System

Abstract

An object of the system according to the embodiment is to modify an expression in accordance with the level of an infant and automatically generate an element that attracts interest.SOLUTION: A system includes a correction unit, a generation unit, and a provision unit. The correction unit analyzes the content and corrects the content to an expression suitable for the level of the reader. The generator automatically generates an element that interests the child based on the content corrected by the corrector. The provision unit provides the content generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, content for young children was not provided in a way that suited their level, and there was a lack of elements that would pique their interest.

[0005] The system according to the embodiment aims to automatically generate elements that attract the interest of young children by modifying the expressions to suit their level. [Means for solving the problem]

[0006] The system according to the embodiment includes a correction unit, a generation unit, and a provision unit. The correction unit analyzes content and modifies it to suit the level of the reader. The generation unit automatically generates elements that will interest young children based on the content modified by the correction unit. The provision unit provides the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can modify expressions to suit the level of young children and automatically generate elements that will attract their interest. [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 platform according to an embodiment of the present invention addresses the problem that, while the widespread use of the Internet has enabled access to a variety of content from an early age, the provided content is rarely expressed in a way that is appropriate for the level of young children, and there is no mechanism for engaging the content itself. This platform includes a correction unit that analyzes content and modifies it to suit the reader's level, a generation unit that automatically generates elements that will attract young children's interest based on the content modified by the correction unit, and a provision unit that provides the content generated by the generation unit. For example, the platform inputs content provided by a user into an LLM (large-scale language model). The LLM then analyzes the content and modifies it to suit the reader's level. For example, the LLM replaces difficult words and technical terms with simpler terms and simplifies sentence structures to make the content easier for young children to understand. Furthermore, the LLM automatically generates elements that will attract young children's interest. For example, by adding colorful illustrations, animations, audio guides, and the like, the platform provides content that young children can enjoy while learning. This allows the platform to provide content that is both interesting and easy to understand for young children. The platform allows young children to learn while having fun with content appropriate for their level, thereby acquiring knowledge. It also makes it easier for parents and educators to provide content appropriate for young children.

[0029] A platform according to an embodiment includes a correction unit, a generation unit, and a provision unit. The correction unit analyzes content and corrects it to expressions appropriate for the reader's level. For example, the correction unit replaces difficult words or technical terms with simpler words. The correction unit can also simplify sentence structure. The correction unit can also include a determination unit that determines the reader's level. For example, the determination unit determines the reader's level based on criteria such as age, educational level, and reading comprehension. The generation unit automatically generates elements that attract the interest of young children based on the content corrected by the correction unit. For example, the generation unit can include an illustration generation unit that generates colorful illustrations. The generation unit can also include an audio guide generation unit that generates audio guides. The provision unit provides the content generated by the generation unit. For example, the provision unit can include a distribution unit that distributes the generated content. As a result, the platform according to an embodiment can automatically generate and provide expressions appropriate for young children and elements that attract their interest.

[0030] The correction unit may include a determination unit that determines the level of the reader. The determination unit determines the level of the reader based on criteria such as age, education level, and reading comprehension. For example, the determination unit determines the level based on the reader's age. The determination unit may also determine the level based on the reader's education level. The determination unit may also determine the level based on the reader's reading comprehension. This enables appropriate correction according to the reader's level.

[0031] The generation unit may include an illustration generation unit that generates colorful illustrations. The illustration generation unit generates colorful illustrations based on, for example, the types of colors used and the content of the illustration. For example, the illustration generation unit generates illustrations using colorful colors. The illustration generation unit can also generate illustrations with content that will interest young children. The illustration generation unit can also dynamically display the generated illustrations. This makes it possible to automatically generate colorful illustrations that will attract the interest of young children.

[0032] The generation unit may include an audio guide generation unit that generates an audio guide. The audio guide generation unit generates the audio guide using, for example, speech synthesis technology. For example, the audio guide generation unit generates the audio guide using simple words that are easy for young children to understand. The audio guide generation unit can also provide the generated audio guide in multiple languages. The audio guide generation unit can also add background music to the audio guide. This makes it possible to provide the audio guide to young children and help them understand.

[0033] The providing unit may include a distribution unit that distributes the generated content. The distribution unit distributes the generated content via the Internet, for example. For example, the distribution unit distributes the content via a website or a mobile app. The distribution unit may also distribute the generated content by email. The distribution unit may also share the generated content on social media. This allows the generated content to be distributed efficiently.

[0034] The correction unit can analyze the reader's past learning history and select an appropriate correction method. For example, the correction unit retrieves the reader's past learning history from a database and analyzes it. For example, the correction unit prioritizes expressions that the reader found easy to understand in the past. The correction unit can also avoid expressions that the reader found difficult in the past and replace them with other expressions. The correction unit can also add expressions that complement knowledge in a specific field from the reader's learning history. This makes it possible to make optimal corrections based on the reader's past learning history.

[0035] The editing unit may filter content based on the reader's current interests when editing the content. For example, the editing unit may analyze the reader's recent search history or browsing history to identify the current interests. For example, the editing unit may preferentially provide information related to topics in which the reader is currently interested. The editing unit may also omit information about topics in which the reader is not interested. The editing unit may also add related topics based on the reader's current interests. This allows content to be edited according to the reader's interests.

[0036] When correcting content, the correction unit can select an appropriate correction method depending on the reader's input method. For example, if the reader is using voice input, the correction unit uses concise expressions that are suitable for voice. For example, the correction unit uses short sentences that are suitable for voice input. Furthermore, if the reader is using text input, the correction unit can also add detailed explanations. Furthermore, if the reader is using image input, the correction unit can also use expressions that are visually easy to understand. This enables optimal corrections to be made depending on the reader's input method.

[0037] When revising content, the revision unit can prioritize revisions that are highly relevant, taking into account the reader's geographical location information. The revision unit, for example, acquires the reader's GPS data or IP address to identify the geographical location information. For example, if the reader is in a specific area, the revision unit can prioritize revisions to information related to that area. Also, if the reader is traveling, the revision unit can prioritize revisions to information related to the reader's travel destination. Also, the revision unit can prioritize revisions to information related to the area where the reader lives. This enables revisions that are highly relevant based on the reader's geographical location information.

[0038] When revising content, the revision unit can analyze the reader's social media activity and make relevant revisions. The revision unit, for example, analyzes the content of the reader's social media posts and the number of likes. For example, the revision unit makes revisions based on the information the reader has shared on social media. The revision unit can also analyze the content of the reader's social media posts and make relevant revisions. The revision unit can also make revisions based on the activity of the reader's friends on social media. This enables highly relevant revisions based on the reader's social media activity.

[0039] When revising content, the correction unit can customize the revision method by reflecting the reader's past feedback. For example, the correction unit adjusts the revision method based on the reader's past feedback. For example, the correction unit makes revisions to avoid specific expressions based on the reader's past feedback. The correction unit can also analyze the reader's feedback and suggest the optimal revision method. This makes it possible to make optimal revisions based on the reader's past feedback.

[0040] The generator can adjust the level of detail of the generated content based on the importance of the content during generation. For example, the generator evaluates the importance of the content and draws illustrations related to important information in detail. For example, the generator simplifies illustrations related to less important information. The generator can also provide detailed audio guidance related to important information. This makes it possible to generate optimal elements according to the importance of the content.

[0041] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit applies a generation algorithm specialized for education to educational content. For example, the generation unit applies a generation algorithm specialized for entertainment to entertainment content. The generation unit can also apply a generation algorithm specialized for news to news content. This makes it possible to apply the optimal generation algorithm depending on the content category.

[0042] During generation, the generation unit can improve the accuracy of generation by referring to the reader's past generation results. For example, the generation unit generates by referring to the illustration style that the reader has previously preferred. For example, the generation unit generates by referring to the tone of the audio guide that the reader has previously preferred. The generation unit can also analyze the reader's past generation results and suggest the optimal generation method. This makes it possible to generate optimal content based on the reader's past generation results.

[0043] The generation unit can determine the generation priority based on the submission time of the content at the time of generation. For example, the generation unit evaluates the submission deadline of the content and generates content with a deadline approaching preferentially. For example, the generation unit postpones content with a more distant submission time. The generation unit can also adjust the level of detail of generation depending on the submission time. This enables optimal generation prioritization according to the submission time of the content.

[0044] The generator can adjust the order of generation based on the relevance of the content during generation. For example, the generator evaluates the similarity of the content topics and related keywords, and generates highly relevant content with priority. For example, the generator postpones less relevant content. The generator can also adjust the level of detail of generation according to the relevance. This enables optimal generation ordering according to the relevance of the content.

[0045] The generator may adjust the use of technical terminology during generation according to the reader's level of expertise. The generator may evaluate the level of expertise based on criteria such as the reader's qualifications, educational background, and work history. For example, if the reader is a beginner, the generator may avoid technical terminology and use simple language. If the reader is an intermediate learner, the generator may use technical terminology appropriately. If the reader is an advanced learner, the generator may use a lot of technical terminology to provide detailed explanations. This allows the use of optimal technical terminology according to the reader's level of expertise.

[0046] When providing the information, the providing unit can select the optimal display method by referring to the reader's past operation history. The providing unit, for example, obtains and analyzes the reader's click history and browsing history from a database. For example, the providing unit preferentially provides the display method that the reader has preferred in the past. The providing unit can also suggest the optimal display method based on the reader's past operation history. The providing unit can also analyze the reader's operation history and provide a display method with high visibility. This makes it possible to provide the optimal display method based on the reader's past operation history.

[0047] The providing unit can customize the display content according to the reader's current task when providing the content. The providing unit, for example, identifies the reader's work content and progress, and provides display content according to the current task. For example, if the reader is studying, the providing unit can prioritize displaying information related to studying. Also, if the reader is relaxing, the providing unit can also prioritize displaying information related to entertainment. The providing unit can also provide optimal display content according to the reader's current task. This makes it possible to display optimal content according to the reader's current task.

[0048] When providing the information, the providing unit can select the optimal display method by taking into consideration the reader's device information. The providing unit, for example, acquires and analyzes the reader's device type and OS version. For example, if the reader is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the reader is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the reader is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method based on the reader's device information.

[0049] When providing the information, the providing unit can select the optimal display method by taking into consideration the reader's device information. The providing unit, for example, acquires and analyzes the reader's device type and OS version. For example, if the reader is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the reader is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the reader is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method based on the reader's device information.

[0050] The providing unit can make the displayed content multilingual according to the reader's language setting at the time of providing. The providing unit, for example, obtains the language setting of the reader's device and automatically translates the displayed content. For example, the providing unit provides a language switching function when the reader uses multiple languages. Furthermore, if the reader selects a specific language, the providing unit can also provide the displayed content in that language. This makes it possible to provide multilingual support based on the reader's language setting.

[0051] At the time of providing the information, the providing unit can analyze the reader's social media activity and provide relevant information. The providing unit, for example, analyzes the content of the reader's social media posts and the number of likes. For example, the providing unit can provide relevant content based on the information the reader has shared on social media. The providing unit can also analyze the content of the reader's social media posts and provide relevant information. The providing unit can also provide relevant information by taking into account the activities of the reader's friends on social media. This makes it possible to provide highly relevant information based on the reader's social media activity.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The correction department can analyze the reader's learning style and select the most appropriate correction method. For example, if the reader is a visual learner, the correction department can make extensive use of diagrams and illustrations. If the reader is an auditory learner, the correction department can also emphasize audio guides. If the reader is an experiential learner, the correction department can also add interactive elements. This allows for optimal correction according to the reader's learning style.

[0054] The redaction unit can modify the content taking into account the reader's cultural background. For example, the redaction unit uses examples and metaphors that are appropriate for the reader's culture. The redaction unit can also add historical background and examples that are relevant to the reader's culture. The redaction unit can also use language that is considerate of the reader's culture. This allows the content to be modified optimally according to the reader's cultural background.

[0055] The generator can monitor the reader's learning progress in real time and adjust the content it generates. For example, if the reader is struggling with a particular topic, it can generate supplementary materials related to that topic. If the reader is progressing smoothly, it can generate content for the next level. If the reader excels in a particular area, it can generate advanced content related to that area. This makes it possible to generate optimal content according to the reader's learning progress.

[0056] The providing unit can adjust the content providing method taking into account the remaining battery level of the reader's device. For example, if the remaining battery level is low, lightweight text content can be provided. If the remaining battery level is sufficient, rich media content can be provided. If the remaining battery level is medium, balanced content can be provided. This makes it possible to provide optimal content according to the remaining battery level of the reader's device.

[0057] The editing department can monitor the reader's health status and select the appropriate editing method. For example, if the reader is tired, it can use simple expressions. If the reader is concentrating, it can add detailed explanations. If the reader is stressed, it can use expressions that help the reader relax. This allows it to make the most appropriate edits according to the reader's health status.

[0058] The provider can adjust the content delivery method taking into account the reader's internet connection status. For example, if the connection is unstable, it can provide content that can be viewed offline. If the connection is stable, it can provide streaming content. If the connection speed is slow, it can provide lightweight content. This makes it possible to provide optimal content according to the reader's internet connection status.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The correction unit analyzes the content and corrects it to suit the reader's level. For example, difficult words or technical terms are replaced with simpler words, and sentence structures are simplified. The correction unit may also include a judgment unit that determines the reader's level, which is determined based on criteria such as age, education level, and reading comprehension. Step 2: The generation unit automatically generates elements that attract the interest of young children based on the content corrected by the correction unit. For example, the generation unit may include an illustration generation unit that generates colorful illustrations and an audio guide generation unit that generates audio guides. Step 3: The providing unit provides the content generated by the generating unit. For example, the providing unit may include a distributing unit that distributes the generated content.

[0061] (Example 2) A platform according to an embodiment of the present invention addresses the problem that, while the widespread use of the Internet has enabled access to a variety of content from an early age, the provided content is rarely expressed in a way that is appropriate for the level of young children, and there is no mechanism for engaging the content itself. This platform includes a correction unit that analyzes content and modifies it to suit the reader's level, a generation unit that automatically generates elements that will attract young children's interest based on the content modified by the correction unit, and a provision unit that provides the content generated by the generation unit. For example, the platform inputs content provided by a user into an LLM (large-scale language model). The LLM then analyzes the content and modifies it to suit the reader's level. For example, the LLM replaces difficult words and technical terms with simpler terms and simplifies sentence structures to make the content easier for young children to understand. Furthermore, the LLM automatically generates elements that will attract young children's interest. For example, by adding colorful illustrations, animations, audio guides, and the like, the platform provides content that young children can enjoy while learning. This allows the platform to provide content that is both interesting and easy to understand for young children. The platform allows young children to learn while having fun with content appropriate for their level, thereby acquiring knowledge. It also makes it easier for parents and educators to provide content appropriate for young children.

[0062] A platform according to an embodiment includes a correction unit, a generation unit, and a provision unit. The correction unit analyzes content and corrects it to expressions appropriate for the reader's level. For example, the correction unit replaces difficult words or technical terms with simpler words. The correction unit can also simplify sentence structure. The correction unit can also include a determination unit that determines the reader's level. For example, the determination unit determines the reader's level based on criteria such as age, educational level, and reading comprehension. The generation unit automatically generates elements that attract the interest of young children based on the content corrected by the correction unit. For example, the generation unit can include an illustration generation unit that generates colorful illustrations. The generation unit can also include an audio guide generation unit that generates audio guides. The provision unit provides the content generated by the generation unit. For example, the provision unit can include a distribution unit that distributes the generated content. As a result, the platform according to an embodiment can automatically generate and provide expressions appropriate for young children and elements that attract their interest.

[0063] The correction unit may include a determination unit that determines the level of the reader. The determination unit determines the level of the reader based on criteria such as age, education level, and reading comprehension. For example, the determination unit determines the level based on the reader's age. The determination unit may also determine the level based on the reader's education level. The determination unit may also determine the level based on the reader's reading comprehension. This enables appropriate correction according to the reader's level.

[0064] The generation unit may include an illustration generation unit that generates colorful illustrations. The illustration generation unit generates colorful illustrations based on, for example, the types of colors used and the content of the illustration. For example, the illustration generation unit generates illustrations using colorful colors. The illustration generation unit can also generate illustrations with content that will interest young children. The illustration generation unit can also dynamically display the generated illustrations. This makes it possible to automatically generate colorful illustrations that will attract the interest of young children.

[0065] The generation unit may include an audio guide generation unit that generates an audio guide. The audio guide generation unit generates the audio guide using, for example, speech synthesis technology. For example, the audio guide generation unit generates the audio guide using simple words that are easy for young children to understand. The audio guide generation unit can also provide the generated audio guide in multiple languages. The audio guide generation unit can also add background music to the audio guide. This makes it possible to provide the audio guide to young children and help them understand.

[0066] The providing unit may include a distribution unit that distributes the generated content. The distribution unit distributes the generated content via the Internet, for example. For example, the distribution unit distributes the content via a website or a mobile app. The distribution unit may also distribute the generated content by email. The distribution unit may also share the generated content on social media. This allows the generated content to be distributed efficiently.

[0067] The correction unit can estimate the user's emotions and adjust the content correction method based on the estimated user emotions. For example, the correction unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the correction unit calculates an emotion score based on changes in facial expressions. The correction unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the correction unit analyzes the tone and speed of the voice and calculates an emotion score. The correction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the correction unit calculates an emotion score based on heart rate fluctuations. This enables optimal content correction based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The correction unit can analyze the reader's past learning history and select an appropriate correction method. For example, the correction unit retrieves the reader's past learning history from a database and analyzes it. For example, the correction unit prioritizes expressions that the reader found easy to understand in the past. The correction unit can also avoid expressions that the reader found difficult in the past and replace them with other expressions. The correction unit can also add expressions that complement knowledge in a specific field from the reader's learning history. This makes it possible to make optimal corrections based on the reader's past learning history.

[0069] The editing unit may filter content based on the reader's current interests when editing the content. For example, the editing unit may analyze the reader's recent search history or browsing history to identify the current interests. For example, the editing unit may preferentially provide information related to topics in which the reader is currently interested. The editing unit may also omit information about topics in which the reader is not interested. The editing unit may also add related topics based on the reader's current interests. This allows content to be edited according to the reader's interests.

[0070] When correcting content, the correction unit can select an appropriate correction method depending on the reader's input method. For example, if the reader is using voice input, the correction unit uses concise expressions that are suitable for voice. For example, the correction unit uses short sentences that are suitable for voice input. Furthermore, if the reader is using text input, the correction unit can also add detailed explanations. Furthermore, if the reader is using image input, the correction unit can also use expressions that are visually easy to understand. This enables optimal corrections to be made depending on the reader's input method.

[0071] The correction unit can estimate the user's emotions and determine the priority of content to be modified based on the estimated user emotions. For example, the correction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the correction unit calculates an emotion score based on changes in facial expression. The correction unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the correction unit analyzes the tone and speed of the voice and calculates an emotion score. The correction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the correction unit calculates an emotion score based on heart rate fluctuations. This enables optimal content modification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] When revising content, the revision unit can prioritize revisions that are highly relevant, taking into account the reader's geographical location information. The revision unit, for example, acquires the reader's GPS data or IP address to identify the geographical location information. For example, if the reader is in a specific area, the revision unit can prioritize revisions to information related to that area. Also, if the reader is traveling, the revision unit can prioritize revisions to information related to the reader's travel destination. Also, the revision unit can prioritize revisions to information related to the area where the reader lives. This enables revisions that are highly relevant based on the reader's geographical location information.

[0073] When revising content, the revision unit can analyze the reader's social media activity and make relevant revisions. The revision unit, for example, analyzes the content of the reader's social media posts and the number of likes. For example, the revision unit makes revisions based on the information the reader has shared on social media. The revision unit can also analyze the content of the reader's social media posts and make relevant revisions. The revision unit can also make revisions based on the activity of the reader's friends on social media. This enables highly relevant revisions based on the reader's social media activity.

[0074] When revising content, the correction unit can customize the revision method by reflecting the reader's past feedback. For example, the correction unit adjusts the revision method based on the reader's past feedback. For example, the correction unit makes revisions to avoid specific expressions based on the reader's past feedback. The correction unit can also analyze the reader's feedback and suggest the optimal revision method. This makes it possible to make optimal revisions based on the reader's past feedback.

[0075] The generation unit can estimate the user's emotion and adjust the expression method of the generated element based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to generate optimal elements according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The generator can adjust the level of detail of the generated content based on the importance of the content during generation. For example, the generator evaluates the importance of the content and draws illustrations related to important information in detail. For example, the generator simplifies illustrations related to less important information. The generator can also provide detailed audio guidance related to important information. This makes it possible to generate optimal elements according to the importance of the content.

[0077] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit applies a generation algorithm specialized for education to educational content. For example, the generation unit applies a generation algorithm specialized for entertainment to entertainment content. The generation unit can also apply a generation algorithm specialized for news to news content. This makes it possible to apply the optimal generation algorithm depending on the content category.

[0078] During generation, the generation unit can improve the accuracy of generation by referring to the reader's past generation results. For example, the generation unit generates by referring to the illustration style that the reader has previously preferred. For example, the generation unit generates by referring to the tone of the audio guide that the reader has previously preferred. The generation unit can also analyze the reader's past generation results and suggest the optimal generation method. This makes it possible to generate optimal content based on the reader's past generation results.

[0079] The generation unit can estimate the user's emotion and adjust the length of the generated element based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to adjust the optimal element length according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The generation unit can determine the generation priority based on the submission time of the content at the time of generation. For example, the generation unit evaluates the submission deadline of the content and generates content with a deadline approaching preferentially. For example, the generation unit postpones content with a more distant submission time. The generation unit can also adjust the level of detail of generation depending on the submission time. This enables optimal generation prioritization according to the submission time of the content.

[0081] The generator can adjust the order of generation based on the relevance of the content during generation. For example, the generator evaluates the similarity of the content topics and related keywords, and generates highly relevant content with priority. For example, the generator postpones less relevant content. The generator can also adjust the level of detail of generation according to the relevance. This enables optimal generation ordering according to the relevance of the content.

[0082] The generator may adjust the use of technical terminology during generation according to the reader's level of expertise. The generator may evaluate the level of expertise based on criteria such as the reader's qualifications, educational background, and work history. For example, if the reader is a beginner, the generator may avoid technical terminology and use simple language. If the reader is an intermediate learner, the generator may use technical terminology appropriately. If the reader is an advanced learner, the generator may use a lot of technical terminology to provide detailed explanations. This allows the use of optimal technical terminology according to the reader's level of expertise.

[0083] The providing unit can estimate the user's emotion and adjust the display method of the content to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This enables optimal content display according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When providing the information, the providing unit can select the optimal display method by referring to the reader's past operation history. The providing unit, for example, obtains and analyzes the reader's click history and browsing history from a database. For example, the providing unit preferentially provides the display method that the reader has preferred in the past. The providing unit can also suggest the optimal display method based on the reader's past operation history. The providing unit can also analyze the reader's operation history and provide a display method with high visibility. This makes it possible to provide the optimal display method based on the reader's past operation history.

[0085] The providing unit can customize the display content according to the reader's current task when providing the content. The providing unit, for example, identifies the reader's work content and progress, and provides display content according to the current task. For example, if the reader is studying, the providing unit can prioritize displaying information related to studying. Also, if the reader is relaxing, the providing unit can also prioritize displaying information related to entertainment. The providing unit can also provide optimal display content according to the reader's current task. This makes it possible to display optimal content according to the reader's current task.

[0086] When providing the information, the providing unit can select the optimal display method by taking into consideration the reader's device information. The providing unit, for example, acquires and analyzes the reader's device type and OS version. For example, if the reader is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the reader is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the reader is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method based on the reader's device information.

[0087] The providing unit can estimate the user's emotions and adjust the operation procedures of the content to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This enables optimal operation procedures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When providing the information, the providing unit can select the optimal display method by taking into consideration the reader's device information. The providing unit, for example, acquires and analyzes the reader's device type and OS version. For example, if the reader is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the reader is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the reader is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method based on the reader's device information.

[0089] The providing unit can make the displayed content multilingual according to the reader's language setting at the time of providing. The providing unit, for example, obtains the language setting of the reader's device and automatically translates the displayed content. For example, the providing unit provides a language switching function when the reader uses multiple languages. Furthermore, if the reader selects a specific language, the providing unit can also provide the displayed content in that language. This makes it possible to provide multilingual support based on the reader's language setting.

[0090] At the time of providing the information, the providing unit can analyze the reader's social media activity and provide relevant information. The providing unit, for example, analyzes the content of the reader's social media posts and the number of likes. For example, the providing unit can provide relevant content based on the information the reader has shared on social media. The providing unit can also analyze the content of the reader's social media posts and provide relevant information. The providing unit can also provide relevant information by taking into account the activities of the reader's friends on social media. This makes it possible to provide highly relevant information based on the reader's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned correction unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the correction unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned correction unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the correction unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned correction 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 correction unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned correction unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the correction unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The correction department can analyze the reader's learning style and select the most appropriate correction method. For example, if the reader is a visual learner, the correction department can make extensive use of diagrams and illustrations. If the reader is an auditory learner, the correction department can also emphasize audio guides. If the reader is an experiential learner, the correction department can also add interactive elements. This allows for optimal correction according to the reader's learning style.

[0093] The correction unit can estimate the reader's emotions and adjust the difficulty of the content based on the estimated emotions. For example, if the reader is excited, the difficulty level can be increased to provide challenging content. If the reader is tired, the difficulty level can be decreased to provide easier content. If the reader is losing interest, the difficulty level can be adjusted to bring the reader back into interest. This makes it possible to optimally adjust the difficulty of the content according to the reader's emotions.

[0094] The generation unit can estimate the reader's emotions and adjust the color tone of the generated illustration based on the estimated emotions. For example, if the reader is happy, it can generate an illustration with bright colors. If the reader is depressed, it can generate an illustration with calm colors. If the reader is excited, it can generate an illustration with vivid colors. This makes it possible to optimally adjust the color tone of the illustration according to the reader's emotions.

[0095] The providing unit can estimate the reader's emotions and adjust the order of content to be provided based on the estimated emotions. For example, if the reader is excited, stimulating content can be provided first. If the reader is relaxed, calm content can be provided first. If the reader is concentrating, content suitable for learning can be provided first. This makes it possible to adjust the order of content optimally according to the reader's emotions.

[0096] The providing unit can estimate the reader's emotions and adjust the format of the content to be provided based on the estimated emotions. For example, if the reader is tired, short video content can be provided. If the reader is excited, interactive game content can be provided. If the reader is relaxed, long reading content can be provided. This makes it possible to adjust the optimal content format according to the reader's emotions.

[0097] The redaction unit can modify the content taking into account the reader's cultural background. For example, the redaction unit uses examples and metaphors that are appropriate for the reader's culture. The redaction unit can also add historical background and examples that are relevant to the reader's culture. The redaction unit can also use language that is considerate of the reader's culture. This allows the content to be modified optimally according to the reader's cultural background.

[0098] The generator can monitor the reader's learning progress in real time and adjust the content it generates. For example, if the reader is struggling with a particular topic, it can generate supplementary materials related to that topic. If the reader is progressing smoothly, it can generate content for the next level. If the reader excels in a particular area, it can generate advanced content related to that area. This makes it possible to generate optimal content according to the reader's learning progress.

[0099] The providing unit can adjust the content providing method taking into account the remaining battery level of the reader's device. For example, if the remaining battery level is low, lightweight text content can be provided. If the remaining battery level is sufficient, rich media content can be provided. If the remaining battery level is medium, balanced content can be provided. This makes it possible to provide optimal content according to the remaining battery level of the reader's device.

[0100] The editing department can monitor the reader's health status and select the appropriate editing method. For example, if the reader is tired, it can use simple expressions. If the reader is concentrating, it can add detailed explanations. If the reader is stressed, it can use expressions that help the reader relax. This allows it to make the most appropriate edits according to the reader's health status.

[0101] The provider can adjust the content delivery method taking into account the reader's internet connection status. For example, if the connection is unstable, it can provide content that can be viewed offline. If the connection is stable, it can provide streaming content. If the connection speed is slow, it can provide lightweight content. This makes it possible to provide optimal content according to the reader's internet connection status.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The correction unit analyzes the content and corrects it to suit the reader's level. For example, difficult words or technical terms are replaced with simpler words, and sentence structures are simplified. The correction unit may also include a judgment unit that determines the reader's level, which is determined based on criteria such as age, education level, and reading comprehension. Step 2: The generation unit automatically generates elements that attract the interest of young children based on the content corrected by the correction unit. For example, the generation unit may include an illustration generation unit that generates colorful illustrations and an audio guide generation unit that generates audio guides. Step 3: The providing unit provides the content generated by the generating unit. For example, the providing unit may include a distributing unit that distributes the generated content.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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, in order to avoid confusion and to 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A correction department analyzes the content and modifies it to suit the level of the reader. a generating unit that automatically generates elements that attract the interest of young children based on the content corrected by the correcting unit; a providing unit that provides the content generated by the generating unit. A system characterized by:

2. The correction unit Equipped with a judgment section to judge the reader's level 2. The system of claim 1.

3. The generation unit Equipped with an illustration generation unit that generates colorful illustrations 2. The system of claim 1.

4. The generation unit A voice guide generating unit is provided to generate voice guides.

2. The system of claim 1.

5. The providing unit A distribution unit that distributes the generated content is provided.

2. The system of claim 1.

6. The correction unit Inferring user sentiment and adjusting content modification methods based on the estimated user sentiment 2. The system of claim 1.

7. The correction unit Analyze the reader's past learning history and select appropriate revision methods 2. The system of claim 1.

8. The correction unit When revising content, filter it based on your readers' current interests 2. The system of claim 1.

9. The correction unit When correcting content, choose the appropriate correction method depending on the reader's input method 2. The system of claim 1.

Images