System

Abstract

An object of a system according to an embodiment is to generate and provide a story on the basis of a request from a user.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception unit receives a request from a user. The generation unit generates a story on the basis of the request received by the reception unit. The provision unit provides the story generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to select picture books that are suitable for children, and parents have had to take the time to select picture books.

[0005] The system according to the embodiment aims to generate and provide a story based on a user's request. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a user request. The generation unit generates a story based on the request received by the reception unit. The provision unit provides the story generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide a story based on a user's request. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that generates a picture book story using a generation AI. In this system, a user requests picture book content, the generation AI generates a story based on the request, and the generated story is provided for parent-child enjoyment. For example, when a user requests picture book content, the user can input a specific theme or keywords. The generation AI generates an appropriate story based on the request and displays the generated story on a smartphone or tablet. The generated story can also be printed and used as a picture book. This reduces the effort required for selecting a picture book and promotes communication between parents and children. A system that generates picture book stories using a generation AI thus promotes communication between parents and children and reduces the effort required for selecting a picture book. For example, a user simply inputs a request, and the generation AI generates an appropriate story, enhancing reading time between parents and children and providing satisfaction for both parents and children.

[0029] A picture book generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives user requests. User requests include, but are not limited to, text input, voice input, and image input. The reception unit includes, for example, an interface through which the user inputs the theme and keywords of the picture book they desire. The generation unit generates a story based on the request received by the reception unit using a generation AI. The generation unit, for example, constructs an appropriate storyline based on the request and sets characters and scenes. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a story according to the request. The provision unit provides the story generated by the generation unit. The provision unit, for example, displays the generated story on a smartphone or tablet. The provision unit can also print the generated story and use it as a picture book. Furthermore, the provision unit includes a mechanism for collecting user feedback and allowing the generation AI to learn and improve the quality of the story. As a result, the picture book generation system according to an embodiment generates and provides a story based on a user request, thereby eliminating the hassle of selecting a picture book and promoting communication between parents and children.

[0030] The reception unit may include an interface for inputting a specific theme or keywords of a picture book desired by the user. Specific themes or keywords include, but are not limited to, animals, adventure, and friendship. The reception unit may include, for example, a text box or a drop-down menu for inputting the theme or keywords of a picture book desired by the user. The reception unit may also provide an interface for inputting the theme or keywords using voice input or image input. This allows the user to input a specific theme or keywords, thereby generating a more appropriate story. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the theme or keywords entered by the user into the generation AI and provide information for the generation AI to generate an appropriate story.

[0031] The generation unit can construct an appropriate storyline based on the request and set characters and scenes. Examples of storylines include, but are not limited to, an introduction, development, twist, and conclusion, and plot points. For example, the generation unit can construct a storyline based on the request and set characters and scenes. Examples of characters include, but are not limited to, a protagonist, an antagonist, and a supporting character. Examples of scenes include, but are not limited to, a location, time, and situation. The generation unit uses a generation AI to construct an appropriate storyline based on the request and set characters and scenes. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a story in accordance with the request. By constructing a storyline based on the request and setting characters and scenes, a story that meets the user's wishes can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's request into the generation AI, which can then construct an appropriate storyline and set characters and scenes.

[0032] The providing unit can display the generated story on a smartphone or tablet. Examples of smartphones or tablets include, but are not limited to, operating systems such as iOS and Android. The providing unit can, for example, provide an application for displaying the generated story on a smartphone or tablet. The providing unit can also display the generated story through a web browser. This allows parents and children to easily enjoy the story by displaying the generated story on a smartphone or tablet. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated story into the generation AI, which can display the story in an appropriate format.

[0033] The providing unit can print the generated story and use it as a picture book. The use as a picture book can be achieved, for example, by changing the printing format and binding method, but is not limited to these examples. For example, the providing unit can provide the generated story in PDF format, allowing the user to print it on their home printer. The providing unit can also bind the generated story through a printing service and deliver it to the user. This allows the user to print the generated story and use it as a picture book, making it enjoyable as a physical picture book. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated story into the generation AI, which can then print the story in an appropriate format.

[0034] The providing unit may include a specific mechanism for collecting user feedback and allowing the generation AI to learn and improve the quality of the story. Examples of feedback include, but are not limited to, questionnaires, reviews, and ratings. For example, the providing unit may display a questionnaire after the user reads a story to collect feedback. The providing unit may also provide an interface for users to review and rate the story. The generation AI learns based on the collected feedback and improves the quality of the story. The generation AI analyzes the feedback and optimizes the story generation algorithm using technologies such as neural networks and deep learning. This allows the generation AI to collect user feedback and learn, thereby continuously improving the quality of the story. Some or all of the above-described processing in the providing unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the providing unit may input the collected feedback into the generation AI, allowing the generation AI to learn to improve the quality of the story.

[0035] The reception unit can analyze the user's past request history and suggest a specific request method. The request history includes, for example, the content, date and time, and frequency of past requests, but is not limited to these examples. For example, the reception unit can suggest related themes based on themes previously requested by the user. The reception unit can also suggest characters for new stories based on characters previously preferred by the user. Furthermore, the reception unit can suggest an optimal story length based on the lengths of stories previously requested by the user. In this way, by analyzing the user's past request history, a more appropriate request method can be suggested. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past request history into a generation AI, which can then suggest an optimal request method.

[0036] When receiving a request, the reception unit can filter the request content based on the user's current living situation and areas of interest. Examples of living situation include, but are not limited to, family structure, occupation, and hobbies. Examples of areas of interest include, but are not limited to, sports, music, and art. For example, if the user is raising a child, the reception unit can prioritize suggesting stories that are useful for raising a child. Furthermore, if the user is participating in a specific event, the reception unit can also suggest stories related to the event. Furthermore, if the user has a specific hobby, the reception unit can also suggest stories related to the hobby. In this way, by filtering the request content based on the user's current living situation and areas of interest, more appropriate stories can be provided. Some or all of the above-mentioned processing by the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI, which can then filter the request content.

[0037] When accepting a request, the acceptance unit can select a specific acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the acceptance unit can accept the request using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the request using text analysis technology. Furthermore, if the user selects image input, the acceptance unit can also accept the request using image recognition technology. This improves the accuracy of request acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data into a generation AI, which can then select the optimal acceptance means.

[0038] When receiving a request, the reception unit can prioritize requests that are specific and highly relevant by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit can prioritize suggesting stories related to that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting stories related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize suggesting stories related to the event. This allows for providing more relevant stories by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, and the generation AI can prioritize accepting requests that are highly relevant.

[0039] When receiving a request, the reception unit can analyze the user's social media activity and suggest related requests. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, suggest stories related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related stories. Furthermore, the reception unit can also suggest related stories based on the activities of the user's friends on social media. This makes it possible to provide more relevant stories by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI, which can then suggest related requests.

[0040] When accepting a request, the reception unit can customize the specific reception method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, reviews, and ratings. The reception unit, for example, suggests the optimal request method based on the user's past feedback. The reception unit can also prioritize preferred themes and characters based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the request reception interface. This allows for a more appropriate request method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which can then customize the reception method.

[0041] When generating a story, the generation unit can adjust the level of detail of a specific storyline based on the importance of the request content. The importance of the request content includes, but is not limited to, the user's priority and the urgency of the request. For example, the generation unit can provide a detailed storyline for a request with a high level of importance. The generation unit can also provide a concise storyline for a request with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the storyline according to the request content. This allows for the generation of a more appropriate story by adjusting the level of detail of the storyline based on the importance of the request content. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input importance data of the request content into the generation AI, which can then adjust the level of detail of the storyline.

[0042] When generating a story, the generation unit can apply different generation algorithms depending on the requested theme. Examples of generation algorithms include, but are not limited to, neural networks and rule-based generation. For example, the generation unit can apply a generation algorithm that emphasizes educational content to an educational theme. Furthermore, the generation unit can also apply a generation algorithm that emphasizes fun to a theme that emphasizes entertainment. Furthermore, for a request based on a specific character, the generation unit can apply a generation algorithm that emphasizes the characteristics of that character. By applying different generation algorithms depending on the requested theme, a more appropriate story can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input requested theme data into the generation AI, which can then apply an appropriate generation algorithm.

[0043] When generating a story, the generation unit can improve the accuracy of specific generation by referring to the user's past request results. Request results include, but are not limited to, past creations and user ratings. The generation unit can generate a new story, for example, based on story patterns that the user has previously preferred. The generation unit can also analyze the user's past request results and optimize the generation algorithm. Furthermore, the generation unit can also adjust the content and tone of the story by referring to the user's past request results. This allows the accuracy of generation to be improved by referring to the user's past request results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past request result data into the generation AI, which can then improve the accuracy of generation.

[0044] When generating a story, the generation unit can determine specific generation priorities based on the time of request submission. The submission time includes, but is not limited to, the submission date and time, the submission frequency, and the like. For example, the generation unit determines the priority based on the time of submission during a time period when there are many requests. The generation unit can also prioritize requests with high urgency regardless of the time of submission. Furthermore, the generation unit can dynamically adjust the order of generation based on the time of submission. This allows for more efficient provision of stories by determining the priority of generation based on the time of request submission. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input request submission time data into the generation AI, which can then determine the priority of generation.

[0045] When generating a story, the generation unit can adjust the order of generation based on the relevance of the requests. The relevance of the requests includes, but is not limited to, for example, matching themes and the content of past requests. For example, the generation unit can prioritize highly relevant requests. The generation unit can also postpone less relevant requests. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of the requests. By adjusting the order of generation based on the relevance of the requests, a more relevant story can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input request relevance data into the generation AI, which can then adjust the order of generation.

[0046] When generating a story, the generation unit can adjust the use of technical terms in a specific story according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has specialized knowledge, the generation unit can generate a story that uses a lot of technical terms. Alternatively, if the user does not have specialized knowledge, the generation unit can generate a story that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms according to the user's level of expertise. This allows for a more appropriate story to be provided by adjusting the use of technical terms in the story according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terms in the story.

[0047] When providing a story, the providing unit can select a specific delivery method by referring to the user's past usage history. The usage history includes, for example, the number of past uses, the content of use, and ratings, but is not limited to these examples. The providing unit, for example, selects the optimal delivery method based on the user's past preferred delivery method. The providing unit can also provide the story at the optimal timing based on the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and customize the delivery method. This allows the story to be provided in a more appropriate manner by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI, which can then select the optimal delivery method.

[0048] When providing a story, the providing unit can customize the means of providing the story based on the user's current living situation. Examples of living situations include, but are not limited to, family structure, occupation, and hobbies. For example, if the user is raising a child, the providing unit can prioritize providing stories that are useful for raising the child. Furthermore, if the user is participating in a specific event, the providing unit can provide stories related to the event. Furthermore, if the user has a specific hobby, the providing unit can provide stories related to the hobby. This allows the story to be provided in a more appropriate manner by customizing the means of providing the story based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's living situation data into the generation AI, which can then customize the means of providing the story.

[0049] When providing a story, the providing unit can improve the specific delivery method by reflecting user feedback. Examples of feedback include, but are not limited to, questionnaires, reviews, and ratings. The providing unit, for example, customizes the delivery method based on user feedback. The providing unit can also adjust the content and tone of the story based on user feedback. Furthermore, the providing unit can analyze user feedback and optimize the delivery method. This allows the story to be delivered in a more appropriate manner by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI, which can then improve the delivery method.

[0050] When providing a story, the providing unit can select a specific delivery method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the providing unit can provide a story related to that area. Furthermore, if the user is traveling, the providing unit can also provide a story related to the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can also provide a story related to the event. This allows the story to be provided in a more appropriate manner by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which can then select the optimal delivery method.

[0051] When providing a story, the providing unit can analyze the user's social media activity and suggest a means of providing the story. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the providing unit can provide stories related to places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related stories. Furthermore, the providing unit can provide related stories by referring to the activities of the user's friends on social media. This makes it possible to provide stories in a more appropriate manner by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI, which can then suggest a means of providing the story.

[0052] When providing a story, the providing unit can customize the specific delivery method by reflecting the user's past feedback. Feedback includes, but is not limited to, for example, questionnaires, reviews, and ratings. The providing unit, for example, suggests the optimal delivery method based on the user's past feedback. The providing unit can also prioritize the user's preferred display method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and optimize the delivery method. This allows the story to be delivered in a more appropriate manner by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then customize the delivery method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When accepting a user's request, the accepting unit can analyze the user's past request history and suggest the content of the request. For example, it can suggest related themes and keywords based on themes and keywords requested by the user in the past. It can also suggest characters and scenes for a new story based on characters and scenes that the user liked in the past. It can also analyze the user's past request history and determine the priority of the request content. In this way, by utilizing the user's past request history, it is possible to suggest more appropriate request content and improve the accuracy of request acceptance.

[0055] When generating a story, the generation unit can adjust the level of detail of a specific storyline based on the importance of the request content. For example, a detailed storyline can be provided for a request with a high level of importance. A concise storyline can also be provided for a request with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the storyline according to the request content. This allows for the generation of a more appropriate story by adjusting the level of detail of the storyline based on the importance of the request content.

[0056] When providing a story, the providing unit can select a specific delivery method by referring to the user's past usage history. For example, the optimal method can be selected based on the delivery method that the user has preferred in the past. The unit can also provide the story at the optimal timing based on the user's past usage history. Furthermore, the unit can analyze the user's past usage history and customize the delivery method. This makes it possible to provide the story in a more appropriate way by referring to the user's past usage history.

[0057] When providing a story, the providing unit can customize the means of providing the story based on the user's current living situation. For example, if the user is raising a child, stories that are useful for raising the child can be provided preferentially. Also, if the user is participating in a specific event, stories related to the event can be provided. Furthermore, if the user has a specific hobby, stories related to the hobby can be provided. In this way, by customizing the means of providing the story based on the user's current living situation, it is possible to provide stories in a more appropriate manner.

[0058] The providing unit can improve the specific delivery method by reflecting user feedback when providing a story. For example, the delivery method can be customized based on user feedback. The content and tone of the story can also be adjusted based on user feedback. Furthermore, the delivery method can be optimized by analyzing user feedback. In this way, the story can be delivered in a more appropriate manner by reflecting user feedback.

[0059] When providing a story, the providing unit can select a specific method of providing the story by taking into consideration the user's geographical location information. For example, if the user is in a specific area, a story related to that area can be provided. Also, if the user is traveling, a story related to the travel destination can be provided. Furthermore, if the user is participating in a specific event, a story related to the event can be provided. In this way, by taking into consideration the user's geographical location information, it is possible to provide stories in a more appropriate manner.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives a user request. The user request may include text input, voice input, image input, etc. The reception unit has an interface that allows the user to input the theme and keywords of the picture book they want. Step 2: The generator uses a generation AI to generate a story based on the request received by the reception unit. The generator builds an appropriate storyline based on the request and sets up characters and scenes. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a story according to the request. Step 3: The providing unit provides the story generated by the generating unit. The providing unit displays the generated story on a smartphone or tablet. The providing unit can also print the generated story and use it as a picture book. Furthermore, the providing unit has a mechanism for collecting user feedback and allowing the generating AI to learn and improve the quality of the story.

[0062] (Example 2) A system according to an embodiment of the present invention is a system that generates a picture book story using a generation AI. In this system, a user requests picture book content, the generation AI generates a story based on the request, and the generated story is provided for parent-child enjoyment. For example, when a user requests picture book content, the user can input a specific theme or keywords. The generation AI generates an appropriate story based on the request and displays the generated story on a smartphone or tablet. The generated story can also be printed and used as a picture book. This reduces the effort required for selecting a picture book and promotes communication between parents and children. A system that generates picture book stories using a generation AI thus promotes communication between parents and children and reduces the effort required for selecting a picture book. For example, a user simply inputs a request, and the generation AI generates an appropriate story, enhancing reading time between parents and children and providing satisfaction for both parents and children.

[0063] A picture book generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives user requests. User requests include, but are not limited to, text input, voice input, and image input. The reception unit includes, for example, an interface through which the user inputs the theme and keywords of the picture book they desire. The generation unit generates a story based on the request received by the reception unit using a generation AI. The generation unit, for example, constructs an appropriate storyline based on the request and sets characters and scenes. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a story according to the request. The provision unit provides the story generated by the generation unit. The provision unit, for example, displays the generated story on a smartphone or tablet. The provision unit can also print the generated story and use it as a picture book. Furthermore, the provision unit includes a mechanism for collecting user feedback and allowing the generation AI to learn and improve the quality of the story. As a result, the picture book generation system according to an embodiment generates and provides a story based on a user request, thereby eliminating the hassle of selecting a picture book and promoting communication between parents and children.

[0064] The reception unit may include an interface for inputting a specific theme or keywords of a picture book desired by the user. Specific themes or keywords include, but are not limited to, animals, adventure, and friendship. The reception unit may include, for example, a text box or a drop-down menu for inputting the theme or keywords of a picture book desired by the user. The reception unit may also provide an interface for inputting the theme or keywords using voice input or image input. This allows the user to input a specific theme or keywords, thereby generating a more appropriate story. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the theme or keywords entered by the user into the generation AI and provide information for the generation AI to generate an appropriate story.

[0065] The generation unit can construct an appropriate storyline based on the request and set characters and scenes. Examples of storylines include, but are not limited to, an introduction, development, twist, and conclusion, and plot points. For example, the generation unit can construct a storyline based on the request and set characters and scenes. Examples of characters include, but are not limited to, a protagonist, an antagonist, and a supporting character. Examples of scenes include, but are not limited to, a location, time, and situation. The generation unit uses a generation AI to construct an appropriate storyline based on the request and set characters and scenes. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a story in accordance with the request. By constructing a storyline based on the request and setting characters and scenes, a story that meets the user's wishes can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's request into the generation AI, which can then construct an appropriate storyline and set characters and scenes.

[0066] The providing unit can display the generated story on a smartphone or tablet. Examples of smartphones or tablets include, but are not limited to, operating systems such as iOS and Android. The providing unit can, for example, provide an application for displaying the generated story on a smartphone or tablet. The providing unit can also display the generated story through a web browser. This allows parents and children to easily enjoy the story by displaying the generated story on a smartphone or tablet. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated story into the generation AI, which can display the story in an appropriate format.

[0067] The providing unit can print the generated story and use it as a picture book. The use as a picture book can be achieved, for example, by changing the printing format and binding method, but is not limited to these examples. For example, the providing unit can provide the generated story in PDF format, allowing the user to print it on their home printer. The providing unit can also bind the generated story through a printing service and deliver it to the user. This allows the user to print the generated story and use it as a picture book, making it enjoyable as a physical picture book. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated story into the generation AI, which can then print the story in an appropriate format.

[0068] The providing unit may include a specific mechanism for collecting user feedback and allowing the generation AI to learn and improve the quality of the story. Examples of feedback include, but are not limited to, questionnaires, reviews, and ratings. For example, the providing unit may display a questionnaire after the user reads a story to collect feedback. The providing unit may also provide an interface for users to review and rate the story. The generation AI learns based on the collected feedback and improves the quality of the story. The generation AI analyzes the feedback and optimizes the story generation algorithm using technologies such as neural networks and deep learning. This allows the generation AI to collect user feedback and learn, thereby continuously improving the quality of the story. Some or all of the above-described processing in the providing unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the providing unit may input the collected feedback into the generation AI, allowing the generation AI to learn to improve the quality of the story.

[0069] The reception unit can estimate the user's emotions and prioritize the request content based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can prioritize requests for relaxing stories. Furthermore, if the user is excited, the reception unit can prioritize requests for action- or adventure-themed stories. Furthermore, if the user is tired, the reception unit can prioritize requests for soothing stories. This allows for more appropriate stories to be provided by prioritizing the request content based on the user's emotions. The emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then prioritize the request content.

[0070] The reception unit can analyze the user's past request history and suggest a specific request method. The request history includes, for example, the content, date and time, and frequency of past requests, but is not limited to these examples. For example, the reception unit can suggest related themes based on themes previously requested by the user. The reception unit can also suggest characters for new stories based on characters previously preferred by the user. Furthermore, the reception unit can suggest an optimal story length based on the lengths of stories previously requested by the user. In this way, by analyzing the user's past request history, a more appropriate request method can be suggested. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past request history into a generation AI, which can then suggest an optimal request method.

[0071] When receiving a request, the reception unit can filter the request content based on the user's current living situation and areas of interest. Examples of living situation include, but are not limited to, family structure, occupation, and hobbies. Examples of areas of interest include, but are not limited to, sports, music, and art. For example, if the user is raising a child, the reception unit can prioritize suggesting stories that are useful for raising a child. Furthermore, if the user is participating in a specific event, the reception unit can also suggest stories related to the event. Furthermore, if the user has a specific hobby, the reception unit can also suggest stories related to the hobby. In this way, by filtering the request content based on the user's current living situation and areas of interest, more appropriate stories can be provided. Some or all of the above-mentioned processing by the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI, which can then filter the request content.

[0072] When accepting a request, the acceptance unit can select a specific acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the acceptance unit can accept the request using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the request using text analysis technology. Furthermore, if the user selects image input, the acceptance unit can also accept the request using image recognition technology. This improves the accuracy of request acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data into a generation AI, which can then select the optimal acceptance means.

[0073] The reception unit can estimate the user's emotions and adjust the display method of the request content based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the reception unit can provide a simple, highly visible interface. Furthermore, if the user is relaxed, the reception unit can provide an interface that includes detailed information. Furthermore, if the user is in a hurry, the reception unit can provide an interface that focuses on the main points. This allows for adjusting the display method of the request content based on the user's emotions to provide a more appropriate interface. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then adjust the display method of the request content.

[0074] When receiving a request, the reception unit can prioritize requests that are specific and highly relevant by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit can prioritize suggesting stories related to that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting stories related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize suggesting stories related to the event. This allows for providing more relevant stories by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, and the generation AI can prioritize accepting requests that are highly relevant.

[0075] When receiving a request, the reception unit can analyze the user's social media activity and suggest related requests. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, suggest stories related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related stories. Furthermore, the reception unit can also suggest related stories based on the activities of the user's friends on social media. This makes it possible to provide more relevant stories by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI, which can then suggest related requests.

[0076] When accepting a request, the reception unit can customize the specific reception method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, questionnaires, reviews, and ratings. The reception unit, for example, suggests the optimal request method based on the user's past feedback. The reception unit can also prioritize preferred themes and characters based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the request reception interface. This allows for a more appropriate request method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which can then customize the reception method.

[0077] The generation unit can estimate the user's emotions and adjust the tone and theme of the story based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the generation unit can generate a story with a calm tone. If the user is excited, the generation unit can generate a story with an action or adventure theme. If the user is sad, the generation unit can generate a story with an encouraging or comforting theme. This allows for adjusting the tone and theme of the story based on the user's emotions to generate a more appropriate story. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then adjust the tone and theme of the story.

[0078] When generating a story, the generation unit can adjust the level of detail of a specific storyline based on the importance of the request content. The importance of the request content includes, but is not limited to, the user's priority and the urgency of the request. For example, the generation unit can provide a detailed storyline for a request with a high level of importance. The generation unit can also provide a concise storyline for a request with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the storyline according to the request content. This allows for the generation of a more appropriate story by adjusting the level of detail of the storyline based on the importance of the request content. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input importance data of the request content into the generation AI, which can then adjust the level of detail of the storyline.

[0079] When generating a story, the generation unit can apply different generation algorithms depending on the requested theme. Examples of generation algorithms include, but are not limited to, neural networks and rule-based generation. For example, the generation unit can apply a generation algorithm that emphasizes educational content to an educational theme. Furthermore, the generation unit can also apply a generation algorithm that emphasizes fun to a theme that emphasizes entertainment. Furthermore, for a request based on a specific character, the generation unit can apply a generation algorithm that emphasizes the characteristics of that character. By applying different generation algorithms depending on the requested theme, a more appropriate story can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input requested theme data into the generation AI, which can then apply an appropriate generation algorithm.

[0080] When generating a story, the generation unit can improve the accuracy of specific generation by referring to the user's past request results. Request results include, but are not limited to, past creations and user ratings. The generation unit can generate a new story, for example, based on story patterns that the user has previously preferred. The generation unit can also analyze the user's past request results and optimize the generation algorithm. Furthermore, the generation unit can also adjust the content and tone of the story by referring to the user's past request results. This allows the accuracy of generation to be improved by referring to the user's past request results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past request result data into the generation AI, which can then improve the accuracy of generation.

[0081] The generation unit can estimate the user's emotions and adjust the length of the story based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the generation unit can generate a longer story if the user is relaxed. The generation unit can also generate a shorter story if the user is in a hurry. Furthermore, the generation unit can generate a story of an appropriate length if the user is excited. This allows for adjusting the length of the story based on the user's emotions, thereby providing a more appropriate story. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, 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-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then adjust the length of the story.

[0082] When generating a story, the generation unit can determine specific generation priorities based on the time of request submission. The submission time includes, but is not limited to, the submission date and time, the submission frequency, and the like. For example, the generation unit determines the priority based on the time of submission during a time period when there are many requests. The generation unit can also prioritize requests with high urgency regardless of the time of submission. Furthermore, the generation unit can dynamically adjust the order of generation based on the time of submission. This allows for more efficient provision of stories by determining the priority of generation based on the time of request submission. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input request submission time data into the generation AI, which can then determine the priority of generation.

[0083] When generating a story, the generation unit can adjust the order of generation based on the relevance of the requests. The relevance of the requests includes, but is not limited to, for example, matching themes and the content of past requests. For example, the generation unit can prioritize highly relevant requests. The generation unit can also postpone less relevant requests. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of the requests. By adjusting the order of generation based on the relevance of the requests, a more relevant story can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input request relevance data into the generation AI, which can then adjust the order of generation.

[0084] When generating a story, the generation unit can adjust the use of technical terms in a specific story according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has specialized knowledge, the generation unit can generate a story that uses a lot of technical terms. Alternatively, if the user does not have specialized knowledge, the generation unit can generate a story that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms according to the user's level of expertise. This allows for a more appropriate story to be provided by adjusting the use of technical terms in the story according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terms in the story.

[0085] The providing unit can estimate the user's emotions and adjust the story presentation method based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the providing unit can provide the story at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can also provide the story quickly. Furthermore, if the user is excited, the providing unit can also provide the story in a visually stimulating manner. This allows the story to be presented in a more appropriate manner by adjusting the story presentation method based on the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, 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-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the story presentation method.

[0086] When providing a story, the providing unit can select a specific delivery method by referring to the user's past usage history. The usage history includes, for example, the number of past uses, the content of use, and ratings, but is not limited to these examples. The providing unit, for example, selects the optimal delivery method based on the user's past preferred delivery method. The providing unit can also provide the story at the optimal timing based on the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and customize the delivery method. This allows the story to be provided in a more appropriate manner by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI, which can then select the optimal delivery method.

[0087] When providing a story, the providing unit can customize the means of providing the story based on the user's current living situation. Examples of living situations include, but are not limited to, family structure, occupation, and hobbies. For example, if the user is raising a child, the providing unit can prioritize providing stories that are useful for raising the child. Furthermore, if the user is participating in a specific event, the providing unit can provide stories related to the event. Furthermore, if the user has a specific hobby, the providing unit can provide stories related to the hobby. This allows the story to be provided in a more appropriate manner by customizing the means of providing the story based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's living situation data into the generation AI, which can then customize the means of providing the story.

[0088] When providing a story, the providing unit can improve the specific delivery method by reflecting user feedback. Examples of feedback include, but are not limited to, questionnaires, reviews, and ratings. The providing unit, for example, customizes the delivery method based on user feedback. The providing unit can also adjust the content and tone of the story based on user feedback. Furthermore, the providing unit can analyze user feedback and optimize the delivery method. This allows the story to be delivered in a more appropriate manner by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI, which can then improve the delivery method.

[0089] The providing unit can estimate the user's emotions and adjust the story display method based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the providing unit can provide a display method with calm colors. Furthermore, if the user is excited, the providing unit can provide a display method with vivid colors. Furthermore, if the user is tired, the providing unit can provide a display method with high visibility. This allows the story to be presented in a more appropriate manner by adjusting the story display method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the story display method.

[0090] When providing a story, the providing unit can select a specific delivery method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the providing unit can provide a story related to that area. Furthermore, if the user is traveling, the providing unit can also provide a story related to the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can also provide a story related to the event. This allows the story to be provided in a more appropriate manner by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which can then select the optimal delivery method.

[0091] When providing a story, the providing unit can analyze the user's social media activity and suggest a means of providing the story. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the providing unit can provide stories related to places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related stories. Furthermore, the providing unit can provide related stories by referring to the activities of the user's friends on social media. This makes it possible to provide stories in a more appropriate manner by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI, which can then suggest a means of providing the story.

[0092] When providing a story, the providing unit can customize the specific delivery method by reflecting the user's past feedback. Feedback includes, but is not limited to, for example, questionnaires, reviews, and ratings. The providing unit, for example, suggests the optimal delivery method based on the user's past feedback. The providing unit can also prioritize the user's preferred display method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and optimize the delivery method. This allows the story to be delivered in a more appropriate manner by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a user's request via a touch panel 38A or a microphone 38B. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a story based on the request using a generation AI. The provision unit is realized by the output device 40 of the smart device 14 and displays the generated story on the display 40A. The provision unit also has a function for printing the generated story and using it as a picture book. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's request as a voice input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a story based on the request using a generation AI. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated story by voice. The provision unit also has a function for printing the generated story and using it as a picture book. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives a user's request as a voice input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a story based on the request using a generation AI. The provision unit is realized by the display 343 of the headset type terminal 314 and displays the generated story. The provision unit also has a function for printing the generated story and using it as a picture book. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a user's request as a voice input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a story based on the request using a generation AI. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated story by voice. The provision unit also has a function for printing the generated story and using it as a picture book.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When accepting a user's request, the accepting unit can analyze the user's past request history and suggest the content of the request. For example, it can suggest related themes and keywords based on themes and keywords requested by the user in the past. It can also suggest characters and scenes for a new story based on characters and scenes that the user liked in the past. It can also analyze the user's past request history and determine the priority of the request content. In this way, by utilizing the user's past request history, it is possible to suggest more appropriate request content and improve the accuracy of request acceptance.

[0095] The reception unit can estimate the user's emotions and prioritize the request content based on the estimated user emotions. For example, if the user is feeling stressed, a request for a relaxing story can be prioritized. Also, if the user is excited, a request for an action- or adventure-themed story can be prioritized. Furthermore, if the user is tired, a request for a soothing story can be prioritized. In this way, by prioritizing the request content based on the user's emotions, more appropriate stories can be provided.

[0096] When generating a story, the generation unit can adjust the level of detail of a specific storyline based on the importance of the request content. For example, a detailed storyline can be provided for a request with a high level of importance. A concise storyline can also be provided for a request with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the storyline according to the request content. This allows for the generation of a more appropriate story by adjusting the level of detail of the storyline based on the importance of the request content.

[0097] The generation unit can estimate the user's emotions and adjust the tone and theme of the story based on the estimated user's emotions. For example, if the user is relaxed, a story with a calm tone can be generated. If the user is excited, a story with an action or adventure theme can be generated. Furthermore, if the user is sad, a story with an encouraging or healing theme can be generated. In this way, by adjusting the tone and theme of the story based on the user's emotions, a more appropriate story can be generated.

[0098] The providing unit can estimate the user's emotions and adjust the story providing method based on the estimated user's emotions. For example, if the user is relaxed, the story can be provided at a leisurely pace. If the user is in a hurry, the story can be provided quickly. Furthermore, if the user is excited, the story can be provided in a visually stimulating manner. In this way, by adjusting the story providing method based on the user's emotions, the story can be provided in a more appropriate manner.

[0099] When providing a story, the providing unit can select a specific delivery method by referring to the user's past usage history. For example, the optimal method can be selected based on the delivery method that the user has preferred in the past. The unit can also provide the story at the optimal timing based on the user's past usage history. Furthermore, the unit can analyze the user's past usage history and customize the delivery method. This makes it possible to provide the story in a more appropriate way by referring to the user's past usage history.

[0100] When providing a story, the providing unit can customize the means of providing the story based on the user's current living situation. For example, if the user is raising a child, stories that are useful for raising the child can be provided preferentially. Also, if the user is participating in a specific event, stories related to the event can be provided. Furthermore, if the user has a specific hobby, stories related to the hobby can be provided. In this way, by customizing the means of providing the story based on the user's current living situation, it is possible to provide stories in a more appropriate manner.

[0101] The providing unit can improve the specific delivery method by reflecting user feedback when providing a story. For example, the delivery method can be customized based on user feedback. The content and tone of the story can also be adjusted based on user feedback. Furthermore, the delivery method can be optimized by analyzing user feedback. In this way, the story can be delivered in a more appropriate manner by reflecting user feedback.

[0102] The providing unit can estimate the user's emotions and adjust the story display method based on the estimated user's emotions. For example, if the user is relaxed, a display method using gentle colors can be provided. If the user is excited, a display method using vivid colors can be provided. Furthermore, if the user is tired, a display method with high visibility can be provided. In this way, by adjusting the story display method based on the user's emotions, the story can be provided in a more appropriate manner.

[0103] When providing a story, the providing unit can select a specific method of providing the story by taking into consideration the user's geographical location information. For example, if the user is in a specific area, a story related to that area can be provided. Also, if the user is traveling, a story related to the travel destination can be provided. Furthermore, if the user is participating in a specific event, a story related to the event can be provided. In this way, by taking into consideration the user's geographical location information, it is possible to provide stories in a more appropriate manner.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives a user request. The user request may include text input, voice input, image input, etc. The reception unit has an interface that allows the user to input the theme and keywords of the picture book they want. Step 2: The generator uses a generation AI to generate a story based on the request received by the reception unit. The generator builds an appropriate storyline based on the request and sets up characters and scenes. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a story according to the request. Step 3: The providing unit provides the story generated by the generating unit. The providing unit displays the generated story on a smartphone or tablet. The providing unit can also print the generated story and use it as a picture book. Furthermore, the providing unit has a mechanism for collecting user feedback and allowing the generating AI to learn and improve the quality of the story.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a user request; a generation unit that generates a story based on the request received by the reception unit; a providing unit that provides the story generated by the generating unit. A system characterized by:

2. The reception unit Provides an interface for users to input specific themes or keywords for the picture book they want 2. The system of claim 1.

3. The generation unit Build an appropriate storyline based on the request, setting up characters and scenes 2. The system of claim 1.

4. The providing unit View the generated story on your smartphone or tablet 2. The system of claim 1.

5. The providing unit Print the generated story and use it as a picture book 2. The system of claim 1.

6. The providing unit Equipping the AI ​​with a concrete mechanism for collecting user feedback and for the AI ​​to learn and improve the quality of the story 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize the request contents based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past request history and suggest specific request methods 2. The system of claim 1.

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