system

Abstract

The system according to this embodiment aims to automatically generate and provide novels and comics tailored to the user's preferences. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives information from the user, such as their preferred genre, word count, characters, and location. The generation unit analyzes the information entered by the reception unit and automatically creates a novel or manga based on it. The provision unit provides the novel or manga generated by the generation unit to the user.

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 steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 conventional technology, there is a problem that it is difficult to automatically generate novels and comics according to the user's preferences and it is difficult to meet individual needs.

[0005] The system according to the embodiment aims to automatically generate and provide novels and comics according to the user's preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives information from the user, such as their preferred genre, word count, characters, and location. The generation unit analyzes the information entered by the reception unit and automatically creates a novel or manga based on it. The provision unit provides the novel or manga generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate and provide novels and comics tailored to the user's preferences. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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 novel generation system according to an embodiment of the present invention is a system in which a generating AI automatically creates novels and comics based on information input by the user. This novel generation system analyzes the input information, such as the user's preferred genre, word count, characters, and location, and automatically creates novels and comics based on that information. Furthermore, by specifying story details, the system can generate novels and comics that are closer to what the user wants to read. The generated novels and comics are provided to the user and sold at a low price. By purchasing the generated novels and comics, the user can obtain works that closely match their reading preferences. Additionally, the copyright of the generated works is transferred to the owner, and commercial use is permitted. This mechanism allows users to easily create novels and comics tailored to their preferences, lowering the barrier to creativity. For example, works can be generated for various purposes, such as picture books for birthday presents or autobiographies depicting one's own life. Thus, the novel generation system can automatically generate and provide novels and comics tailored to the user's preferences.

[0029] The novel generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives information from the user, such as their preferred genre, word count, characters, and location. The reception unit allows the user to select genres such as mystery, romance, or science fiction. The reception unit also allows the user to set a range and limit on the number of characters to be entered. For example, a minimum and maximum number of characters can be specified. Furthermore, the reception unit allows the user to input information about characters. For example, information such as names, genders, and ages can be entered. The reception unit also allows the user to input information about locations. For example, a city name, country name, or fictional location can be specified. The generation unit uses a generation AI to analyze the information entered by the reception unit and automatically creates a novel or manga based on that analysis. The generation unit analyzes the input information using, for example, natural language processing technology or machine learning algorithms. The generation unit automatically creates a novel or manga using a generation algorithm. For example, the generation unit generates novels or manga using template-based generation or neural networks. The generation unit can also generate novels or manga based on story details specified by the user. For example, it generates based on detailed information such as plot, character settings, and background settings. The provider provides the novels and comics generated by the generation unit to the user. The provider can provide them in, for example, ebook format or print format. The provider can sell the generated novels and comics at a low price. For example, it can sell them by setting a price range or discount rate. The provider can transfer the copyright of the generated works to the owner. For example, it can clarify the type of copyright and the transfer procedure. The provider can use the generated works for commercial purposes. For example, it can use them for commercial purposes through sales, licensing, advertising revenue, etc. As a result, the novel generation system according to this embodiment can automatically generate and provide novels and comics tailored to the user's preferences.

[0030] The reception desk allows users to input information such as their preferred genre, word count, characters, and location. Specifically, users can choose from a variety of genres, including mystery, romance, science fiction, fantasy, and horror. This allows users to generate novels and comics in genres that suit their preferences. The reception desk can also set ranges and limits on the word count that users can input. For example, users can specify a maximum of 5,000 characters for short stories, 20,000 characters for novellas, and 50,000 characters or more for novels. Furthermore, the reception desk allows users to input detailed information about the characters. For example, they can input information such as name, gender, age, occupation, personality, and background. This allows users to create characters that match their image. Detailed information about locations can also be entered. For example, users can specify city names, country names, fictional locations, or specific place names and building names. This allows users to concretely define the setting of their story. The reception desk builds a database to centrally manage this information and pass it on to the generation department. The information entered by users is saved in real time and can be edited or modified as needed. This allows the reception department to respond to diverse user needs and collect information flexibly.

[0031] The generation unit uses generation AI to analyze information entered by the reception unit and automatically create novels and comics based on that analysis. Specifically, the generation unit uses natural language processing technology and machine learning algorithms to analyze information such as genre, word count, characters, and location entered by the user. For example, the generation AI selects an appropriate writing style and story template based on the genre selected by the user. Furthermore, it adjusts the story's development and episode length based on the word count specified by the user. Based on the character information, it generates character dialogue and actions, clarifying their roles in the story. Based on the location information, it sets the stage and describes the background of the story. The generation unit integrates this information and generates novels and comics using template-based generation or neural networks. For example, template-based generation quickly generates novels and comics by embedding user information into pre-prepared story templates. On the other hand, generation using neural networks can generate more flexible and creative stories. The generation unit can also generate novels and comics based on detailed story information specified by the user. For example, it can depict the story's development and character growth based on detailed information such as plot, character settings, and background settings. This allows the generation unit to automatically produce high-quality novels and comics tailored to the user's preferences.

[0032] The service provider provides users with novels and comics generated by the generation service provider. Specifically, the service provider can provide the generated works in ebook format or print format. In ebook format, works can be provided in common formats such as PDF, ePub, and Mobi, and users can view them on smartphones, tablets, and e-readers. In print format, physical books can be provided to users upon request using on-demand printing services. The service provider can sell the generated novels and comics at low prices. For example, they can set price ranges and discount rates to make them easily accessible to users. Furthermore, the service provider can transfer the copyright of the generated works to the owners. For example, they can clarify the types of copyrights and transfer procedures to allow users to freely use the generated works. The service provider can use the generated works for commercial purposes. For example, they can use them for commercial purposes through sales, licensing, and advertising revenue. This allows the service provider to provide generated works in a variety of ways and offer services that meet user needs. Furthermore, the service provider can collect feedback from users and use it to improve the service. For example, they can improve the generation algorithm and add new features based on user ratings and comments. This allows the service provider to consistently deliver high-quality service and improve user satisfaction.

[0033] The reception section allows users to input information specifying story details. For example, users can input detailed information such as plot, character settings, and background settings. By allowing users to specify story details, the reception section can generate novels or comics that are closer to what the user wants to read. Some or all of the above processing in the reception section may be performed using AI, or not. For example, the reception section can input the story details entered by the user into a generation AI, and the generation AI can generate a novel or comic based on that information.

[0034] The generation unit can generate novels and comics based on the details of a story specified by the user. For example, the generation unit generates novels and comics based on detailed information such as the plot, character settings, and background settings specified by the user. The generation unit uses a generation AI to generate novels and comics based on the details of a story specified by the user. For example, the generation AI develops the story based on the plot specified by the user, describes the characters based on the character settings, and constructs the scenes based on the background settings. The generation unit can use a generation AI to generate novels and comics based on the details of a story specified by the user. This allows for the provision of more personalized works by generating novels and comics based on the details of a story specified by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the detailed story information specified by the user into the generation AI, and the generation AI can generate novels and comics based on that information.

[0035] The service provider can sell the generated novels and comics at a low price. For example, the service provider can sell the generated novels and comics in e-book format or print format. The service provider can set price ranges and discount rates to sell the generated novels and comics at a low price. This allows the service provider to secure revenue by selling the generated novels and comics at a low price. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can optimize the sales price of the generated novels and comics using AI to maximize revenue.

[0036] The service provider can transfer the copyright of the generated work to the owner. For example, the service provider can transfer the copyright of a generated novel or manga to the user. The service provider can transfer the copyright of the generated work to the owner by clearly defining the type of copyright and the transfer procedure. This allows users to freely use the work by transferring the copyright of the generated work to the owner. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can automate the copyright transfer procedure for generated works using AI to perform it efficiently.

[0037] The provider can use the generated works for commercial purposes. For example, the provider can sell the generated novels or comics. The provider can also license the generated works. The provider can also earn advertising revenue based on the generated works. This allows the provider to generate further revenue by using the generated works for commercial purposes. Some or all of the above processes in the provider may be performed using AI, for example, or not using AI. For example, the provider can use AI to optimize the sales strategy for the generated works and maximize revenue.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display genres and characters that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest genres and characters that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI, and the generating AI can suggest the optimal input method based on that data.

[0039] The input section can suggest input options based on the user's current interests and trends during input. For example, the input section can suggest relevant genres and characters based on keywords and topics the user has recently searched for. The input section can analyze the user's social media activity and suggest input options based on current interests. The input section can suggest trending input options based on the content of news and blogs the user subscribes to. In this way, by suggesting input options based on the user's current interests and trends, it is possible to generate works that match the user's interests. Some or all of the above processing in the input section may be performed using AI, for example, or not using AI. For example, the input section can input the user's search history data into a generating AI, and the generating AI can suggest input options based on that data.

[0040] The input field can prioritize and present highly relevant input suggestions by considering the user's geographical location during input. For example, the input field can suggest genres themed around history and culture related to the user's current location. Based on the user's geographical location, the input field can suggest local trends and popular genres. If the user is traveling, the input field can suggest stories and characters related to the place they are visiting. This allows for the generation of more relevant works by considering the user's geographical location. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field can input the user's geographical location data into a generating AI, which can then present highly relevant input suggestions based on that data.

[0041] The input field can analyze the user's social media activity during input and suggest relevant input options. For example, the input field can suggest relevant genres and characters based on posts the user has recently liked or shared. The input field can also suggest input options based on topics of interest to the user's followers and friends. Furthermore, the input field can analyze trends in online communities the user participates in and suggest relevant input options. This allows for the generation of more relevant content by analyzing the user's social media activity. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field can input the user's social media activity data into a generating AI, which can then suggest relevant input options based on that data.

[0042] The generation unit can select the optimal generation algorithm by referring to the user's past work generation history during generation. For example, the generation unit can select the optimal generation algorithm based on the genre and style of works the user has previously generated. The generation unit can select an algorithm that emphasizes a specific theme or tone from the user's past work generation history. The generation unit can select the optimal generation algorithm based on the story development the user has previously preferred. In this way, by referring to the user's past work generation history, the optimal generation algorithm can be selected and the accuracy of generation can be improved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past work generation history data into a generation AI, and the generation AI can select the optimal generation algorithm based on that data.

[0043] The generation unit can apply different generation algorithms depending on the genre or theme specified by the user during generation. For example, if the user specifies fantasy, the generation unit will apply a generation algorithm specialized for fantasy. If the user specifies mystery, the generation unit can apply a generation algorithm specialized for mystery. If the user specifies romance, the generation unit can apply a generation algorithm specialized for romance. By applying different generation algorithms depending on the genre or theme specified by the user, more appropriate works can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the genre or theme specified by the user into a generation AI, and the generation AI can apply the most suitable generation algorithm based on that data.

[0044] The generation unit can determine the generation priority based on the user's submission timing during generation. For example, if a user makes an urgent request, the generation AI will prioritize generating that work. If a user sets a specific deadline, the generation unit can determine the generation priority according to that deadline. If a user makes multiple requests, the generation unit can determine the generation order based on the submission timing. This allows the generation unit to provide works that meet the user's needs by determining the generation priority based on the user's submission timing. Some or all of the above processes in the generation unit may be performed using the generation AI, or not. For example, the generation unit can input user submission timing data into the generation AI, and the generation AI can determine the generation priority based on that data.

[0045] The generation unit can improve the accuracy of generation by referencing the user's relevant past works during the generation process. For example, the generation unit can improve the accuracy of generation based on the style and tone of works previously generated by the user. The generation unit can improve the accuracy of generation by referencing specific themes and character settings from the user's past works. The generation unit can improve the accuracy of generation based on story developments that the user has previously enjoyed. In this way, the accuracy of generation can be improved by referencing the user's relevant past works. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data of the user's past works into a generation AI, and the generation AI can improve the accuracy of generation based on that data.

[0046] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the genre and style of works the user has purchased in the past. The delivery unit can select a delivery method that emphasizes a specific theme or tone from the user's past purchase history. The delivery unit can select the optimal delivery method based on the story development that the user has preferred in the past. In this way, by referring to the user's past purchase history, the delivery unit can select the optimal delivery method and improve the accuracy of the delivery. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past purchase history data into a generating AI, and the generating AI can select the optimal delivery method based on that data.

[0047] The delivery unit can customize the delivery method based on the user's current purchasing trends at the time of delivery. For example, the delivery unit can customize the delivery method based on the genre and style of works recently purchased by the user. The delivery unit can customize the delivery method to emphasize a specific theme or tone based on the user's current purchasing trends. The delivery unit can customize the delivery method based on the story development that the user has recently enjoyed. This allows for the provision of a more appropriate delivery method by customizing the delivery method based on the user's current purchasing trends. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's current purchasing trend data into a generating AI, and the generating AI can customize the delivery method based on that data.

[0048] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location. For example, the service provider can provide works themed on history and culture related to the user's current location. Based on the user's geographical location, the service provider can provide works related to local trends and popular works. If the user is traveling, the service provider can provide works that include stories and characters related to the place they are visiting. This allows for the provision of more relevant works by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location data into a generating AI, which can then select the optimal delivery method based on that data.

[0049] The service provider can analyze the user's social media activity and suggest delivery methods at the time of delivery. For example, the service provider can provide relevant works based on posts the user has recently liked or shared. The service provider can suggest delivery methods based on topics that the user's followers and friends are interested in. The service provider can analyze trends in online communities the user participates in and provide relevant works. In this way, by analyzing the user's social media activity, it is possible to provide more relevant works. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can suggest delivery methods based on that data.

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

[0051] The reception desk can provide information on relevant past literary works, films, and television dramas based on the user's input. For example, if a user selects the mystery genre, the reception desk will display a list of famous past mystery novels and films for the user to refer to. Furthermore, if a user enters specific character settings, the reception desk can suggest similar characters from past works. This makes it easier for the user to visualize their ideas more concretely. The reception desk can also provide historical and cultural information related to a location based on the location information entered by the user. For example, if a user wants to write a novel set in Paris, the reception desk will provide information on the history and culture of Paris, allowing the user to create a more realistic setting. By providing relevant information based on the user's input, it is possible to generate higher-quality works.

[0052] The generator can produce multiple different endings based on the story details specified by the user. For example, if the user desires both a happy ending and a tragic ending, the generator can produce a novel or comic containing both endings. Furthermore, the generator can dynamically change the story's progression based on the actions and choices of the characters selected by the user. For example, if a character makes a particular choice, the story can proceed in a different direction based on that choice. The generator can also describe the setting of the story in detail based on the background setting specified by the user. For example, if the user desires a novel set in medieval Europe, the generator can describe the scenery, buildings, and costumes of that era in detail. This allows for the generation of more diverse and engaging works based on the story details specified by the user.

[0053] The service provider can offer social media integration features to allow users to easily share generated novels and comics. For example, it can provide buttons for users to share their generated works on social media. Furthermore, the service provider can also provide features that allow users to collaboratively edit generated works with other users. For example, if a user wants to create a novel with a friend, the service provider can provide a collaborative editing function, allowing multiple users to edit the work simultaneously. The service provider can also provide a function to save generated works to cloud storage. For example, it can allow users to save their generated works to cloud storage for access at any time. This makes it easier for the service provider to share, collaboratively edit, and save generated works, improving user convenience.

[0054] The service provider can provide printable formats so that users can print the generated novels and comics themselves. For example, users can download the generated works in PDF format and print them on their home printers. Furthermore, the service provider can also offer a service that allows users to order the generated works from professional printers. For example, users can have their generated works printed as hardcover books and delivered to their homes. The service provider can also provide the generated works in a format compatible with e-readers. For example, users can read the generated works on e-readers such as Kindle and Kobo. This allows the service provider to make it easier to print and view the generated works on e-readers, improving user convenience.

[0055] The service provider can offer a rating function for generated works. For example, it can allow users to rate and comment on generated novels and comics. Furthermore, the service provider can also provide a function that allows users to view ratings of works generated by other users. For example, users can view other users' works and refer to their ratings and comments. The service provider can also provide a ranking function for generated works. For example, it can display the highest-rated works or the works with the most comments in a ranking format. In this way, the service provider can make it easier for users to discover other users' works through the ratings and rankings of generated works, thereby revitalizing the community.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk takes in information such as the user's preferred genre, word count, characters, and location. For example, the user can select a genre such as mystery, romance, or science fiction, and specify a minimum and maximum word count. They can also enter information such as the names, genders, and ages of the characters, as well as location information such as city names, country names, or fictional places. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and automatically creates novels and comics based on that analysis. The generation unit analyzes the input information using natural language processing technology and machine learning algorithms, and generates novels and comics using template-based generation and neural networks. For example, it generates based on detailed information such as plot, character settings, and background settings. Step 3: The provider provides the novels and comics generated by the generator to the users. The provider can provide them in ebook or print format and sell the generated novels and comics at a low price. Furthermore, the provider can transfer the copyright of the generated works to the owner and allow them to use them for commercial purposes through sales, licensing, advertising revenue, etc.

[0058] (Example of form 2) The novel generation system according to an embodiment of the present invention is a system in which a generating AI automatically creates novels and comics based on information input by the user. This novel generation system analyzes the input information, such as the user's preferred genre, word count, characters, and location, and automatically creates novels and comics based on that information. Furthermore, by specifying story details, the system can generate novels and comics that are closer to what the user wants to read. The generated novels and comics are provided to the user and sold at a low price. By purchasing the generated novels and comics, the user can obtain works that closely match their reading preferences. Additionally, the copyright of the generated works is transferred to the owner, and commercial use is permitted. This mechanism allows users to easily create novels and comics tailored to their preferences, lowering the barrier to creativity. For example, works can be generated for various purposes, such as picture books for birthday presents or autobiographies depicting one's own life. Thus, the novel generation system can automatically generate and provide novels and comics tailored to the user's preferences.

[0059] The novel generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives information from the user, such as their preferred genre, word count, characters, and location. The reception unit allows the user to select genres such as mystery, romance, or science fiction. The reception unit also allows the user to set a range and limit on the number of characters to be entered. For example, a minimum and maximum number of characters can be specified. Furthermore, the reception unit allows the user to input information about characters. For example, information such as names, genders, and ages can be entered. The reception unit also allows the user to input information about locations. For example, a city name, country name, or fictional location can be specified. The generation unit uses a generation AI to analyze the information entered by the reception unit and automatically creates a novel or manga based on that analysis. The generation unit analyzes the input information using, for example, natural language processing technology or machine learning algorithms. The generation unit automatically creates a novel or manga using a generation algorithm. For example, the generation unit generates novels or manga using template-based generation or neural networks. The generation unit can also generate novels or manga based on story details specified by the user. For example, it generates based on detailed information such as plot, character settings, and background settings. The provider provides the novels and comics generated by the generation unit to the user. The provider can provide them in, for example, ebook format or print format. The provider can sell the generated novels and comics at a low price. For example, it can sell them by setting a price range or discount rate. The provider can transfer the copyright of the generated works to the owner. For example, it can clarify the type of copyright and the transfer procedure. The provider can use the generated works for commercial purposes. For example, it can use them for commercial purposes through sales, licensing, advertising revenue, etc. As a result, the novel generation system according to this embodiment can automatically generate and provide novels and comics tailored to the user's preferences.

[0060] The reception desk allows users to input information such as their preferred genre, word count, characters, and location. Specifically, users can choose from a variety of genres, including mystery, romance, science fiction, fantasy, and horror. This allows users to generate novels and comics in genres that suit their preferences. The reception desk can also set ranges and limits on the word count that users can input. For example, users can specify a maximum of 5,000 characters for short stories, 20,000 characters for novellas, and 50,000 characters or more for novels. Furthermore, the reception desk allows users to input detailed information about the characters. For example, they can input information such as name, gender, age, occupation, personality, and background. This allows users to create characters that match their image. Detailed information about locations can also be entered. For example, users can specify city names, country names, fictional locations, or specific place names and building names. This allows users to concretely define the setting of their story. The reception desk builds a database to centrally manage this information and pass it on to the generation department. The information entered by users is saved in real time and can be edited or modified as needed. This allows the reception department to respond to diverse user needs and collect information flexibly.

[0061] The generation unit uses generation AI to analyze information entered by the reception unit and automatically create novels and comics based on that analysis. Specifically, the generation unit uses natural language processing technology and machine learning algorithms to analyze information such as genre, word count, characters, and location entered by the user. For example, the generation AI selects an appropriate writing style and story template based on the genre selected by the user. Furthermore, it adjusts the story's development and episode length based on the word count specified by the user. Based on the character information, it generates character dialogue and actions, clarifying their roles in the story. Based on the location information, it sets the stage and describes the background of the story. The generation unit integrates this information and generates novels and comics using template-based generation or neural networks. For example, template-based generation quickly generates novels and comics by embedding user information into pre-prepared story templates. On the other hand, generation using neural networks can generate more flexible and creative stories. The generation unit can also generate novels and comics based on detailed story information specified by the user. For example, it can depict the story's development and character growth based on detailed information such as plot, character settings, and background settings. This allows the generation unit to automatically produce high-quality novels and comics tailored to the user's preferences.

[0062] The service provider provides users with novels and comics generated by the generation service provider. Specifically, the service provider can provide the generated works in ebook format or print format. In ebook format, works can be provided in common formats such as PDF, ePub, and Mobi, and users can view them on smartphones, tablets, and e-readers. In print format, physical books can be provided to users upon request using on-demand printing services. The service provider can sell the generated novels and comics at low prices. For example, they can set price ranges and discount rates to make them easily accessible to users. Furthermore, the service provider can transfer the copyright of the generated works to the owners. For example, they can clarify the types of copyrights and transfer procedures to allow users to freely use the generated works. The service provider can use the generated works for commercial purposes. For example, they can use them for commercial purposes through sales, licensing, and advertising revenue. This allows the service provider to provide generated works in a variety of ways and offer services that meet user needs. Furthermore, the service provider can collect feedback from users and use it to improve the service. For example, they can improve the generation algorithm and add new features based on user ratings and comments. This allows the service provider to consistently deliver high-quality service and improve user satisfaction.

[0063] The reception section allows users to input information specifying story details. For example, users can input detailed information such as plot, character settings, and background settings. By allowing users to specify story details, the reception section can generate novels or comics that are closer to what the user wants to read. Some or all of the above processing in the reception section may be performed using AI, or not. For example, the reception section can input the story details entered by the user into a generation AI, and the generation AI can generate a novel or comic based on that information.

[0064] The generation unit can generate novels and comics based on the details of a story specified by the user. For example, the generation unit generates novels and comics based on detailed information such as the plot, character settings, and background settings specified by the user. The generation unit uses a generation AI to generate novels and comics based on the details of a story specified by the user. For example, the generation AI develops the story based on the plot specified by the user, describes the characters based on the character settings, and constructs the scenes based on the background settings. The generation unit can use a generation AI to generate novels and comics based on the details of a story specified by the user. This allows for the provision of more personalized works by generating novels and comics based on the details of a story specified by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the detailed story information specified by the user into the generation AI, and the generation AI can generate novels and comics based on that information.

[0065] The service provider can sell the generated novels and comics at a low price. For example, the service provider can sell the generated novels and comics in e-book format or print format. The service provider can set price ranges and discount rates to sell the generated novels and comics at a low price. This allows the service provider to secure revenue by selling the generated novels and comics at a low price. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can optimize the sales price of the generated novels and comics using AI to maximize revenue.

[0066] The service provider can transfer the copyright of the generated work to the owner. For example, the service provider can transfer the copyright of a generated novel or manga to the user. The service provider can transfer the copyright of the generated work to the owner by clearly defining the type of copyright and the transfer procedure. This allows users to freely use the work by transferring the copyright of the generated work to the owner. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can automate the copyright transfer procedure for generated works using AI to perform it efficiently.

[0067] The provider can use the generated works for commercial purposes. For example, the provider can sell the generated novels or comics. The provider can also license the generated works. The provider can also earn advertising revenue based on the generated works. This allows the provider to generate further revenue by using the generated works for commercial purposes. Some or all of the above processes in the provider may be performed using AI, for example, or not using AI. For example, the provider can use AI to optimize the sales strategy for the generated works and maximize revenue.

[0068] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This improves the user's input experience by adjusting the display of the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then adjust the display of the input interface based on that data.

[0069] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display genres and characters that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest genres and characters that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI, and the generating AI can suggest the optimal input method based on that data.

[0070] The input section can suggest input options based on the user's current interests and trends during input. For example, the input section can suggest relevant genres and characters based on keywords and topics the user has recently searched for. The input section can analyze the user's social media activity and suggest input options based on current interests. The input section can suggest trending input options based on the content of news and blogs the user subscribes to. In this way, by suggesting input options based on the user's current interests and trends, it is possible to generate works that match the user's interests. Some or all of the above processing in the input section may be performed using AI, for example, or not using AI. For example, the input section can input the user's search history data into a generating AI, and the generating AI can suggest input options based on that data.

[0071] The reception desk can estimate the user's emotions and prioritize input content based on those emotions. For example, if the user is excited, the reception desk may prioritize suggesting genres with high entertainment value. If the user is calm, the reception desk may prioritize suggesting genres with deep themes or complex storylines. If the user is tired, the reception desk may prioritize suggesting genres with relaxing content. By prioritizing input content based on the user's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can then prioritize input content based on that data.

[0072] The input field can prioritize and present highly relevant input suggestions by considering the user's geographical location during input. For example, the input field can suggest genres themed around history and culture related to the user's current location. Based on the user's geographical location, the input field can suggest local trends and popular genres. If the user is traveling, the input field can suggest stories and characters related to the place they are visiting. This allows for the generation of more relevant works by considering the user's geographical location. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field can input the user's geographical location data into a generating AI, which can then present highly relevant input suggestions based on that data.

[0073] The input field can analyze the user's social media activity during input and suggest relevant input options. For example, the input field can suggest relevant genres and characters based on posts the user has recently liked or shared. The input field can also suggest input options based on topics of interest to the user's followers and friends. Furthermore, the input field can analyze trends in online communities the user participates in and suggest relevant input options. This allows for the generation of more relevant content by analyzing the user's social media activity. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field can input the user's social media activity data into a generating AI, which can then suggest relevant input options based on that data.

[0074] The generation unit can estimate the user's emotions and adjust the tone and style of the generated novel or comic based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a novel or comic with a calm tone. If the user is excited, the generation unit can generate a novel or comic that emphasizes action or suspense elements. If the user is sad, the generation unit can generate a novel or comic that includes an emotional story. In this way, by adjusting the tone and style based on the user's emotions, it is possible to generate works that better suit the user's preferences. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can adjust the tone and style of the novel or comic based on that data.

[0075] The generation unit can select the optimal generation algorithm by referring to the user's past work generation history during generation. For example, the generation unit can select the optimal generation algorithm based on the genre and style of works the user has previously generated. The generation unit can select an algorithm that emphasizes a specific theme or tone from the user's past work generation history. The generation unit can select the optimal generation algorithm based on the story development the user has previously preferred. In this way, by referring to the user's past work generation history, the optimal generation algorithm can be selected and the accuracy of generation can be improved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past work generation history data into a generation AI, and the generation AI can select the optimal generation algorithm based on that data.

[0076] The generation unit can apply different generation algorithms depending on the genre or theme specified by the user during generation. For example, if the user specifies fantasy, the generation unit will apply a generation algorithm specialized for fantasy. If the user specifies mystery, the generation unit can apply a generation algorithm specialized for mystery. If the user specifies romance, the generation unit can apply a generation algorithm specialized for romance. By applying different generation algorithms depending on the genre or theme specified by the user, more appropriate works can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the genre or theme specified by the user into a generation AI, and the generation AI can apply the most suitable generation algorithm based on that data.

[0077] The generation unit can estimate the user's emotions and adjust the length of the generated work based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise novel or comic. If the user is relaxed, the generation unit can generate a longer novel or comic with detailed explanations. If the user is excited, the generation unit can generate a novel or comic with visually stimulating effects. By adjusting the length of the work based on the user's emotions, a more appropriate work can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the length of the work based on that data.

[0078] The generation unit can determine the generation priority based on the user's submission timing during generation. For example, if a user makes an urgent request, the generation AI will prioritize generating that work. If a user sets a specific deadline, the generation unit can determine the generation priority according to that deadline. If a user makes multiple requests, the generation unit can determine the generation order based on the submission timing. This allows the generation unit to provide works that meet the user's needs by determining the generation priority based on the user's submission timing. Some or all of the above processes in the generation unit may be performed using the generation AI, or not. For example, the generation unit can input user submission timing data into the generation AI, and the generation AI can determine the generation priority based on that data.

[0079] The generation unit can improve the accuracy of generation by referencing the user's relevant past works during the generation process. For example, the generation unit can improve the accuracy of generation based on the style and tone of works previously generated by the user. The generation unit can improve the accuracy of generation by referencing specific themes and character settings from the user's past works. The generation unit can improve the accuracy of generation based on story developments that the user has previously enjoyed. In this way, the accuracy of generation can be improved by referencing the user's relevant past works. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data of the user's past works into a generation AI, and the generation AI can improve the accuracy of generation based on that data.

[0080] The service provider can estimate the user's emotions and adjust how the provided content is displayed based on those emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the display method based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the display method based on that data.

[0081] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the genre and style of works the user has purchased in the past. The delivery unit can select a delivery method that emphasizes a specific theme or tone from the user's past purchase history. The delivery unit can select the optimal delivery method based on the story development that the user has preferred in the past. In this way, by referring to the user's past purchase history, the delivery unit can select the optimal delivery method and improve the accuracy of the delivery. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past purchase history data into a generating AI, and the generating AI can select the optimal delivery method based on that data.

[0082] The delivery unit can customize the delivery method based on the user's current purchasing trends at the time of delivery. For example, the delivery unit can customize the delivery method based on the genre and style of works recently purchased by the user. The delivery unit can customize the delivery method to emphasize a specific theme or tone based on the user's current purchasing trends. The delivery unit can customize the delivery method based on the story development that the user has recently enjoyed. This allows for the provision of a more appropriate delivery method by customizing the delivery method based on the user's current purchasing trends. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's current purchasing trend data into a generating AI, and the generating AI can customize the delivery method based on that data.

[0083] The service provider can estimate the user's emotions and prioritize the content offered based on those emotions. For example, if the user is excited, the service provider may prioritize offering highly entertaining content. If the user is calm, the service provider may prioritize offering content with deep themes or complex storylines. If the user is tired, the service provider may prioritize offering content that promotes relaxation. By prioritizing content based on the user's emotions, the service provider can offer more appropriate content. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI, which can then use that data to prioritize the content offered.

[0084] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location. For example, the service provider can provide works themed on history and culture related to the user's current location. Based on the user's geographical location, the service provider can provide works related to local trends and popular works. If the user is traveling, the service provider can provide works that include stories and characters related to the place they are visiting. This allows for the provision of more relevant works by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location data into a generating AI, which can then select the optimal delivery method based on that data.

[0085] The service provider can analyze the user's social media activity and suggest delivery methods at the time of delivery. For example, the service provider can provide relevant works based on posts the user has recently liked or shared. The service provider can suggest delivery methods based on topics that the user's followers and friends are interested in. The service provider can analyze trends in online communities the user participates in and provide relevant works. In this way, by analyzing the user's social media activity, it is possible to provide more relevant works. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can suggest delivery methods based on that data.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can provide information on relevant past literary works, films, and television dramas based on the user's input. For example, if a user selects the mystery genre, the reception desk will display a list of famous past mystery novels and films for the user to refer to. Furthermore, if a user enters specific character settings, the reception desk can suggest similar characters from past works. This makes it easier for the user to visualize their ideas more concretely. The reception desk can also provide historical and cultural information related to a location based on the location information entered by the user. For example, if a user wants to write a novel set in Paris, the reception desk will provide information on the history and culture of Paris, allowing the user to create a more realistic setting. By providing relevant information based on the user's input, it is possible to generate higher-quality works.

[0088] The generator can produce multiple different endings based on the story details specified by the user. For example, if the user desires both a happy ending and a tragic ending, the generator can produce a novel or comic containing both endings. Furthermore, the generator can dynamically change the story's progression based on the actions and choices of the characters selected by the user. For example, if a character makes a particular choice, the story can proceed in a different direction based on that choice. The generator can also describe the setting of the story in detail based on the background setting specified by the user. For example, if the user desires a novel set in medieval Europe, the generator can describe the scenery, buildings, and costumes of that era in detail. This allows for the generation of more diverse and engaging works based on the story details specified by the user.

[0089] The service provider can offer social media integration features to allow users to easily share generated novels and comics. For example, it can provide buttons for users to share their generated works on social media. Furthermore, the service provider can also provide features that allow users to collaboratively edit generated works with other users. For example, if a user wants to create a novel with a friend, the service provider can provide a collaborative editing function, allowing multiple users to edit the work simultaneously. The service provider can also provide a function to save generated works to cloud storage. For example, it can allow users to save their generated works to cloud storage for access at any time. This makes it easier for the service provider to share, collaboratively edit, and save generated works, improving user convenience.

[0090] The service provider can provide printable formats so that users can print the generated novels and comics themselves. For example, users can download the generated works in PDF format and print them on their home printers. Furthermore, the service provider can also offer a service that allows users to order the generated works from professional printers. For example, users can have their generated works printed as hardcover books and delivered to their homes. The service provider can also provide the generated works in a format compatible with e-readers. For example, users can read the generated works on e-readers such as Kindle and Kobo. This allows the service provider to make it easier to print and view the generated works on e-readers, improving user convenience.

[0091] The service provider can offer a rating function for generated works. For example, it can allow users to rate and comment on generated novels and comics. Furthermore, the service provider can also provide a function that allows users to view ratings of works generated by other users. For example, users can view other users' works and refer to their ratings and comments. The service provider can also provide a ranking function for generated works. For example, it can display the highest-rated works or the works with the most comments in a ranking format. In this way, the service provider can make it easier for users to discover other users' works through the ratings and rankings of generated works, thereby revitalizing the community.

[0092] The reception system can estimate the user's emotions and adjust the input interface's colors and design based on those estimates. For example, if the user is relaxed, it can provide an interface with calming colors; if the user is stressed, it can provide a simple and calming interface design. Furthermore, if the user is excited, the reception system can provide an interface with vibrant colors and a dynamic design. The reception system can also adjust the font size and layout of the input interface according to the user's emotions. For example, if the user is tired, it can provide a larger font size and a simpler layout; if the user is focused, it can provide a layout with more detailed information. By adjusting the input interface's colors and design based on the user's emotions, a more comfortable input experience can be provided.

[0093] The generation unit can estimate the user's emotions and adjust the emotional expression of the characters in the generated work based on those estimated emotions. For example, if the user is sad, the generated novel or comic will include more scenes where the characters express sadness. Furthermore, if the user is happy, the generated work will include more scenes where the characters express joy. The generation unit can also adjust the tone and theme of the story according to the user's emotions. For example, if the user is relaxed, it will generate a story with a calm tone, and if the user is excited, it will generate a story that emphasizes action and suspense elements. In this way, by adjusting the emotional expression of the characters and the tone of the story based on the user's emotions, it is possible to generate works that evoke a greater sense of empathy.

[0094] The service provider can estimate the user's emotions and adjust the format of the content offered based on those emotions. For example, if the user is relaxed, it can offer a full-length novel or a detailed comic; if the user is in a hurry, it can offer a short novel or a concise comic. Furthermore, if the user is excited, the service provider can offer content in a visually stimulating format. The service provider can also adjust how the content is displayed according to the user's emotions. For example, if the user is tired, it can offer a simple and easy-to-read display; if the user is focused, it can offer a display that includes detailed information. In this way, by adjusting the format and display method of the content offered based on the user's emotions, the service provider can offer more appropriate content.

[0095] The service provider can estimate the user's emotions and suggest genres of content based on those emotions. For example, if the user is sad, it can suggest genres with emotionally moving stories; if the user is happy, it can suggest genres such as comedy or romance. Furthermore, if the user is relaxed, it can suggest genres with a calm tone. The service provider can also suggest themes and topics of content according to the user's emotions. For example, if the user is excited, it can suggest themes with action or suspense elements; if the user is calm, it can suggest topics with deep themes or complex storylines. By suggesting genres and themes of content based on the user's emotions, the service provider can provide more appropriate content.

[0096] The distribution team can estimate the user's emotions and adjust the promotional methods for the content based on those emotions. For example, if the user is relaxed, they can provide a promotional video with a calm tone; if the user is excited, they can provide a visually stimulating promotional video. Furthermore, if the user is sad, the distribution team can provide a promotional method that emphasizes an emotionally moving story. The distribution team can also adjust the timing and frequency of promotions according to the user's emotions. For example, if the user is busy, they can reduce the frequency of promotions; if the user has free time, they can increase the frequency. By adjusting the promotional methods for the content based on the user's emotions, more effective promotions can be achieved.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk takes in information such as the user's preferred genre, word count, characters, and location. For example, the user can select a genre such as mystery, romance, or science fiction, and specify a minimum and maximum word count. They can also enter information such as the names, genders, and ages of the characters, as well as location information such as city names, country names, or fictional places. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and automatically creates novels and comics based on that analysis. The generation unit analyzes the input information using natural language processing technology and machine learning algorithms, and generates novels and comics using template-based generation and neural networks. For example, it generates based on detailed information such as plot, character settings, and background settings. Step 3: The provider provides the novels and comics generated by the generator to the users. The provider can provide them in ebook or print format and sell the generated novels and comics at a low price. Furthermore, the provider can transfer the copyright of the generated works to the owner and allow them to use them for commercial purposes through sales, licensing, advertising revenue, etc.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs information such as genre and character count. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the input information and automatically creates a novel or comic. The provision unit is implemented by the output device 40 of the smart device 14, where the generated novel or comic is provided to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0116] 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.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs information such as genre and character count. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the input information and automatically creates a novel or comic. The provision unit is implemented by the output device 40 of the smart glasses 214, where the generated novel or comic is provided to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs information such as genre and character count. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the input information and automatically creates a novel or comic. The provision unit is implemented by the output device 40 of the headset terminal 314, where the generated novel or comic is provided to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs information such as genre and character count. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it analyzes the input information using a generation AI and automatically creates a novel or comic. The provision unit is implemented by, for example, the output device 40 of the robot 414, where it provides the generated novel or comic to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A reception area where users input information such as their preferred genre, word count, characters, and location, The generation unit analyzes the information entered by the reception unit and automatically creates novels and comics based on that information, The system comprises a supply unit that provides the novels and comics generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned reception unit is The user enters information to specify the story details. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generates novels and comics based on the story details specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Selling the generated novels and comics at a low price. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Transfer the copyright of the generated work to the owner. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Commercial use of the generated works The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When inputting text, the system suggests input options based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting text, the system prioritizes displaying highly relevant input suggestions, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the tone and style of the novels and comics generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the system selects the optimal generation algorithm by referring to the user's past work generation history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the genre or theme specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generated work based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generation priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the system references the user's related past works to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the works are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current purchasing trends. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the content offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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. A reception area where users input information such as their preferred genre, word count, characters, and location, The generation unit analyzes the information entered by the reception unit and automatically creates novels and comics based on that information, The system comprises a supply unit that provides the novels and comics generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned reception unit is The user enters information to specify the story details. The system according to feature 1.

3. The generating unit is Generates novels and comics based on the story details specified by the user. The system according to feature 1.

4. The aforementioned supply unit is, Selling the generated novels and comics at a low price. The system according to feature 1.

5. The aforementioned supply unit is, Transfer the copyright of the generated work to the owner. The system according to feature 1.

6. The aforementioned supply unit is, Commercial use of the generated works The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

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