and preferences. For example, the suggestion unit can analyze the user's past works and propose similar themes. It can also analyze the user's preferences and propose themes that will interest them. Furthermore, the suggestion unit can refer to successful examples of the user's past works and propose customized themes. In this way, by taking into account the user's past works and preferences, it can propose themes that are more suitable for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past work data into AI, and the AI ​​can analyze the data and propose customized themes.

[0047] The suggestion unit can propose popular themes for each region, taking into account the user's geographical location. For example, the suggestion unit can propose popular themes for each region based on the user's location. Furthermore, the suggestion unit can analyze the preferences of readers in each region based on the user's geographical location and propose themes accordingly. In addition, the suggestion unit can propose themes that take regional trends into account, based on the user's location. This allows for theme proposals tailored to regional needs by considering the user's geographical location when proposing popular themes for each region. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location into an AI, which can then propose popular themes for each region.

[0048] The suggestion unit can analyze a user's social media activity and propose relevant themes. For example, the suggestion unit can analyze a user's social media accounts and propose themes that might interest them. It can also analyze a user's social media posts and propose relevant themes. Furthermore, it can analyze a user's social media hashtags and propose themes that might interest them. In this way, by analyzing a user's social media activity, the suggestion unit can propose themes that are tailored to the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into an AI, which can then analyze the data and propose relevant themes.

[0049] The editorial department can offer suggestions not only on grammar and expression, but also on story structure and character development. For example, the editorial department can point out grammatical errors in a manuscript and suggest appropriate corrections. They can also analyze the story structure and suggest areas for improvement. Furthermore, they can analyze character development and propose compelling characters. This allows for the creation of more engaging manuscripts by offering suggestions not only on grammar and expression, but also on story structure and character development. Some or all of the above processes performed by the editorial department may be carried out using AI, or not. For example, the editorial department can input manuscript data into an AI, which can then detect grammatical errors and suggest story structure and character development.

[0050] The editorial department can improve the accuracy of editing by referring to past editing history. For example, the editorial department can analyze past editing history and suggest similar revisions. The editorial department can also maintain editing consistency by referring to past editing history. Furthermore, the editorial department can suggest the optimal editing method based on past editing history. In this way, by referring to past editing history, editing consistency can be maintained and accuracy improved. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input past editing history data into AI, and the AI ​​can analyze the data to improve editing accuracy.

[0051] The editorial team can suggest regional expressions and phrases by taking into account the user's geographical location. For example, the editorial team can suggest region-specific expressions based on the user's location. Furthermore, the editorial team can suggest regional expressions based on the user's geographical location. In addition, the editorial team can suggest expressions tailored to the culture of each region based on the user's location. This allows for the provision of regionally appropriate expressions by suggesting regional expressions and phrases while considering the user's geographical location. Some or all of the above processing by the editorial team may be performed using AI, or not. For example, the editorial team can input the user's geographical location into AI, which can then suggest regional expressions and phrases.

[0052] The editorial team can analyze users' social media activity and suggest relevant expressions and designs. For example, the editorial team can analyze users' social media accounts and suggest relevant expressions. They can also analyze users' social media posts and suggest relevant designs. Furthermore, they can analyze users' social media hashtags and suggest relevant expressions. In this way, by analyzing users' social media activity, they can suggest expressions and designs that match the users' interests. Some or all of the above processes performed by the editorial team may be carried out using AI, for example, or not. For example, the editorial team can input users' social media data into an AI, which can then analyze the data and suggest relevant expressions and designs.

[0053] The selection unit can select the optimal platform by referring to past publishing performance. For example, the selection unit can analyze past publishing performance and propose the optimal platform. The selection unit can also improve the accuracy of platform selection by referring to data from past successful publishing projects. Furthermore, the selection unit can analyze past reader responses and propose the optimal platform. This improves the accuracy of selecting the optimal platform by referring to past publishing performance. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input past publishing performance data into AI, which can then analyze the data and propose the optimal platform.

[0054] The selection unit can propose different platforms depending on the genre of the work and the target audience. For example, the selection unit can analyze the genre of the work and propose the most suitable e-book platform. It can also analyze the attributes of the target audience and propose the most suitable on-demand printing service. Furthermore, the selection unit can analyze the content of the work and propose a publishing platform specialized for a particular genre. This allows for the selection of a more appropriate publishing method by proposing different platforms depending on the genre of the work and the target audience. Some or all of the above processes in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input genre data of the work into an AI, which can then analyze the data and propose the most suitable platform.

[0055] The selection unit can propose regional publishing platforms considering the user's geographical location. For example, the selection unit can propose regional publishing platforms based on the user's location. Furthermore, the selection unit can analyze regional reader preferences based on the user's geographical location and propose platforms accordingly. Additionally, the selection unit can propose publishing platforms that consider regional trends based on the user's location. This allows for the provision of regionally appropriate publishing methods by proposing regional publishing platforms that consider the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, or without AI. For example, the selection unit can input the user's geographical location into an AI, which can then propose regional publishing platforms.

[0056] The selection unit can analyze a user's social media activity and suggest relevant publishing platforms. For example, the selection unit can analyze a user's social media accounts and suggest relevant publishing platforms. It can also analyze a user's social media posts and suggest relevant publishing platforms. Furthermore, the selection unit can analyze a user's social media hashtags and suggest relevant publishing platforms. In this way, by analyzing a user's social media activity, it is possible to suggest publishing platforms that match the user's interests. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's social media data into an AI, which can analyze the data and suggest relevant publishing platforms.

[0057] The support department can propose optimal strategies by referring to past marketing data. For example, the support department can analyze past marketing data and propose the optimal promotional strategy. Furthermore, the support department can improve the accuracy of strategies by referring to data from past successful marketing campaigns. In addition, the support department can analyze past reader responses and propose optimal marketing strategies. This improves the accuracy of proposing optimal marketing strategies by referring to past marketing data. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input past marketing data into an AI, which can then analyze the data and propose the optimal strategy.

[0058] The support department can propose customized promotions that take into account the attributes and preferences of the target audience. For example, the support department can analyze the attributes of the target audience and propose a customized promotion strategy. It can also analyze the preferences of the target audience and propose promotional activities that will attract their interest. Furthermore, the support department can analyze the purchase history of the target audience and propose the optimal promotion strategy. This makes it possible to conduct more effective promotional activities by taking into account the attributes and preferences of the target audience. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input target audience attribute data into AI, and the AI ​​can analyze the data and propose a customized promotion.

[0059] The support department can propose region-specific promotional activities, taking into account the user's geographical location information. For example, the support department can propose region-specific promotional activities based on the user's location. Furthermore, the support department can analyze the preferences of readers in each region based on the user's geographical location information and propose promotional activities accordingly. In addition, the support department can propose promotional activities that take into account regional trends based on the user's location. This enables marketing activities tailored to each region by proposing region-specific promotional activities that take into account the user's geographical location information. Some or all of the above processing in the support department may be performed using AI, or not. For example, the support department can input the user's geographical location information into AI, which can then propose region-specific promotional activities.

[0060] The support department can analyze users' social media activity and propose relevant marketing strategies. For example, the support department can analyze users' social media accounts and propose relevant marketing strategies. It can also analyze users' social media posts and propose relevant marketing strategies. Furthermore, the support department can analyze users' social media hashtags and propose relevant marketing strategies. In this way, by analyzing users' social media activity, it is possible to propose marketing strategies tailored to users' interests. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input user social media data into AI, which can analyze the data and propose relevant marketing strategies.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The publishing support system can analyze an author's writing style and themes from their past works, and based on that, suggest themes and editorial approaches. For example, it can analyze the genres and themes of the author's past works and suggest similar themes. It can also analyze the author's writing style and suggest editorial approaches that suit that style. Furthermore, it can refer to successful examples of the author's past works and suggest ideas that incorporate elements of those successes. This allows for suggestions that take into account the author's individuality and past achievements, enabling the provision of more appropriate support.

[0063] A publishing support system can collect and analyze reader feedback on an author's work in real time. For example, it can collect reader reviews and comments and categorize them into positive and negative feedback. It can also suggest ways to improve the work based on reader feedback. Furthermore, it can analyze reader feedback and use it as a reference for selecting themes and editing for the next work. In this way, it can provide support to authors in creating better works by utilizing reader feedback.

[0064] A publishing support system can identify a target audience based on the content of an author's work and propose a marketing strategy tailored to that audience. For example, it can identify specific age groups or reader demographics based on the genre and theme of the work. It can also suggest effective promotional methods for that audience. Furthermore, it can analyze the preferences and purchase history of the target audience to develop the optimal marketing strategy. This helps ensure that the author's work reaches a wider audience.

[0065] A publishing support system can suggest the most suitable publishing format based on the author's work. For example, it can suggest formats such as ebooks, print books, and audiobooks based on the genre and theme of the work. It can also select the most suitable publishing platform based on the content of the work and the target audience. Furthermore, it can suggest designs and layouts suitable for specific publishing formats based on the content of the work. In this way, it can support authors in ensuring their works are published in the most optimal format.

[0066] The publishing support system can suggest the most suitable promotional methods based on the content of the author's work. For example, it can suggest promotional methods such as social media, blogs, and email newsletters based on the genre and theme of the work. It can also plan optimal advertising campaigns according to the content of the work and the target audience. Furthermore, it can suggest content suitable for specific promotional methods based on the content of the work. In this way, it can help authors reach a wider audience with their work.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis department analyzes market data. Market data includes sales data, consumer survey data, and competitor analysis data. The analysis department analyzes market data using methods such as statistical analysis, data mining, and machine learning algorithms. Step 2: The proposal team proposes theme selections based on the analysis results obtained by the analysis team. The proposal team identifies popular themes, trending themes, niche themes, etc., and proposes these themes to the authors. Step 3: The editorial team edits and proofreads the manuscript and proposes designs. The editorial team performs grammar checks, applies style guides, and adjusts the design. The editorial team analyzes the manuscript written by the author and proposes corrections to grammar and expression. The editorial team uses grammar checking tools to point out grammatical errors in the manuscript and provides appropriate correction suggestions. The editorial team also generates cover and layout design proposals and proposes them to the author. The editorial team generates multiple design proposals using design templates and proposes them to the author. Step 4: The selection team selects the optimal publishing platform. The selection team proposes the most suitable platform from among e-book platforms, print publishing platforms, and online publishing platforms, based on the content of the author's work and target audience. Step 5: The support department assists with marketing strategies and promotional activities. The support department proposes targeted marketing, promotional campaigns, and advertising strategies. The support department proposes promotions utilizing social media and advertising campaigns aimed at target audiences.

[0069] (Example of form 2) The publishing support system according to an embodiment of the present invention is a system that supports individuals in easily publishing their own works. This publishing support system analyzes market data and proposes theme selections based on popularity and trends. Furthermore, it provides manuscript editing and proofreading, design suggestions, and selects the optimal publishing platform. It also supports marketing strategies and promotional activities, reducing the burden on authors. For example, the publishing support system first analyzes market data. In this process, the publishing support system analyzes past sales data, reader preferences, trends, etc., to identify popular themes and genres. For example, it analyzes recent bestsellers and popular genres and proposes themes to authors. This allows authors to select themes that meet market needs. Next, the publishing support system provides manuscript editing and proofreading, and design suggestions. The publishing support system analyzes the manuscript created by the author and suggests corrections to grammar and expression. It also provides suggestions for cover and layout design. For example, the publishing support system points out grammatical errors in the manuscript and presents appropriate correction suggestions. Regarding cover design, the publishing support system generates multiple design options and proposes them to the author. This allows authors to create high-quality manuscripts and designs. Furthermore, the publishing support system selects the optimal publishing platform. Based on the author's work content and target audience, the system suggests the most suitable publishing platform. For example, it selects a platform that meets the author's needs, such as an e-book platform or an on-demand printing service. This allows authors to publish their works in the most optimal way. Finally, the publishing support system assists with marketing strategies and promotional activities. The system analyzes market data and proposes effective marketing strategies. For example, it suggests promotions utilizing social media or advertising campaigns targeting the audience. This allows authors to conduct effective marketing activities and increase awareness of their works. In this way, the publishing support system is a service that uses AI to support individuals in easily publishing their works, providing an environment where anyone, from beginners to experienced writers, can easily achieve high-quality publishing.This allows publishing support systems to reduce the burden on authors and provide an environment where anyone can easily achieve high-quality publishing.

[0070] The publishing support system according to this embodiment comprises an analysis unit, a proposal unit, an editorial unit, a selection unit, and a support unit. The analysis unit analyzes market data. Market data includes, but is not limited to, sales data, consumer survey data, and competitor analysis data. The analysis unit analyzes market data using methods such as statistical analysis, data mining, and machine learning algorithms. The proposal unit proposes theme selections based on the analysis results obtained by the analysis unit. The proposal unit identifies, for example, popular themes, trending themes, and niche themes, and proposes these themes to authors. The editorial unit performs editing and proofreading of manuscripts and proposes designs. The editorial unit performs, for example, grammar checks, applies style guides, and adjusts designs. The editorial unit analyzes the manuscript created by the author and proposes corrections to grammar and expression. The editorial unit identifies grammatical errors in the manuscript using a grammar checking tool and presents appropriate corrections. The editorial unit also generates cover and layout design proposals and proposes them to authors. The editorial department, for example, generates multiple design options using design templates and proposes them to the author. The selection department selects the optimal publishing platform. The selection department proposes the most suitable platform from among, for example, e-book platforms, print publishing platforms, and online publishing platforms, based on the content of the author's work and the target audience. The support department provides support for marketing strategies and promotional activities. The support department proposes, for example, targeted marketing, promotional campaigns, and advertising strategies. The support department proposes promotions utilizing social media and advertising campaigns aimed at the target audience. As a result, the publishing support system according to this embodiment can consistently handle everything from market data analysis and theme selection to manuscript editing, publishing platform selection, and marketing strategy support.

[0071] The analytics department analyzes market data. Market data includes, but is not limited to, sales data, consumer survey data, and competitor analysis data. Specifically, sales data includes past sales performance, sales trends, and identification of best-selling products and genres. Consumer survey data includes reader preferences and purchasing behavior, survey results, reviews, and feedback. Competitive analysis data includes sales data, marketing strategies, pricing, and promotional activities of competitors' publications. The analytics department analyzes this data in detail using methods such as statistical analysis, data mining, and machine learning algorithms. For example, statistical analysis reveals the distribution and correlation of data and identifies trends and patterns. Data mining extracts useful information and insights from large amounts of data, and machine learning algorithms build predictive models based on historical data to forecast future trends and demand. This allows the analytics department to accurately grasp market trends and reader needs and use this information to formulate publishing strategies.

[0072] The Proposal Department proposes theme selections based on the analysis results obtained by the Analysis Department. Specifically, it proposes specific themes to authors based on popular themes, trending themes, and niche themes identified by the Analysis Department. For example, popular themes include those related to current bestsellers and trending topics. Trending themes include social concerns, new technologies, and cultural movements. Niche themes include those focusing on specific specialized fields or minor genres. The Proposal Department proposes these themes to authors and provides information to help them make their selections. Furthermore, the Proposal Department provides detailed information such as the reasons for theme selection, market demand forecasts, and competitive landscape, providing authors with information to help them make informed decisions. This allows the Proposal Department to help authors select themes that meet market needs and increase their chances of success.

[0073] The editorial team handles manuscript editing, proofreading, and design proposals. Specifically, this includes grammar checks, application of style guides, and design adjustments. The editorial team analyzes the manuscript written by the author and proposes corrections to grammar and expression. For example, they use grammar checking tools to identify grammatical errors in the manuscript and provide appropriate correction suggestions. They also apply style guides to improve the consistency and readability of the writing. Furthermore, the editorial team generates cover and layout design proposals and proposes them to the author. For example, they generate multiple design options using design templates and propose them to the author. This allows the editorial team to improve the quality of the manuscript and create an attractive publication for readers. In addition, the editorial team maintains close communication with the author, and proceeds with the editing work while reflecting the author's intentions and feedback. This allows them to finalize the manuscript in the best possible way while respecting the author's intentions.

[0074] The selection committee will choose the most suitable publishing platform. Specifically, they will propose the most appropriate platform from among e-book platforms, print publishing platforms, and online publishing platforms, based on the content of the author's work and target audience. For example, e-book platforms include large platforms that can access a wide range of readers and platforms that specialize in specific genres. Print publishing platforms are suitable for those aiming for sales in traditional bookstores or those who want to provide high-quality printed materials. Online publishing platforms offer methods of reaching readers directly through websites and blogs. The selection committee will compare the characteristics and advantages of these platforms and select the platform that is most suitable for the author's work. The selection committee will also explain in detail how to use the publishing platform and the procedures involved, supporting authors to ensure a smooth publishing process. In this way, the selection committee will enable authors to choose the optimal platform and effectively publish their works.

[0075] The support department assists with marketing strategies and promotional activities. Specifically, it proposes targeted marketing, promotional campaigns, and advertising strategies. The support department proposes promotions utilizing social media and advertising campaigns aimed at target readers. For example, in social media promotions, it regularly posts content related to the author's work to increase engagement with readers. In advertising campaigns aimed at target readers, it deploys advertisements targeting specific reader segments to effectively promote the work. Furthermore, the support department provides the tools and resources necessary for planning and executing marketing strategies, supporting authors in conducting effective promotional activities. This allows the support department to increase awareness of the author's work and boost sales. In addition, the support department regularly evaluates the effectiveness of promotional activities and revise strategies as needed to achieve continuously effective marketing.

[0076] The analysis department can analyze past sales data, reader preferences, trends, and more. For example, the analysis department can collect past sales data and analyze sales data, sales channel data, customer purchase history, etc. It can also collect reader preferences and conduct surveys, purchase history analysis, social media analysis, etc. Furthermore, the analysis department can analyze trends, including search trends, social media trends, and industry trends. By analyzing past sales data, reader preferences, and trends, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input past sales data into an AI, which can then analyze the data and identify trends.

[0077] The suggestion department can identify popular themes and genres and propose them to authors. For example, the suggestion department identifies popular themes and genres based on sales rankings, reader survey results, and social media mentions. The suggestion department can then propose these identified themes and genres to authors. This allows authors to select themes that meet market needs by identifying and proposing popular themes and genres. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input sales ranking data into an AI, which can then analyze the data to identify popular themes.

[0078] The editorial department can analyze the manuscript written by the author and suggest corrections to grammar and expression. For example, the editorial department can use a grammar checking tool to identify grammatical errors in the manuscript and suggest appropriate corrections. The editorial department can also apply a style guide to suggest corrections to expression. This allows authors to create high-quality manuscripts by suggesting corrections to grammar and expression. Some or all of the above processes performed by the editorial department may be carried out using AI, for example, or not. For example, the editorial department can input manuscript data into an AI, which can detect grammatical errors and suggest corrections.

[0079] The editorial department can generate cover and layout design proposals and propose them to the author. For example, the editorial department can generate multiple design proposals using design templates and propose them to the author. Alternatively, the editorial department can create customized design proposals using design software. This allows the author to create an attractive design by presenting cover and layout design proposals. Some or all of the above processes by the editorial department may be performed using AI, or not. For example, the editorial department can input a design template into an AI, which can then generate and propose design proposals.

[0080] The selection unit can propose the most suitable publishing platform based on the content of the author's work and the target audience. For example, the selection unit selects a platform that meets the author's needs from among e-book platforms, print publishing platforms, and online publishing platforms. This allows authors to publish their works in the most optimal way by proposing the most suitable publishing platform based on the content of their work and the target audience. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the author's work data into an AI, which can then propose the most suitable publishing platform.

[0081] The support department can propose promotions utilizing social media and advertising campaigns targeting specific audiences. For example, the support department can propose social media advertising campaigns, influencer marketing, and content marketing. Furthermore, the support department can propose targeted advertising, retargeting advertising, and cross-media campaigns. This allows authors to conduct effective marketing activities by proposing social media-based promotions and advertising campaigns. Some or all of the above processes performed by the support department may be carried out using AI, or not. For example, the support department can input social media data into an AI, which can then propose the optimal promotion strategy.

[0082] The analysis unit can estimate the user's emotions and adjust the market data analysis method based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing the latest trend data and provide results quickly. If the user is relaxed, it can perform a detailed analysis and provide comprehensive market data. Furthermore, if the user is stressed, it can provide simple analysis results to reduce the user's burden. In this way, by adjusting the market data analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the analysis method based on the emotions.

[0083] The analysis department can perform analyses that consider not only historical sales data but also real-time market trends. For example, the analysis department can obtain real-time best-seller lists and analyze them in comparison to historical sales data. The analysis department can also collect social media trends in real time and analyze them in combination with sales data. Furthermore, the analysis department can obtain real-time sales data from online bookstores and integrate it with historical data for analysis. This allows for more accurate analysis results by considering both historical sales data and real-time market trends. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input real-time market data into an AI, which can then analyze the data to identify market trends.

[0084] The analytics department can analyze in detail readers' responses to specific genres or themes. For example, it can collect reader reviews on a particular genre and analyze positive and negative responses. It can also analyze readers' social media posts on a particular theme to identify popular topics. Furthermore, it can analyze readers' purchase history on a particular genre to understand their purchasing trends. This allows for more appropriate theme selection by analyzing readers' responses to specific genres or themes in detail. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input reader reviews into an AI, which can analyze the data to identify reader responses.

[0085] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will prioritize displaying the most important analysis results. If the user is relaxed, it can display detailed analysis results in order. Furthermore, if the user is stressed, it can prioritize displaying concise analysis results. This allows the system to quickly provide the user with the most important information by prioritizing the analysis results according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can then prioritize the analysis results based on the emotions.

[0086] The analysis unit can analyze regional market data while taking into account the user's geographical location. For example, the analysis unit can analyze regional best-seller lists based on the user's location. The analysis unit can also analyze regional reader preferences based on the user's geographical location. Furthermore, the analysis unit can analyze regional sales data based on the user's location. By analyzing regional market data while taking into account the user's geographical location, the analysis unit can provide analysis results that meet the needs of each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI, which can then analyze regional market data.

[0087] The analysis department can incorporate and analyze social media trend data. For example, the analysis department can collect social media trend data and combine it with sales data for analysis. The analysis department can also collect social media hashtag data and analyze reader interest. Furthermore, the analysis department can collect social media post data and analyze responses to specific themes. By incorporating social media trend data, it becomes possible to obtain analysis results that reflect the latest market trends. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input social media trend data into an AI, which can then analyze the data to identify market trends.

[0088] The suggestion unit can estimate the user's emotions and adjust the theme selection suggestion method based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed theme suggestions. If the user is in a hurry, it can provide concise theme suggestions. Furthermore, if the user is excited, it can provide visually appealing theme suggestions. By adjusting the theme selection suggestion method according to the user's emotions, more appropriate theme suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into AI, and the AI ​​can adjust the theme selection suggestion method based on the emotions.

[0089] The proposal department can improve the accuracy of theme selection by referring to past success stories. For example, the proposal department can analyze themes of past bestsellers and suggest similar themes. It can also improve the accuracy of theme selection by referring to data from past successful publishing projects. Furthermore, the proposal department can analyze past reader responses and suggest successful themes. In this way, the accuracy of theme selection is improved by referring to past success stories. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on past success stories into AI, and the AI ​​can analyze the data to improve the accuracy of theme selection.

[0090] The suggestion unit can propose customized themes that take into account the user's past works and preferences. For example, the suggestion unit can analyze the user's past works and propose similar themes. It can also analyze the user's preferences and propose themes that will interest them. Furthermore, the suggestion unit can refer to successful examples of the user's past works and propose customized themes. In this way, by taking into account the user's past works and preferences, it can propose themes that are more suitable for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past work data into AI, and the AI ​​can analyze the data and propose customized themes.

[0091] The suggestion unit can estimate the user's emotions and determine the priority of suggested topics based on those emotions. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting the most important topics. If the user is relaxed, it can suggest detailed topics in order. Furthermore, if the user is stressed, it can prioritize suggesting concise topics. This allows the suggestion unit to quickly suggest the most important topics to the user by determining the priority of suggested topics according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then determine the priority of topics based on the emotions.

[0092] The suggestion unit can propose popular themes for each region, taking into account the user's geographical location. For example, the suggestion unit can propose popular themes for each region based on the user's location. Furthermore, the suggestion unit can analyze the preferences of readers in each region based on the user's geographical location and propose themes accordingly. In addition, the suggestion unit can propose themes that take regional trends into account, based on the user's location. This allows for theme proposals tailored to regional needs by considering the user's geographical location when proposing popular themes for each region. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location into an AI, which can then propose popular themes for each region.

[0093] The suggestion unit can analyze a user's social media activity and propose relevant themes. For example, the suggestion unit can analyze a user's social media accounts and propose themes that might interest them. It can also analyze a user's social media posts and propose relevant themes. Furthermore, it can analyze a user's social media hashtags and propose themes that might interest them. In this way, by analyzing a user's social media activity, the suggestion unit can propose themes that are tailored to the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into an AI, which can then analyze the data and propose relevant themes.

[0094] The editorial team can estimate the user's emotions and adjust the editing and proofreading methods of the manuscript based on the estimated emotions. For example, if the user is relaxed, the editorial team can provide detailed editing suggestions. If the user is in a hurry, they can provide concise editing suggestions. Furthermore, if the user is stressed, they can provide simple proofreading suggestions. By adjusting the editing and proofreading methods according to the user's emotions, more appropriate editing suggestions can be made. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI, or not using AI. For example, the editorial team can input user emotion data into AI, and the AI ​​can adjust the editing and proofreading methods based on the emotions.

[0095] The editorial department can offer suggestions not only on grammar and expression, but also on story structure and character development. For example, the editorial department can point out grammatical errors in a manuscript and suggest appropriate corrections. They can also analyze the story structure and suggest areas for improvement. Furthermore, they can analyze character development and propose compelling characters. This allows for the creation of more engaging manuscripts by offering suggestions not only on grammar and expression, but also on story structure and character development. Some or all of the above processes performed by the editorial department may be carried out using AI, or not. For example, the editorial department can input manuscript data into an AI, which can then detect grammatical errors and suggest story structure and character development.

[0096] The editorial department can improve the accuracy of editing by referring to past editing history. For example, the editorial department can analyze past editing history and suggest similar revisions. The editorial department can also maintain editing consistency by referring to past editing history. Furthermore, the editorial department can suggest the optimal editing method based on past editing history. In this way, by referring to past editing history, editing consistency can be maintained and accuracy improved. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input past editing history data into AI, and the AI ​​can analyze the data to improve editing accuracy.

[0097] The editorial team can estimate the user's emotions and determine editing priorities based on those emotions. For example, if the user is in a hurry, the editorial team can prioritize suggesting the most important edits. If the user is relaxed, it can suggest detailed edits in order. Furthermore, if the user is stressed, it can prioritize suggesting concise edits. This allows for the rapid suggestion of the most important edits for the user by determining editing priorities according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI or not. For example, the editorial team can input user emotion data into an AI, which can then determine editing priorities based on those emotions.

[0098] The editorial team can suggest regional expressions and phrases by taking into account the user's geographical location. For example, the editorial team can suggest region-specific expressions based on the user's location. Furthermore, the editorial team can suggest regional expressions based on the user's geographical location. In addition, the editorial team can suggest expressions tailored to the culture of each region based on the user's location. This allows for the provision of regionally appropriate expressions by suggesting regional expressions and phrases while considering the user's geographical location. Some or all of the above processing by the editorial team may be performed using AI, or not. For example, the editorial team can input the user's geographical location into AI, which can then suggest regional expressions and phrases.

[0099] The editorial team can analyze users' social media activity and suggest relevant expressions and designs. For example, the editorial team can analyze users' social media accounts and suggest relevant expressions. They can also analyze users' social media posts and suggest relevant designs. Furthermore, they can analyze users' social media hashtags and suggest relevant expressions. In this way, by analyzing users' social media activity, they can suggest expressions and designs that match the users' interests. Some or all of the above processes performed by the editorial team may be carried out using AI, for example, or not. For example, the editorial team can input users' social media data into an AI, which can then analyze the data and suggest relevant expressions and designs.

[0100] The selection unit can estimate the user's emotions and adjust the publishing platform selection method based on the estimated user emotions. For example, if the user is relaxed, the selection unit can perform a detailed platform selection. If the user is in a hurry, it can perform a concise platform selection. Furthermore, if the user is stressed, it can perform a simple platform selection. In this way, by adjusting the publishing platform selection method according to the user's emotions, a more appropriate platform can be selected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not using AI. For example, the selection unit can input user emotion data into AI, and the AI ​​can adjust the publishing platform selection method based on the emotions.

[0101] The selection unit can select the optimal platform by referring to past publishing performance. For example, the selection unit can analyze past publishing performance and propose the optimal platform. The selection unit can also improve the accuracy of platform selection by referring to data from past successful publishing projects. Furthermore, the selection unit can analyze past reader responses and propose the optimal platform. This improves the accuracy of selecting the optimal platform by referring to past publishing performance. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input past publishing performance data into AI, which can then analyze the data and propose the optimal platform.

[0102] The selection unit can propose different platforms depending on the genre of the work and the target audience. For example, the selection unit can analyze the genre of the work and propose the most suitable e-book platform. It can also analyze the attributes of the target audience and propose the most suitable on-demand printing service. Furthermore, the selection unit can analyze the content of the work and propose a publishing platform specialized for a particular genre. This allows for the selection of a more appropriate publishing method by proposing different platforms depending on the genre of the work and the target audience. Some or all of the above processes in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input genre data of the work into an AI, which can then analyze the data and propose the most suitable platform.

[0103] The selection unit can estimate the user's emotions and determine the priority of the platforms to select based on the estimated emotions. For example, if the user is in a hurry, the selection unit will prioritize suggesting the most important platforms. If the user is relaxed, it can suggest detailed platforms in order. Furthermore, if the user is stressed, it can prioritize suggesting concise platforms. This allows the system to quickly suggest the most important platforms for the user by determining the priority of the platforms to select according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not using AI. For example, the selection unit can input user emotion data into an AI, which can then determine the priority of the platforms based on the emotions.

[0104] The selection unit can propose regional publishing platforms considering the user's geographical location. For example, the selection unit can propose regional publishing platforms based on the user's location. Furthermore, the selection unit can analyze regional reader preferences based on the user's geographical location and propose platforms accordingly. Additionally, the selection unit can propose publishing platforms that consider regional trends based on the user's location. This allows for the provision of regionally appropriate publishing methods by proposing regional publishing platforms that consider the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, or without AI. For example, the selection unit can input the user's geographical location into an AI, which can then propose regional publishing platforms.

[0105] The selection unit can analyze a user's social media activity and suggest relevant publishing platforms. For example, the selection unit can analyze a user's social media accounts and suggest relevant publishing platforms. It can also analyze a user's social media posts and suggest relevant publishing platforms. Furthermore, the selection unit can analyze a user's social media hashtags and suggest relevant publishing platforms. In this way, by analyzing a user's social media activity, it is possible to suggest publishing platforms that match the user's interests. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's social media data into an AI, which can analyze the data and suggest relevant publishing platforms.

[0106] The support unit can estimate the user's emotions and adjust marketing strategies and promotional activities based on those estimated emotions. For example, if the user is relaxed, the support unit can suggest a detailed marketing strategy. If the user is in a hurry, it can suggest a concise marketing strategy. Furthermore, if the user is stressed, it can suggest simple promotional activities. By adjusting marketing strategies and promotional activities according to the user's emotions, more effective marketing activities become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into AI, and the AI ​​can adjust marketing strategies and promotional activities based on those emotions.

[0107] The support department can propose optimal strategies by referring to past marketing data. For example, the support department can analyze past marketing data and propose the optimal promotional strategy. Furthermore, the support department can improve the accuracy of strategies by referring to data from past successful marketing campaigns. In addition, the support department can analyze past reader responses and propose optimal marketing strategies. This improves the accuracy of proposing optimal marketing strategies by referring to past marketing data. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input past marketing data into an AI, which can then analyze the data and propose the optimal strategy.

[0108] The support department can propose customized promotions that take into account the attributes and preferences of the target audience. For example, the support department can analyze the attributes of the target audience and propose a customized promotion strategy. It can also analyze the preferences of the target audience and propose promotional activities that will attract their interest. Furthermore, the support department can analyze the purchase history of the target audience and propose the optimal promotion strategy. This makes it possible to conduct more effective promotional activities by taking into account the attributes and preferences of the target audience. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input target audience attribute data into AI, and the AI ​​can analyze the data and propose a customized promotion.

[0109] The support unit can estimate the user's emotions and prioritize marketing strategies based on those emotions. For example, if the user is in a hurry, the support unit will prioritize suggesting the most important marketing strategies. If the user is relaxed, it can suggest detailed marketing strategies sequentially. Furthermore, if the user is stressed, it can prioritize suggesting concise marketing strategies. This allows the support unit to quickly suggest the most important strategies to the user by prioritizing marketing strategies according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into an AI, which can then prioritize marketing strategies based on those emotions.

[0110] The support department can propose region-specific promotional activities, taking into account the user's geographical location information. For example, the support department can propose region-specific promotional activities based on the user's location. Furthermore, the support department can analyze the preferences of readers in each region based on the user's geographical location information and propose promotional activities accordingly. In addition, the support department can propose promotional activities that take into account regional trends based on the user's location. This enables marketing activities tailored to each region by proposing region-specific promotional activities that take into account the user's geographical location information. Some or all of the above processing in the support department may be performed using AI, or not. For example, the support department can input the user's geographical location information into AI, which can then propose region-specific promotional activities.

[0111] The support department can analyze users' social media activity and propose relevant marketing strategies. For example, the support department can analyze users' social media accounts and propose relevant marketing strategies. It can also analyze users' social media posts and propose relevant marketing strategies. Furthermore, the support department can analyze users' social media hashtags and propose relevant marketing strategies. In this way, by analyzing users' social media activity, it is possible to propose marketing strategies tailored to users' interests. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input user social media data into AI, which can analyze the data and propose relevant marketing strategies.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The publishing support system can analyze an author's writing style and themes from their past works, and based on that, suggest themes and editorial approaches. For example, it can analyze the genres and themes of the author's past works and suggest similar themes. It can also analyze the author's writing style and suggest editorial approaches that suit that style. Furthermore, it can refer to successful examples of the author's past works and suggest ideas that incorporate elements of those successes. This allows for suggestions that take into account the author's individuality and past achievements, enabling the provision of more appropriate support.

[0114] A publishing support system can estimate the author's emotions, monitor their writing progress based on those emotions, and provide support at the appropriate time. For example, if the author is stuck, it can offer relaxing advice and inspiration. Conversely, if the author is focused, it can refrain from interrupting their writing. Furthermore, if the author is feeling stressed, it can check their writing progress and suggest schedule adjustments as needed. This allows for improved writing efficiency by providing support tailored to the author's emotions.

[0115] A publishing support system can collect and analyze reader feedback on an author's work in real time. For example, it can collect reader reviews and comments and categorize them into positive and negative feedback. It can also suggest ways to improve the work based on reader feedback. Furthermore, it can analyze reader feedback and use it as a reference for selecting themes and editing for the next work. In this way, it can provide support to authors in creating better works by utilizing reader feedback.

[0116] A publishing support system can estimate an author's emotions and provide support to maintain their motivation based on those emotions. For example, if an author is losing motivation, it can offer encouraging messages and success stories. If an author is highly motivated, it can provide advice to maintain that momentum. Furthermore, if an author is feeling anxious, it can offer advice to help them relax and suggest ways to refresh themselves. By providing support tailored to the author's emotions, it can help maintain their motivation and support efficient writing.

[0117] A publishing support system can identify a target audience based on the content of an author's work and propose a marketing strategy tailored to that audience. For example, it can identify specific age groups or reader demographics based on the genre and theme of the work. It can also suggest effective promotional methods for that audience. Furthermore, it can analyze the preferences and purchase history of the target audience to develop the optimal marketing strategy. This helps ensure that the author's work reaches a wider audience.

[0118] A publishing support system can estimate the author's emotions and provide support for managing writing progress based on those emotions. For example, if the author is focused on writing, it can check their progress and provide appropriate feedback. If the author is stuck, it can offer suggestions for refreshing themselves or providing new ideas. Furthermore, if the author is feeling stressed, it can adjust the writing schedule and provide support. This allows for progress management that is tailored to the author's emotions, thereby improving writing efficiency.

[0119] A publishing support system can suggest the most suitable publishing format based on the author's work. For example, it can suggest formats such as ebooks, print books, and audiobooks based on the genre and theme of the work. It can also select the most suitable publishing platform based on the content of the work and the target audience. Furthermore, it can suggest designs and layouts suitable for specific publishing formats based on the content of the work. In this way, it can support authors in ensuring their works are published in the most optimal format.

[0120] A publishing support system can estimate an author's emotions and provide writing advice based on those emotions. For example, if the author is relaxed, it can provide detailed advice. If the author is in a hurry, it can provide concise advice. Furthermore, if the author is stressed, it can offer suggestions for refreshing themselves and encouraging messages. By providing advice tailored to the author's emotions, it can improve the efficiency of the writing process.

[0121] The publishing support system can suggest the most suitable promotional methods based on the content of the author's work. For example, it can suggest promotional methods such as social media, blogs, and email newsletters based on the genre and theme of the work. It can also plan optimal advertising campaigns according to the content of the work and the target audience. Furthermore, it can suggest content suitable for specific promotional methods based on the content of the work. In this way, it can help authors reach a wider audience with their work.

[0122] A publishing support system can estimate the author's emotions and provide reminders to support their writing progress based on those emotions. For example, if the author is relaxed, it can send reminders to check their writing progress. If the author is in a hurry, it can also remind them of important deadlines. Furthermore, if the author is stressed, it can send suggestions for refreshing themselves and encouraging messages. By providing reminders tailored to the author's emotions, this system can improve writing efficiency.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The analysis department analyzes market data. Market data includes sales data, consumer survey data, and competitor analysis data. The analysis department analyzes market data using methods such as statistical analysis, data mining, and machine learning algorithms. Step 2: The proposal team proposes theme selections based on the analysis results obtained by the analysis team. The proposal team identifies popular themes, trending themes, niche themes, etc., and proposes these themes to the authors. Step 3: The editorial team edits and proofreads the manuscript and proposes designs. The editorial team performs grammar checks, applies style guides, and adjusts the design. The editorial team analyzes the manuscript written by the author and proposes corrections to grammar and expression. The editorial team uses grammar checking tools to point out grammatical errors in the manuscript and provides appropriate correction suggestions. The editorial team also generates cover and layout design proposals and proposes them to the author. The editorial team generates multiple design proposals using design templates and proposes them to the author. Step 4: The selection team selects the optimal publishing platform. The selection team proposes the most suitable platform from among e-book platforms, print publishing platforms, and online publishing platforms, based on the content of the author's work and target audience. Step 5: The support department assists with marketing strategies and promotional activities. The support department proposes targeted marketing, promotional campaigns, and advertising strategies. The support department proposes promotions utilizing social media and advertising campaigns aimed at target audiences.

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the analysis unit, proposal unit, editorial unit, selection unit, and support unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes sales data and consumer survey data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes theme selections based on the analysis results. The editorial unit is implemented by the control unit 46A of the smart device 14 and performs grammatical checks on manuscripts and proposes designs. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the optimal publishing platform. The support unit is implemented by the control unit 46A of the smart device 14 and supports marketing strategies and promotional activities. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the analysis unit, proposal unit, editorial unit, selection unit, and support unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes sales data and consumer survey data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes theme selections based on the analysis results. The editorial unit is implemented by, for example, the control unit 46A of the smart glasses 214 and performs grammatical checks on manuscripts and proposes designs. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the optimal publishing platform. The support unit is implemented by, for example, the control unit 46A of the smart glasses 214 and supports marketing strategies and promotional activities. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the analysis department, proposal department, editorial department, selection department, and support department, is implemented by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis department is implemented by the specific processing unit 290 of the data processing device 12 and analyzes sales data and consumer survey data. The proposal department is implemented by the specific processing unit 290 of the data processing device 12 and proposes theme selections based on the analysis results. The editorial department is implemented by the control unit 46A of the headset terminal 314 and performs grammatical checks on manuscripts and proposes designs. The selection department is implemented by the specific processing unit 290 of the data processing device 12 and selects the optimal publishing platform. The support department is implemented by the control unit 46A of the headset terminal 314 and supports marketing strategies and promotional activities. The correspondence between each department and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the analysis unit, proposal unit, editorial unit, selection unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes sales data and consumer survey data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes theme selections based on the analysis results. The editorial unit is implemented by, for example, the control unit 46A of the robot 414 and performs grammatical checks of manuscripts and proposes designs. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the optimal publishing platform. The support unit is implemented by, for example, the control unit 46A of the robot 414 and supports marketing strategies and promotional activities. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The analysis department analyzes market data, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a theme selection, The editorial department handles manuscript editing, proofreading, and design proposals, A selection department to choose the optimal publishing platform, It includes a support department that assists with marketing strategies and promotional activities. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze past sales data, reader preferences, trends, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Identify popular themes and genres and suggest them to authors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned editorial department, We analyze the author's manuscript and suggest corrections to grammar and expression. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned editorial department, Generate cover and layout design proposals and present them to the author. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is Based on the author's work and target audience, we will suggest the most suitable publishing platform. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit, We propose promotions utilizing social media and advertising campaigns targeting specific audiences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We estimate user sentiment and adjust the market data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is The analysis will take into account not only past sales data but also real-time market trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is Analyze in detail readers' reactions to specific genres or themes. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyze regional market data while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We will incorporate and analyze social media trend data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the theme selection suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We improve the accuracy of theme selection by referring to past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system suggests customized themes based on the user's past works and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggested topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We suggest popular themes for each region, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, Analyze users' social media activity and suggest relevant themes. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editorial department, The system estimates the user's emotions and adjusts the editing and proofreading methods of the manuscript based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned editorial department, We offer suggestions not only for grammar and expression, but also for story structure and character development. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned editorial department, Improve editing accuracy by referring to past editing history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned editorial department, It estimates the user's emotions and determines editing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned editorial department, It suggests regional expressions and phrases, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned editorial department, We analyze users' social media activity and suggest relevant expressions and designs. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is We estimate user sentiment and adjust the publishing platform selection method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned selection unit is We will select the most suitable platform by referring to past publishing results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned selection unit is We propose different platforms depending on the genre of the work and the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned selection unit is It estimates user sentiment and determines platform priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned selection unit is We propose regional publishing platforms that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned selection unit is Analyze users' social media activity and suggest relevant publishing platforms. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit, We estimate user emotions and adjust marketing strategies and promotional activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, We propose the optimal strategy by referring to past marketing data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, We propose customized promotions that take into account the attributes and preferences of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, We estimate user sentiment and prioritize marketing strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit, We propose region-specific promotional activities, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit, We analyze users' social media activity and propose relevant marketing strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes market data, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a theme selection, The editorial department handles manuscript editing, proofreading, and design proposals, A selection department to choose the optimal publishing platform, It includes a support department that assists with marketing strategies and promotional activities. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze past sales data, reader preferences, trends, etc. The system according to feature 1.

3. The aforementioned proposal section is, Identify popular themes and genres and suggest them to authors. The system according to feature 1.

4. The aforementioned editorial department, We analyze the author's manuscript and suggest corrections to grammar and expression. The system according to feature 1.

5. The aforementioned editorial department, Generate cover and layout design proposals and present them to the author. The system according to feature 1.

6. The aforementioned selection unit is Based on the author's work and target audience, we will suggest the most suitable publishing platform. The system according to feature 1.

7. The aforementioned support unit, We propose promotions utilizing social media and advertising campaigns targeting specific audiences. The system according to feature 1.

8. The aforementioned analysis unit is We estimate user sentiment and adjust the market data analysis method based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned analysis unit is The analysis will take into account not only past sales data but also real-time market trends. The system according to feature 1.

10. The aforementioned analysis unit is Analyze in detail readers' reactions to specific genres or themes. The system according to feature 1.

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