system

Abstract

The system according to the embodiment aims to automatically generate and provide an attractive story based on the target's age, gender, hobbies and preferences. [Solution] A system according to an embodiment includes an input unit, a generation unit, and a provision unit. The input unit inputs information such as the target's age, gender, and hobbies and preferences. The generation unit generates a story based on the information input by the input unit. The provision unit provides the story generated by the generation unit as a script for reading aloud.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide new stories every day to help children get to sleep, leaving parents with no time to think of new stories to tell.

[0005] The system according to the embodiment aims to automatically generate and provide an attractive story based on the target's age, gender, hobbies and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, and a provision unit. The input unit inputs information such as the target's age, gender, and hobbies and preferences. The generation unit generates a story based on the information input by the input unit. The provision unit provides the story generated by the generation unit as a script for reading aloud. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and provide an attractive story based on the target's age, gender, hobbies, and preferences. [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 story generation system according to an embodiment of the present invention utilizes a generation AI to provide a new story every day to help children get to bed. This story generation system accepts input information such as the target's age, gender, and hobbies and preferences. Based on this information, the generation AI automatically generates a story that is appealing to the listener. This story is provided as a script for reading aloud. Furthermore, the generation AI supports a wide variety of languages, enabling even the general public to publish and sell picture books, novels, textbooks, and manga online worldwide. This opens up new markets. For example, the story generation system first inputs information such as the target's age, gender, and hobbies and preferences. The user then inputs detailed information about the child, such as their age, gender, favorite characters, and themes. For example, a 5-year-old girl who likes animals and adventure stories would enter this information into the generation AI. The generation AI then analyzes the input information and automatically generates an appealing story for the listener. The generation AI generates a story that matches the input information based on pre-trained data. For example, for a 5-year-old girl who likes animals, the system generates a story about animal adventures. The generated story is provided as a script for reading aloud. Users can read the story to their children based on this script. For example, if the generated story is titled "Animal's Great Adventure" and is about animals searching for treasure in the forest, the user can read the story based on that script. Furthermore, the generation AI supports a wide variety of languages. This allows stories to be provided to children who speak different languages. For example, stories can be generated in various languages, such as English, Spanish, and Chinese. Furthermore, even ordinary users can use the generation AI to publish and sell picture books, novels, textbooks, and manga online. This is expected to open up new markets. For example, users can use the generation AI to generate their own original stories and publish them as picture books. This system allows users to provide children with a new story every day to put them to bed, keeping them entertained and entertained. Furthermore, the generation AI allows ordinary users to easily generate, publish, and sell original stories, opening up new markets.This allows the story generation system to automatically generate a story based on information input by the user and provide it as a script for reading aloud.

[0029] A story generation system according to an embodiment includes an input unit, a generation unit, and a provision unit. The input unit inputs information such as a target's age, gender, and hobbies and preferences. For example, the input unit acquires information such as the age, gender, and hobbies and preferences input by a user. The user can input detailed information such as the child's age, gender, favorite characters, and themes. For example, if a user is a 5-year-old girl who likes animals and adventure stories, the user inputs this information into the input unit. The generation unit uses a generation AI to generate a story based on the information input by the input unit. The generation AI generates a story appropriate for the input information based on data learned in advance. For example, if the user is a 5-year-old girl who likes animals, the generation AI generates a story about animals going on adventures. The generation AI can generate a story using a text generation AI (e.g., GPT-4 (registered trademark)) or a multimodal generation AI. For example, the generation AI receives a prompt such as, "Please generate a story about a 5-year-old girl going on adventures with animals," and generates a story. The providing unit provides the story generated by the generating unit as a script for reading aloud. For example, the providing unit provides the generated story as a script for reading aloud. A user can read aloud to a child based on this script. For example, if the generated story is titled "Animal's Great Adventure" and is about animals going on an adventure to search for treasure in the forest, the user can read aloud based on that script. In this way, the story generation system can automatically generate a story based on information input by the user and provide it as a script for reading aloud.

[0030] The generation unit can generate a story using a generation AI. The generation unit generates a story using the generation AI. The generation AI generates a story appropriate to the input information based on pre-learned data. For example, if the generation AI is a 5-year-old girl who likes animals, it generates a story about animals going on adventures. The generation AI can generate a story using a text generation AI (e.g., GPT-4) or a multimodal generation AI. For example, the generation AI receives a prompt such as, "Generate a story about a 5-year-old girl going on adventures with animals," and generates a story. This improves the accuracy of story generation by using the generation AI. The generation AI can understand the context of the story and generate an appropriate story using, for example, natural language processing technology. The generation AI can generate a story plot and add detailed descriptions using, for example, a text generation AI. The generation AI can also generate images and audio related to the story using, for example, a multimodal generation AI. This allows the generation unit to use the generation AI to generate a story based on the user's input information.

[0031] The generation unit can generate multilingual stories using a generation AI. The generation unit generates multilingual stories using a generation AI. The generation AI generates a story appropriate for the input information based on pre-trained data. For example, the generation AI can generate stories in various languages, such as English, Spanish, and Chinese. The generation AI receives a prompt, for example, "Generate a story in English about a 5-year-old girl's adventures with animals," and generates a story. This allows multilingual stories to be generated for children who speak different languages. The generation AI can understand the context of the story and generate an appropriate story, for example, using natural language processing technology. The generation AI can generate a story plot and add detailed descriptions, for example, using a text generation AI. The generation AI can also generate images and audio related to the story, for example, using a multimodal generation AI. This allows the generation unit to generate multilingual stories using the generation AI.

[0032] The providing unit can provide the generated story as a script for reading aloud. The providing unit provides the story generated by the generating unit as a script for reading aloud. For example, the providing unit provides the generated story as a script for reading aloud. A user can read aloud to a child based on this script. For example, if the generated story is titled "Animal's Great Adventure" and is about animals going on an adventure to search for treasure in the forest, the reading can be performed based on that script. By providing the generated story as a script for reading aloud, the user can easily read aloud. The providing unit can also provide the generated story in the form of an e-book, for example. The providing unit can also provide the generated story by printing it, for example. The providing unit can also provide the generated story as audio data, for example. This allows the providing unit to provide the generated story as a script for reading aloud.

[0033] The providing unit can provide the generated story as data for publishing or selling online. The providing unit provides the story generated by the generating unit as data for publishing or selling online. For example, the providing unit can provide the generated story in an e-book format. For example, the providing unit can also provide the generated story in print. For example, the providing unit can also provide the generated story as audio data. This makes it possible to develop a new market by providing the generated story as data for publishing or selling online. For example, the providing unit can also provide the generated story in a PDF format. For example, the providing unit can also provide the generated story as a webpage. For example, the providing unit can also provide the generated story in a video format. This makes it possible for the providing unit to provide the generated story as data for publishing or selling online.

[0034] The input unit can acquire information such as age, gender, and hobbies and preferences input by the user. The input unit acquires information such as age, gender, and hobbies and preferences input by the user. For example, the input unit acquires information such as age, gender, and hobbies and preferences input by the user. The user can input detailed information such as the child's age, gender, favorite characters, and themes. For example, if a child is a 5-year-old girl who likes animals and adventure stories, she inputs this information into the input unit. By acquiring the information input by the user, a more appropriate story can be generated. The input unit can acquire information, for example, in the form of a questionnaire. The input unit can acquire information through profile settings, for example. The input unit can also acquire information using voice input, for example. This allows the input unit to acquire information such as age, gender, and hobbies and preferences input by the user.

[0035] The input unit can analyze the user's past input history and select the optimal input method. The input unit analyzes the user's past input history and selects the optimal input method. For example, the input unit preferentially suggests input methods (such as voice input and text input) that the user has frequently used in the past. The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can customize the input method based on information previously input by the user. In this way, the input method optimal for the user can be provided by analyzing the past input history. For example, the input unit analyzes past input data and identifies frequently used input methods. For example, the input unit analyzes the user's input history in chronological order and suggests the optimal input method for a specific time period. The input unit customizes the input method based on the user's past input content. In this way, the input unit can analyze the user's past input history and select the optimal input method.

[0036] The input unit can filter information based on the user's current living situation and areas of interest at the time of input. The input unit can filter information based on the user's current living situation and areas of interest at the time of input. For example, the input unit prioritizes input of relevant information based on the user's current living situation (work, family, etc.). The input unit can also filter and input relevant information based on the user's areas of interest (hobbies, interests, etc.). Furthermore, the input unit can combine the user's current living situation and areas of interest to input optimal information. In this way, by filtering information based on the user's living situation and areas of interest, more relevant information can be input. The input unit acquires the user's living situation and areas of interest, for example, in the form of a questionnaire. The input unit identifies the user's areas of interest, for example, through social media analysis. The input unit filters information based on the user's profile settings, for example. In this way, the input unit can filter information based on the user's current living situation and areas of interest at the time of input.

[0037] The input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information at the time of input. The input unit prioritizes inputting highly relevant information in consideration of the user's geographical location information at the time of input. For example, the input unit prioritizes inputting relevant information based on the user's current location. Furthermore, when the user is in a specific area, the input unit can also prioritize inputting information related to that area. Furthermore, when the user is moving, the input unit can input optimal information based on the user's current location. In this way, more relevant information can be input by taking the user's geographical location information into consideration. The input unit identifies the user's current location using, for example, GPS data. The input unit estimates the user's geographical location using, for example, an IP address. The input unit acquires geographical location information based on, for example, the user's profile settings. In this way, the input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information at the time of input.

[0038] The input unit can analyze the user's social media activity at the time of input and input relevant information. The input unit can analyze the user's social media activity at the time of input and input relevant information. For example, the input unit can analyze the content of the user's social media posts and input relevant information. The input unit can also input relevant information based on the user's social media following and like history. Furthermore, the input unit can input optimal information taking into account the time period during which the user is active on social media. In this way, more relevant information can be input by analyzing the user's social media activity. The input unit can, for example, acquire the content of the user's posts using a social media API. The input unit can, for example, analyze the user's following and like history using a social media analysis tool. The input unit can, for example, identify the time period during which the user is active on social media and input optimal information. In this way, the input unit can analyze the user's social media activity at the time of input and input relevant information.

[0039] The generation unit can adjust the level of detail of the story based on the age and gender of the target when generating the story. The generation unit can adjust the level of detail of the story based on the age and gender of the target when generating the story. For example, the generation unit generates a simple and easy-to-understand story for young children. The generation unit can also generate a slightly more complex and interesting story for elementary school students. The generation unit can also generate a story including a deep theme or a complex plot for junior high and high school students. In this way, by adjusting the level of detail of the story based on the age and gender of the target, a more appropriate story can be provided. For example, the generation unit adjusts the level of detail of the story based on the age. The generation unit adjusts the character settings of the story based on the gender. For example, the generation unit adjusts the amount of information in the story based on the age and gender. In this way, the generation unit can adjust the level of detail of the story based on the age and gender of the target when generating the story.

[0040] The generation unit can apply different generation algorithms depending on the interests and preferences of the target when generating a story. The generation unit can apply different generation algorithms depending on the interests and preferences of the target when generating a story. For example, the generation unit can generate a story with an animal as the main character for a child who likes animals. The generation unit can also generate an adventure-themed story for a child who likes adventure. The generation unit can also generate a story set in a magical or other world for a child who likes fantasy. In this way, by applying a generation algorithm depending on the interests and preferences of the target, a more attractive story can be provided. The generation unit can, for example, use a rule-based generation algorithm to generate a story based on a specific theme. The generation unit can, for example, use a machine learning-based generation algorithm to generate a story that suits the interests and preferences of the user. The generation unit can, for example, use a hybrid generation algorithm to generate a story that corresponds to a plurality of interests and preferences. In this way, the generation unit can apply different generation algorithms depending on the interests and preferences of the target when generating a story.

[0041] The generation unit can determine the priority of stories based on the target input time when generating a story. The generation unit can determine the priority of stories based on the target input time when generating a story. For example, the generation unit can prioritize generating relaxing stories before going to bed at night. The generation unit can also prioritize generating uplifting stories in the morning. The generation unit can also prioritize generating longer stories on holidays. In this way, by determining the priority of stories based on the target input time, more appropriate stories can be provided. The generation unit identifies the input time using, for example, a timestamp. The generation unit acquires the input time using, for example, calendar information. The generation unit identifies the input time based on, for example, a user's profile settings. In this way, the generation unit can determine the priority of stories based on the target input time when generating a story.

[0042] The generation unit can adjust the order of stories based on the relevance of the targets when generating stories. The generation unit adjusts the order of stories based on the relevance of the targets when generating stories. For example, the generation unit preferentially generates highly relevant stories based on the user's past story history. The generation unit can also preferentially generate highly relevant stories based on the user's current interests. Furthermore, the generation unit can also preferentially generate highly rated stories based on the user's past ratings. In this way, by adjusting the order of stories based on the relevance of the targets, more appropriate stories can be provided. The generation unit adjusts the order of stories based on, for example, chronological order. The generation unit determines the order of stories based on, for example, importance. The generation unit adjusts the order of stories based on, for example, user ratings. In this way, the generation unit can adjust the order of stories based on the relevance of the targets when generating stories.

[0043] The providing unit can adjust the level of detail provided based on the importance of the generated story at the time of providing. The providing unit adjusts the level of detail provided based on the importance of the generated story at the time of providing. For example, the providing unit provides a script including a detailed explanation for an important story. Furthermore, the providing unit can provide a concise script for a general story. Furthermore, the providing unit can provide a script that covers the main points for a short story. In this way, by adjusting the level of detail provided based on the importance of the generated story, more appropriate information can be provided. For example, the providing unit determines the importance of the story based on a user's evaluation. For example, the providing unit evaluates the importance of the story based on the number of views. For example, the providing unit adjusts the importance of the story based on user feedback. In this way, the providing unit can adjust the level of detail provided based on the importance of the generated story at the time of providing.

[0044] The providing unit can apply different providing methods depending on the category of the generated story when providing the information. The providing unit can apply different providing methods depending on the category of the generated story when providing the information. For example, in the case of an adventure story, the providing unit can provide a script with a visually stimulating design. In addition, in the case of an educational story, the providing unit can provide a script with an easy-to-understand design. Furthermore, in the case of a fantasy story, the providing unit can provide a script with a fantastical design. In this way, by applying different providing methods depending on the category of the generated story, more appropriate information can be provided. For example, the providing unit classifies the category of the story based on genre. For example, the providing unit determines the category of the story based on theme. For example, the providing unit adjusts the category of the story based on user evaluation. In this way, the providing unit can apply different providing methods depending on the category of the generated story when providing the information.

[0045] The providing unit can determine the priority of provision based on the submission time of the generated stories at the time of provision. The providing unit can determine the priority of provision based on the submission time of the generated stories at the time of provision. For example, the providing unit can prioritize providing relaxing stories before going to bed at night. The providing unit can also prioritize providing uplifting stories in the morning hours. The providing unit can also prioritize providing longer stories on holidays. In this way, by determining the priority of provision based on the submission time of the generated stories, more appropriate information can be provided. The providing unit identifies the submission time using, for example, a timestamp. The providing unit acquires the submission time using, for example, calendar information. The providing unit identifies the submission time based on, for example, a user's profile settings. In this way, the providing unit can determine the priority of provision based on the submission time of the generated stories at the time of provision.

[0046] The providing unit can adjust the order of providing stories based on the relevance of the generated stories at the time of providing. The providing unit adjusts the order of providing stories based on the relevance of the generated stories at the time of providing. For example, the providing unit prioritizes providing highly relevant stories based on the user's past story history. The providing unit can also prioritize providing highly relevant stories based on the user's current interests. Furthermore, the providing unit can also prioritize providing highly rated stories based on the user's past ratings. In this way, by adjusting the order of providing stories based on the relevance of the generated stories, more appropriate information can be provided. The providing unit adjusts the order of providing based on, for example, chronological order. The providing unit determines the order of providing based on, for example, importance. The providing unit adjusts the order of providing based on, for example, user rating. In this way, the providing unit can adjust the order of providing stories based on the relevance of the generated stories at the time of providing.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The generator can analyze the user's past story generation history and suggest story themes based on the user's preferences. For example, the generator can prioritize suggesting themes that the user has previously liked (adventure, fantasy, animals, etc.). The generator can also generate new stories that incorporate elements of stories that the user has previously rated. Furthermore, the generator can customize the content of the story based on the user's past feedback. This makes it possible to provide stories that meet the user's preferences.

[0049] The provider may automatically synchronize the generated story to the user's device. For example, the provider may synchronize the story to the user's device, such as a smartphone, tablet, or PC. The provider may also synchronize the progress so that the user can continue reading the story on different devices. Furthermore, the provider may download the story to the device so that the user can read the story offline. This allows the user to enjoy the story anytime, anywhere.

[0050] The providing unit can automatically share the generated story to the user's social media account. For example, the providing unit posts the user's generated story to social media such as Facebook, Twitter, and Instagram. The providing unit can also generate a link that the user can use to share the story with friends and family. Furthermore, the providing unit can provide a comment function that allows the user to receive feedback on the story. This allows the user to easily share the generated story and enjoy it with others.

[0051] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can prioritize and suggest input methods (voice input, text input, etc.) that the user has frequently used in the past. The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can customize the input method based on information the user has previously entered. In this way, the input unit can provide the optimal input method for the user by analyzing the past input history.

[0052] When generating a story, the generator can adjust the content of the story based on the user's current lifestyle. For example, if the user is busy, the generator can generate a short and concise story. If the user is relaxed, the generator can generate a long and detailed story. Furthermore, if the user is celebrating a specific event (such as a birthday or a holiday), the generator can generate a story related to that event. This allows the generator to provide a more appropriate story by adjusting the content of the story according to the user's lifestyle.

[0053] The providing unit can customize the generated story based on the user's preferences. For example, the providing unit can provide a story that incorporates characters or themes that the user likes. The providing unit can also provide a new story that reflects elements of stories that the user has previously evaluated. Furthermore, the providing unit can adjust the content of the story based on user feedback. This makes it possible to provide a story that is customized according to the user's preferences.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The input unit inputs information such as the target's age, gender, and hobbies and preferences. For example, the input unit acquires information such as age, gender, and hobbies and preferences entered by the user. The user can enter detailed information such as the child's age, gender, favorite characters, and themes. Step 2: The generation unit uses a generation AI to generate a story based on the information input by the input unit. The generation AI generates a story that suits the input information based on data it has learned in advance. For example, if a 5-year-old girl likes animals, it will generate a story about animal adventures. The generation AI can generate stories using text generation AI (e.g., GPT-4) or multimodal generation AI. Step 3: The providing unit provides the story generated by the generating unit as a script for reading aloud. For example, if the generated story is titled "Animal's Great Adventure" and is about animals going on an adventure to find treasure in the forest, the reading will be based on that script.

[0056] (Example 2) A story generation system according to an embodiment of the present invention utilizes a generation AI to provide a new story every day to help children get to bed. This story generation system accepts input information such as the target's age, gender, and hobbies and preferences. Based on this information, the generation AI automatically generates a story that is appealing to the listener. This story is provided as a script for reading aloud. Furthermore, the generation AI supports a wide variety of languages, enabling even the general public to publish and sell picture books, novels, textbooks, and manga online worldwide. This opens up new markets. For example, the story generation system first inputs information such as the target's age, gender, and hobbies and preferences. The user then inputs detailed information about the child, such as their age, gender, favorite characters, and themes. For example, a 5-year-old girl who likes animals and adventure stories would enter this information into the generation AI. The generation AI then analyzes the input information and automatically generates an appealing story for the listener. The generation AI generates a story that matches the input information based on pre-trained data. For example, for a 5-year-old girl who likes animals, the system generates a story about animal adventures. The generated story is provided as a script for reading aloud. Users can read the story to their children based on this script. For example, if the generated story is titled "Animal's Great Adventure" and is about animals searching for treasure in the forest, the user can read the story based on that script. Furthermore, the generation AI supports a wide variety of languages. This allows stories to be provided to children who speak different languages. For example, stories can be generated in various languages, such as English, Spanish, and Chinese. Furthermore, even ordinary users can use the generation AI to publish and sell picture books, novels, textbooks, and manga online. This is expected to open up new markets. For example, users can use the generation AI to generate their own original stories and publish them as picture books. This system allows users to provide children with a new story every day to put them to bed, keeping them entertained and entertained. Furthermore, the generation AI allows ordinary users to easily generate, publish, and sell original stories, opening up new markets.This allows the story generation system to automatically generate a story based on information input by the user and provide it as a script for reading aloud.

[0057] A story generation system according to an embodiment includes an input unit, a generation unit, and a providing unit. The input unit inputs information such as a target's age, gender, and hobbies and preferences. For example, the input unit acquires information such as the age, gender, and hobbies and preferences input by a user. The user can input detailed information such as the child's age, gender, favorite characters, and themes. For example, if a user is a 5-year-old girl who likes animals and adventure stories, the user inputs this information into the input unit. The generation unit uses a generation AI to generate a story based on the information input by the input unit. The generation AI generates a story appropriate for the input information based on data learned in advance. For example, if the target is a 5-year-old girl who likes animals, the generation AI generates a story about animals going on adventures. The generation AI can generate a story using a text generation AI (e.g., GPT-4) or a multimodal generation AI. For example, the generation AI receives a prompt such as, "Please generate a story about a 5-year-old girl going on adventures with animals," and generates a story. The providing unit provides the story generated by the generation unit as a script for reading aloud. For example, the providing unit provides the generated story as a script for reading aloud. The user can read the story to a child based on this script. For example, if the generated story is titled "Animal's Great Adventure" and is about animals going on an adventure to search for treasure in the forest, the story is read aloud based on that script. In this way, the story generation system can automatically generate a story based on information input by the user and provide it as a script for reading aloud.

[0058] The generation unit can generate a story using a generation AI. The generation unit generates a story using the generation AI. The generation AI generates a story appropriate to the input information based on pre-learned data. For example, if the generation AI is a 5-year-old girl who likes animals, it generates a story about animals going on adventures. The generation AI can generate a story using a text generation AI (e.g., GPT-4) or a multimodal generation AI. For example, the generation AI receives a prompt such as, "Generate a story about a 5-year-old girl going on adventures with animals," and generates a story. This improves the accuracy of story generation by using the generation AI. The generation AI can understand the context of the story and generate an appropriate story using, for example, natural language processing technology. The generation AI can generate a story plot and add detailed descriptions using, for example, a text generation AI. The generation AI can also generate images and audio related to the story using, for example, a multimodal generation AI. This allows the generation unit to use the generation AI to generate a story based on the user's input information.

[0059] The generation unit can generate multilingual stories using a generation AI. The generation unit generates multilingual stories using a generation AI. The generation AI generates a story appropriate for the input information based on pre-trained data. For example, the generation AI can generate stories in various languages, such as English, Spanish, and Chinese. The generation AI receives a prompt, for example, "Generate a story in English about a 5-year-old girl's adventures with animals," and generates a story. This allows multilingual stories to be generated for children who speak different languages. The generation AI can understand the context of the story and generate an appropriate story, for example, using natural language processing technology. The generation AI can generate a story plot and add detailed descriptions, for example, using a text generation AI. The generation AI can also generate images and audio related to the story, for example, using a multimodal generation AI. This allows the generation unit to generate multilingual stories using the generation AI.

[0060] The providing unit can provide the generated story as a script for reading aloud. The providing unit provides the story generated by the generating unit as a script for reading aloud. For example, the providing unit provides the generated story as a script for reading aloud. A user can read aloud to a child based on this script. For example, if the generated story is titled "Animal's Great Adventure" and is about animals going on an adventure to search for treasure in the forest, the reading can be performed based on that script. By providing the generated story as a script for reading aloud, the user can easily read aloud. The providing unit can also provide the generated story in the form of an e-book, for example. The providing unit can also provide the generated story by printing it, for example. The providing unit can also provide the generated story as audio data, for example. This allows the providing unit to provide the generated story as a script for reading aloud.

[0061] The providing unit can provide the generated story as data for publishing or selling online. The providing unit provides the story generated by the generating unit as data for publishing or selling online. For example, the providing unit can provide the generated story in an e-book format. For example, the providing unit can also provide the generated story in print. For example, the providing unit can also provide the generated story as audio data. This makes it possible to develop a new market by providing the generated story as data for publishing or selling online. For example, the providing unit can also provide the generated story in a PDF format. For example, the providing unit can also provide the generated story as a webpage. For example, the providing unit can also provide the generated story in a video format. This makes it possible for the providing unit to provide the generated story as data for publishing or selling online.

[0062] The input unit can acquire information such as age, gender, and hobbies and preferences input by the user. The input unit acquires information such as age, gender, and hobbies and preferences input by the user. For example, the input unit acquires information such as age, gender, and hobbies and preferences input by the user. The user can input detailed information such as the child's age, gender, favorite characters, and themes. For example, if a child is a 5-year-old girl who likes animals and adventure stories, she inputs this information into the input unit. By acquiring the information input by the user, a more appropriate story can be generated. The input unit can acquire information, for example, in the form of a questionnaire. The input unit can acquire information through profile settings, for example. The input unit can also acquire information using voice input, for example. This allows the input unit to acquire information such as age, gender, and hobbies and preferences input by the user.

[0063] The input unit can estimate the user's emotions and adjust the timing of the input based on the estimated user emotions. The input unit estimates the user's emotions and adjusts the timing of the input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit delays the timing of the input to allow the user to input in a relaxed state. Also, if the user is relaxed, the input unit accelerates the timing of the input to allow the user to input smoothly. Furthermore, if the user is in a hurry, the input unit optimizes the timing of the input to allow the user to input information quickly. This allows the input of information at a more appropriate timing by adjusting the input timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0064] The input unit can analyze the user's past input history and select the optimal input method. The input unit analyzes the user's past input history and selects the optimal input method. For example, the input unit preferentially suggests input methods (such as voice input and text input) that the user has frequently used in the past. The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can customize the input method based on information previously input by the user. In this way, the input method optimal for the user can be provided by analyzing the past input history. For example, the input unit analyzes past input data and identifies frequently used input methods. For example, the input unit analyzes the user's input history in chronological order and suggests the optimal input method for a specific time period. The input unit customizes the input method based on the user's past input content. In this way, the input unit can analyze the user's past input history and select the optimal input method.

[0065] The input unit can filter information based on the user's current living situation and areas of interest at the time of input. The input unit can filter information based on the user's current living situation and areas of interest at the time of input. For example, the input unit prioritizes input of relevant information based on the user's current living situation (work, family, etc.). The input unit can also filter and input relevant information based on the user's areas of interest (hobbies, interests, etc.). Furthermore, the input unit can combine the user's current living situation and areas of interest to input optimal information. In this way, by filtering information based on the user's living situation and areas of interest, more relevant information can be input. The input unit acquires the user's living situation and areas of interest, for example, in the form of a questionnaire. The input unit identifies the user's areas of interest, for example, through social media analysis. The input unit filters information based on the user's profile settings, for example. In this way, the input unit can filter information based on the user's current living situation and areas of interest at the time of input.

[0066] The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, when the user is stressed, the input unit can prioritize inputting important information. Furthermore, when the user is relaxed, the input unit can prioritize inputting detailed information. Furthermore, when the user is in a hurry, the input unit can prioritize inputting the minimum necessary information. This allows more appropriate information to be input by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the input unit can be performed using, for example, an AI. For example, the input unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0067] The input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information at the time of input. The input unit prioritizes inputting highly relevant information in consideration of the user's geographical location information at the time of input. For example, the input unit prioritizes inputting relevant information based on the user's current location. Furthermore, when the user is in a specific area, the input unit can also prioritize inputting information related to that area. Furthermore, when the user is moving, the input unit can input optimal information based on the user's current location. In this way, more relevant information can be input by taking the user's geographical location information into consideration. The input unit identifies the user's current location using, for example, GPS data. The input unit estimates the user's geographical location using, for example, an IP address. The input unit acquires geographical location information based on, for example, the user's profile settings. In this way, the input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information at the time of input.

[0068] The input unit can analyze the user's social media activity at the time of input and input relevant information. The input unit can analyze the user's social media activity at the time of input and input relevant information. For example, the input unit can analyze the content of the user's social media posts and input relevant information. The input unit can also input relevant information based on the user's social media following and like history. Furthermore, the input unit can input optimal information taking into account the time period during which the user is active on social media. In this way, more relevant information can be input by analyzing the user's social media activity. The input unit can, for example, acquire the content of the user's posts using a social media API. The input unit can, for example, analyze the user's following and like history using a social media analysis tool. The input unit can, for example, identify the time period during which the user is active on social media and input optimal information. In this way, the input unit can analyze the user's social media activity at the time of input and input relevant information.

[0069] The generation unit can estimate the user's emotions and adjust the story presentation method based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the story presentation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a story in a calm tone. If the user is excited, the generation unit can generate a story in an exciting tone. If the user is sad, the generation unit can generate a story in a comforting tone. This allows the story presentation method to be adjusted according to the user's emotions, providing a more appealing story. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0070] The generation unit can adjust the level of detail of the story based on the age and gender of the target when generating the story. The generation unit can adjust the level of detail of the story based on the age and gender of the target when generating the story. For example, the generation unit generates a simple and easy-to-understand story for young children. The generation unit can also generate a slightly more complex and interesting story for elementary school students. The generation unit can also generate a story including a deep theme or a complex plot for junior high and high school students. In this way, by adjusting the level of detail of the story based on the age and gender of the target, a more appropriate story can be provided. For example, the generation unit adjusts the level of detail of the story based on the age. The generation unit adjusts the character settings of the story based on the gender. For example, the generation unit adjusts the amount of information in the story based on the age and gender. In this way, the generation unit can adjust the level of detail of the story based on the age and gender of the target when generating the story.

[0071] The generation unit can apply different generation algorithms depending on the interests and preferences of the target when generating a story. The generation unit can apply different generation algorithms depending on the interests and preferences of the target when generating a story. For example, the generation unit can generate a story with an animal as the main character for a child who likes animals. The generation unit can also generate an adventure-themed story for a child who likes adventure. The generation unit can also generate a story set in a magical or other world for a child who likes fantasy. In this way, by applying a generation algorithm depending on the interests and preferences of the target, a more attractive story can be provided. The generation unit can, for example, use a rule-based generation algorithm to generate a story based on a specific theme. The generation unit can, for example, use a machine learning-based generation algorithm to generate a story that suits the interests and preferences of the user. The generation unit can, for example, use a hybrid generation algorithm to generate a story that corresponds to a plurality of interests and preferences. In this way, the generation unit can apply different generation algorithms depending on the interests and preferences of the target when generating a story.

[0072] The generation unit can estimate the user's emotions and adjust the length of the story based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the length of the story based on the estimated user emotions. For example, the generation unit can generate a short story when the user is tired. The generation unit can also generate a longer story when the user is relaxed. Furthermore, the generation unit can generate a short story that focuses on the main points when the user is in a hurry. This allows for adjusting the length of the story according to the user's emotions to provide 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 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-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] The generation unit can determine the priority of stories based on the target input time when generating a story. The generation unit can determine the priority of stories based on the target input time when generating a story. For example, the generation unit can prioritize generating relaxing stories before going to bed at night. The generation unit can also prioritize generating uplifting stories in the morning. The generation unit can also prioritize generating longer stories on holidays. In this way, by determining the priority of stories based on the target input time, more appropriate stories can be provided. The generation unit identifies the input time using, for example, a timestamp. The generation unit acquires the input time using, for example, calendar information. The generation unit identifies the input time based on, for example, a user's profile settings. In this way, the generation unit can determine the priority of stories based on the target input time when generating a story.

[0074] The generation unit can adjust the order of stories based on the relevance of the targets when generating stories. The generation unit adjusts the order of stories based on the relevance of the targets when generating stories. For example, the generation unit preferentially generates highly relevant stories based on the user's past story history. The generation unit can also preferentially generate highly relevant stories based on the user's current interests. Furthermore, the generation unit can also preferentially generate highly rated stories based on the user's past ratings. In this way, by adjusting the order of stories based on the relevance of the targets, more appropriate stories can be provided. The generation unit adjusts the order of stories based on, for example, chronological order. The generation unit determines the order of stories based on, for example, importance. The generation unit adjusts the order of stories based on, for example, user ratings. In this way, the generation unit can adjust the order of stories based on the relevance of the targets when generating stories.

[0075] The providing unit can estimate the user's emotions and adjust the display method of the script based on the estimated user's emotions. The providing unit can estimate the user's emotions and adjust the display method of the script based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can display the script in calm colors. Also, if the user is excited, the providing unit can display the script in vivid colors. Furthermore, if the user is tired, the providing unit can display the script in a simple design with high visibility. This allows for more appropriate display by adjusting the display method of the script according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The providing unit can adjust the level of detail provided based on the importance of the generated story at the time of providing. The providing unit adjusts the level of detail provided based on the importance of the generated story at the time of providing. For example, the providing unit provides a script including a detailed explanation for an important story. Furthermore, the providing unit can provide a concise script for a general story. Furthermore, the providing unit can provide a script that covers the main points for a short story. In this way, by adjusting the level of detail provided based on the importance of the generated story, more appropriate information can be provided. For example, the providing unit determines the importance of the story based on a user's evaluation. For example, the providing unit evaluates the importance of the story based on the number of views. For example, the providing unit adjusts the importance of the story based on user feedback. In this way, the providing unit can adjust the level of detail provided based on the importance of the generated story at the time of providing.

[0077] The providing unit can apply different providing methods depending on the category of the generated story when providing the information. The providing unit can apply different providing methods depending on the category of the generated story when providing the information. For example, in the case of an adventure story, the providing unit can provide a script with a visually stimulating design. In addition, in the case of an educational story, the providing unit can provide a script with an easy-to-understand design. Furthermore, in the case of a fantasy story, the providing unit can provide a script with a fantastical design. In this way, by applying different providing methods depending on the category of the generated story, more appropriate information can be provided. For example, the providing unit classifies the category of the story based on genre. For example, the providing unit determines the category of the story based on theme. For example, the providing unit adjusts the category of the story based on user evaluation. In this way, the providing unit can apply different providing methods depending on the category of the generated story when providing the information.

[0078] The providing unit can estimate the user's emotions and adjust the length of the script based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the length of the script based on the estimated user emotions. For example, the providing unit can provide a short script when the user is tired. The providing unit can also provide a longer script when the user is relaxed. Furthermore, the providing unit can also provide a short script that focuses on the main points when the user is in a hurry. This allows for adjusting the length of the script according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The providing unit can determine the priority of provision based on the submission time of the generated stories at the time of provision. The providing unit can determine the priority of provision based on the submission time of the generated stories at the time of provision. For example, the providing unit can prioritize providing relaxing stories before going to bed at night. The providing unit can also prioritize providing uplifting stories in the morning hours. The providing unit can also prioritize providing longer stories on holidays. In this way, by determining the priority of provision based on the submission time of the generated stories, more appropriate information can be provided. The providing unit identifies the submission time using, for example, a timestamp. The providing unit acquires the submission time using, for example, calendar information. The providing unit identifies the submission time based on, for example, a user's profile settings. In this way, the providing unit can determine the priority of provision based on the submission time of the generated stories at the time of provision.

[0080] The providing unit can adjust the order of providing stories based on the relevance of the generated stories at the time of providing. The providing unit adjusts the order of providing stories based on the relevance of the generated stories at the time of providing. For example, the providing unit prioritizes providing highly relevant stories based on the user's past story history. The providing unit can also prioritize providing highly relevant stories based on the user's current interests. Furthermore, the providing unit can also prioritize providing highly rated stories based on the user's past ratings. In this way, by adjusting the order of providing stories based on the relevance of the generated stories, more appropriate information can be provided. The providing unit adjusts the order of providing based on, for example, chronological order. The providing unit determines the order of providing based on, for example, importance. The providing unit adjusts the order of providing based on, for example, user rating. In this way, the providing unit can adjust the order of providing stories based on the relevance of the generated stories at the time of providing. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and the user inputs information such as the child's age, gender, hobbies, and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a story based on the input information using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated story as a script to be read aloud. === Hard Collateral 1-2 === Each of the multiple elements including the input unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, and the user inputs information such as the child's age, gender, hobbies, and preferences by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a story based on the input information using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated story by voice as a script to be read aloud. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input 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 input unit is realized by the microphone 238 of the headset-type terminal 314, and the user vocally inputs information such as the child's age, gender, hobbies, and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a story based on the input information using a generation AI. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and displays the generated story as a script for reading aloud. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and the user inputs information such as the child's age, gender, hobbies, and preferences by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a story based on the input information using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated story by voice as a script to be read aloud.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The input unit can analyze the user's tone of voice and speaking style to estimate the user's emotions. For example, if the user is excited, the input unit can display a prompt requesting more detailed information. If the user is calm, the input unit can display a concise prompt. Furthermore, if the user is tired, the input unit can provide an option to simplify input. This allows for smoother operation by adjusting the input method according to the user's emotions.

[0083] The generator can analyze the user's past story generation history and suggest story themes based on the user's preferences. For example, the generator can prioritize suggesting themes that the user has previously liked (adventure, fantasy, animals, etc.). The generator can also generate new stories that incorporate elements of stories that the user has previously rated. Furthermore, the generator can customize the content of the story based on the user's past feedback. This makes it possible to provide stories that meet the user's preferences.

[0084] When generating a story, the generation unit can adjust the tone of the story based on the user's current mood. For example, if the user is relaxed, the generation unit can generate a story in a calm tone. If the user is excited, the generation unit can generate a story in an exciting tone. Furthermore, if the user is sad, the generation unit can generate a story in a comforting tone. In this way, by adjusting the tone of the story according to the user's emotions, a more attractive story can be provided.

[0085] The provider may automatically synchronize the generated story to the user's device. For example, the provider may synchronize the story to the user's device, such as a smartphone, tablet, or PC. The provider may also synchronize the progress so that the user can continue reading the story on different devices. Furthermore, the provider may download the story to the device so that the user can read the story offline. This allows the user to enjoy the story anytime, anywhere.

[0086] The providing unit can automatically share the generated story to the user's social media account. For example, the providing unit posts the user's generated story to social media such as Facebook, Twitter, and Instagram. The providing unit can also generate a link that the user can use to share the story with friends and family. Furthermore, the providing unit can provide a comment function that allows the user to receive feedback on the story. This allows the user to easily share the generated story and enjoy it with others.

[0087] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can prioritize and suggest input methods (voice input, text input, etc.) that the user has frequently used in the past. The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can customize the input method based on information the user has previously entered. In this way, the input unit can provide the optimal input method for the user by analyzing the past input history.

[0088] The input unit can estimate the user's emotions and adjust the timing of input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can delay the timing of input to allow input in a relaxed state. Also, if the user is relaxed, the input unit can speed up the timing of input to allow input to proceed smoothly. Furthermore, if the user is in a hurry, the input unit can optimize the timing of input to allow information to be input quickly. In this way, by adjusting the timing of input according to the user's emotions, information can be input at a more appropriate timing.

[0089] When generating a story, the generator can adjust the content of the story based on the user's current lifestyle. For example, if the user is busy, the generator can generate a short and concise story. If the user is relaxed, the generator can generate a long and detailed story. Furthermore, if the user is celebrating a specific event (such as a birthday or a holiday), the generator can generate a story related to that event. This allows the generator to provide a more appropriate story by adjusting the content of the story according to the user's lifestyle.

[0090] The providing unit can estimate the user's emotions and adjust the display method of the script based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can display the script in calm colors. If the user is excited, the providing unit can display the script in vivid colors. Furthermore, if the user is tired, the providing unit can display the script in a simple design with high visibility. This allows for more appropriate display by adjusting the display method of the script according to the user's emotions.

[0091] The providing unit can customize the generated story based on the user's preferences. For example, the providing unit can provide a story that incorporates characters or themes that the user likes. The providing unit can also provide a new story that reflects elements of stories that the user has previously evaluated. Furthermore, the providing unit can adjust the content of the story based on user feedback. This makes it possible to provide a story that is customized according to the user's preferences.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The input unit inputs information such as the target's age, gender, and hobbies and preferences. For example, the input unit acquires information such as age, gender, and hobbies and preferences entered by the user. The user can enter detailed information such as the child's age, gender, favorite characters, and themes. Step 2: The generation unit uses a generation AI to generate a story based on the information input by the input unit. The generation AI generates a story that suits the input information based on data it has learned in advance. For example, if a 5-year-old girl likes animals, it will generate a story about animal adventures. The generation AI can generate stories using text generation AI (e.g., GPT-4) or multimodal generation AI. Step 3: The providing unit provides the story generated by the generating unit as a script for reading aloud. For example, if the generated story is titled "Animal's Great Adventure" and is about animals going on an adventure to find treasure in the forest, the reading will be based on that script.

[0094] 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.

[0095] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting information such as the target's age, gender, and hobbies and preferences; a generation unit that generates a story based on the information input by the input unit; a providing unit that provides the story generated by the generating unit as a script for reading aloud; Equipped with A system characterized by:

2. The generation unit Generate stories using generative AI 2. The system of claim 1.

3. The generation unit Generative AI generates multilingual stories 2. The system of claim 1.

4. The providing unit Providing the generated story as a script for reading aloud 2. The system of claim 1.

5. The providing unit Providing the generated stories as data for publishing and online sales 2. The system of claim 1.

6. The input unit includes: Obtain information about the user's age, gender, and hobbies and preferences 2. The system of claim 1.

7. The input unit includes: Estimate the user's emotions and adjust the timing of input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit includes: Analyze the user's past input history and select the appropriate input method 2. The system of claim 1.

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