system

Abstract

An object of a system according to an embodiment is to appropriately translate and adjust an overseas blog for Japanese.SOLUTION: A system includes a reception unit, a translation unit, and an adjustment unit. The reception unit receives a URL. The translation unit analyzes the URL accepted by the acceptance unit and translates the contents of the blog. The adjustment unit adjusts the content translated by the translation unit for Japanese.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately translate and adapt overseas blogs for Japanese readers, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately translate and adjust overseas blogs for Japanese readers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a translation unit, and an adjustment unit. The reception unit receives a URL. The translation unit analyzes the URL received by the reception unit and translates the content of the blog. The adjustment unit adjusts the content translated by the translation unit to suit Japanese readers. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately translate and adjust overseas blogs for Japanese readers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that easily converts overseas blogs into Japanese versions. In this system, a user inputs the URL of the overseas blog they wish to convert, and a generation AI analyzes the URL and translates the blog content into Japanese. The translated content is adjusted to take into account expressions and cultural backgrounds appropriate for Japanese readers. This allows overseas blogs to be provided in a format that is easy for Japanese readers to understand. This allows the system to provide overseas blogs in a format that is easy for Japanese readers to understand. For example, a user can easily view a blog post translated into Japanese simply by inputting the URL of a specific blog post. This makes it easier for Japanese people to understand information about travel destinations.

[0029] A conversion system according to an embodiment includes a reception unit, a translation unit, and an adjustment unit. The reception unit receives the URL of a foreign blog that a user wants to convert. For example, a user simply inputs the URL of a specific blog post. The translation unit uses a generation AI to analyze the URL received by the reception unit and translate the blog post content into Japanese. For example, the generation AI analyzes the text of the blog post and translates it into appropriate Japanese. For example, the generation AI translates a travel blog post written in English into Japanese. The adjustment unit adjusts the content translated by the translation unit for Japanese audiences. For example, instead of translating English expressions directly into Japanese, the adjustment unit replaces them with expressions that are easier for Japanese people to understand. Appropriate background information can also be added, taking cultural differences into consideration. This makes the translated blog post easier for Japanese people to understand. As a result, the conversion system according to an embodiment can easily convert foreign blogs into Japanese audiences.

[0030] The conversion system includes a providing unit that provides the translated content to the user. The providing unit provides the translated content to the user. For example, the providing unit can provide the translated content as a web page or a PDF. In this way, the translated content can be provided to the user.

[0031] The conversion system includes an optimization unit that optimizes the translation algorithm. The optimization unit optimizes the translation algorithm. For example, the optimization unit updates the learning data of the generation AI to improve the accuracy of the translation. This allows the translation algorithm to be optimized.

[0032] The conversion system includes a feedback receiving unit that receives feedback from users. The feedback receiving unit receives feedback from users. For example, the feedback receiving unit collects opinions and points for improvement from users and uses them to improve the system. This allows the conversion system to receive feedback from users.

[0033] The providing unit can provide the translated content as a web page or PDF. The providing unit provides the translated content as a web page or PDF. For example, the providing unit provides the translated content as a web page in HTML format. The providing unit can also provide the translated content in PDF format. This allows the translated content to be provided as a web page or PDF.

[0034] The optimization unit updates the learning data of the generation AI and can improve the accuracy of translation. The optimization unit updates the learning data of the generation AI and improves the accuracy of translation. For example, the optimization unit periodically updates the learning data of the generation AI to reflect the latest data. The optimization unit can also update the learning data based on feedback from users. This updates the learning data of the generation AI and can improve the accuracy of translation.

[0035] The feedback receiving unit can collect opinions or improvements from users and use them to improve the system. The feedback receiving unit can collect opinions and improvements from users and use them to improve the system. For example, the feedback receiving unit can collect opinions from users in the form of a questionnaire. The feedback receiving unit can also collect improvements from users in the form of comments. In this way, the opinions and improvements from users can be collected and used to improve the system.

[0036] The reception unit can analyze the user's past URL input history and select the optimal reception method. The reception unit analyzes the user's past URL input history and selects the optimal reception method. For example, the reception unit automatically displays URLs that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit predicts and suggests URLs to be used during specific time periods based on the user's past input history. This makes it possible to analyze the user's past URL input history and select the optimal reception method.

[0037] The reception unit can filter URLs based on the user's current areas of interest when receiving the URLs. The reception unit filters URLs based on the user's current areas of interest when receiving the URLs. For example, the reception unit preferentially receives URLs related to keywords recently searched by the user. The reception unit can also filter related URLs based on categories of blogs the user has previously viewed. Furthermore, the reception unit analyzes the user's social media activity and filters URLs based on the user's areas of interest. This makes it possible to filter URLs based on the user's current areas of interest.

[0038] The reception unit can select the optimal reception means depending on the user's input method when receiving a URL. The reception unit selects the optimal reception means depending on the user's input method when receiving a URL. For example, when the user inputs a URL by voice, the reception unit uses voice recognition technology to receive the URL. Also, when the user inputs a URL using text, the reception unit can provide a text input interface. Furthermore, when the user inputs a URL using an image, the reception unit analyzes the URL using image recognition technology and receives it. This makes it possible to select the optimal reception means depending on the user's input method.

[0039] When receiving a URL, the reception unit can preferentially receive highly relevant URLs taking into account the user's geographical location information. When receiving a URL, the reception unit preferentially receives highly relevant URLs taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can preferentially receive URLs of blogs related to that area. Also, if the user is traveling, the reception unit can preferentially receive URLs of blogs related to the user's travel destination. Furthermore, if the user is participating in a specific event, the reception unit can preferentially receive URLs of blogs related to the event. In this way, it is possible to preferentially receive highly relevant URLs taking into account the user's geographical location information.

[0040] The reception unit can analyze the user's social media activity and receive related URLs when receiving a URL. The reception unit analyzes the user's social media activity and receives related URLs when receiving a URL. For example, the reception unit preferentially receives URLs of blogs shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and receive URLs of related blogs. Furthermore, the reception unit receives URLs of related blogs by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related URLs can be received.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a URL. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a URL. For example, the reception unit preferentially receives URLs of blogs that the user has previously rated highly. The reception unit can also preferentially receive URLs of blogs for which the user has previously provided feedback. Furthermore, the reception unit analyzes the user's past feedback and suggests the optimal reception method. This makes it possible to customize the reception method by reflecting the user's past feedback.

[0042] The translation unit can adjust the level of detail of the translation based on the importance of the blog during translation. The translation unit uses the generative AI to adjust the level of detail of the translation based on the importance of the blog during translation. For example, the translation unit provides a detailed translation for a blog that contains important information. The translation unit can also provide a concise translation for a blog that contains general information. Furthermore, the translation unit provides a detailed translation for a blog that the user is particularly interested in. This makes it possible to adjust the level of detail of the translation based on the importance of the blog.

[0043] The translation unit can apply different translation algorithms depending on the blog category during translation. The translation unit uses generative AI to apply different translation algorithms depending on the blog category during translation. For example, in the case of a travel blog, the translation unit appropriately translates travel-related terminology. In addition, in the case of a technology blog, the translation unit can accurately translate technical terminology. Furthermore, in the case of a cooking blog, the translation unit appropriately translates cooking-related terminology. This makes it possible to apply different translation algorithms depending on the blog category.

[0044] The translation unit can improve the accuracy of translation by referring to the user's past translation results when translating. The translation unit uses generative AI to improve the accuracy of translation by referring to the user's past translation results when translating. For example, the translation unit translates by referring to translation results that the user has previously given high ratings. The translation unit can also improve the accuracy of translation based on feedback that the user has previously provided. Furthermore, the translation unit analyzes the user's past translation history and suggests the optimal translation method. This makes it possible to improve the accuracy of translation by referring to the user's past translation results.

[0045] The translation unit can determine the priority of translations based on when the blog was posted during translation. The translation unit uses generative AI to determine the priority of translations based on when the blog was posted during translation. For example, the translation unit prioritizes translating the most recent blog posts. The translation unit can also prioritize translating blog posts from a period in which the user is particularly interested. Furthermore, the translation unit prioritizes translating blog posts related to a specific event. This makes it possible to determine the priority of translations based on when the blog was posted.

[0046] The translation unit can adjust the order of translations based on the relevance of blogs during translation. The translation unit uses generative AI to adjust the order of translations based on the relevance of blogs during translation. For example, the translation unit prioritizes translating blog articles in categories that the user is particularly interested in. The translation unit can also prioritize translating highly relevant blog articles based on the user's past browsing history. Furthermore, the translation unit analyzes the user's social media activity and prioritizes translating highly relevant blog articles. This makes it possible to adjust the order of translations based on the relevance of blogs.

[0047] The translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise during translation. The translation unit uses generative AI to adjust the use of technical terminology in the translation according to the user's level of expertise during translation. For example, if the user has specialized knowledge, the translation unit will perform a translation that makes heavy use of technical terminology. In addition, if the user is a beginner, the translation unit can also perform a simple translation that avoids technical terminology. Furthermore, the translation unit suggests the optimal use of technical terminology based on the user's past translation history. This makes it possible to adjust the use of technical terminology in the translation according to the user's level of expertise.

[0048] The adjustment unit can improve the accuracy of the adjustment by taking into account the cultural background of the blog when making adjustments. The adjustment unit uses AI to improve the accuracy of the adjustment by taking into account the cultural background of the blog when making adjustments. For example, the adjustment unit replaces English expressions with expressions that are easy for Japanese people to understand, rather than simply translating them into Japanese. The adjustment unit can also add appropriate background information by taking cultural differences into account. Furthermore, the adjustment unit explains the cultural background so that it is easy for Japanese people to understand. This makes it possible to improve the accuracy of the adjustment by taking into account the cultural background of the blog.

[0049] The adjustment unit can improve the accuracy of adjustment by referring to the user's past adjustment results when making adjustments. The adjustment unit uses AI to improve the accuracy of adjustment by referring to the user's past adjustment results when making adjustments. For example, the adjustment unit makes adjustments by referring to adjustment results that the user has given high ratings to in the past. The adjustment unit can also improve the accuracy of adjustment based on feedback that the user has provided in the past. Furthermore, the adjustment unit analyzes the user's past adjustment history and suggests the optimal adjustment method. This makes it possible to improve the accuracy of adjustment by referring to the user's past adjustment results.

[0050] The adjustment unit can apply different adjustment algorithms depending on the category of the blog during adjustment. The adjustment unit uses AI to apply different adjustment algorithms depending on the category of the blog during adjustment. For example, in the case of a travel blog, the adjustment unit appropriately adjusts travel-related terminology. In addition, in the case of a technology blog, the adjustment unit can accurately adjust technical terminology. Furthermore, in the case of a cooking blog, the adjustment unit appropriately adjusts cooking-related terminology. This makes it possible to apply different adjustment algorithms depending on the category of the blog.

[0051] The adjustment unit can determine the priority of adjustment based on the time of blog posting during adjustment. The adjustment unit uses AI to determine the priority of adjustment based on the time of blog posting during adjustment. For example, the adjustment unit prioritizes adjusting the latest blog posts. The adjustment unit can also prioritize adjusting blog posts from a period in which the user is particularly interested. Furthermore, the adjustment unit prioritizes adjusting blog posts related to a specific event. This makes it possible to determine the priority of adjustment based on the time of blog posting.

[0052] The adjustment unit can adjust the adjustment order based on the relevance of the blogs during adjustment. The adjustment unit uses AI to adjust the adjustment order based on the relevance of the blogs during adjustment. For example, the adjustment unit prioritizes blog articles in categories in which the user is particularly interested. The adjustment unit can also prioritize highly relevant blog articles based on the user's past browsing history. Furthermore, the adjustment unit analyzes the user's social media activity and prioritizes highly relevant blog articles. This makes it possible to adjust the adjustment order based on the relevance of the blogs.

[0053] The adjustment unit can adjust the use of technical terms in the adjustment according to the user's level of expertise during adjustment. The adjustment unit uses AI to adjust the use of technical terms in the adjustment according to the user's level of expertise during adjustment. For example, if the user has technical knowledge, the adjustment unit makes adjustments that use a lot of technical terms. In addition, if the user is a beginner, the adjustment unit can also make simple adjustments that avoid technical terms. Furthermore, the adjustment unit suggests the optimal use of technical terms based on the user's past adjustment history. This makes it possible to adjust the use of technical terms in the adjustment according to the user's level of expertise.

[0054] The provision unit can select an appropriate delivery method by referring to the user's past browsing history when providing the content. The provision unit uses AI to select an appropriate delivery method by referring to the user's past browsing history when providing the content. For example, the provision unit prioritizes providing blog articles that the user has previously rated highly. The provision unit can also provide related blog articles based on the categories of blogs that the user has previously viewed. Furthermore, the provision unit analyzes the user's past browsing history and suggests the optimal delivery method. This makes it possible to select the optimal delivery method by referring to the user's past browsing history.

[0055] The providing unit can customize the provided content based on the user's current areas of interest at the time of providing the content. The providing unit uses AI to customize the provided content based on the user's current areas of interest at the time of providing the content. For example, the providing unit can provide related blog articles based on keywords recently searched by the user. The providing unit can also provide related blog articles based on categories of blogs that the user has previously viewed. Furthermore, the providing unit analyzes the user's social media activity and provides blog articles based on the user's areas of interest. This makes it possible to customize the provided content based on the user's current areas of interest.

[0056] The provision unit can improve the provision method by reflecting user feedback at the time of provision. The provision unit uses AI to improve the provision method by reflecting user feedback at the time of provision. For example, the provision unit improves the provision method based on feedback provided by the user in the past. The provision unit can also analyze the user's past feedback and propose the optimal provision method. Furthermore, the provision unit reflects user feedback in real time and adjusts the provision method. In this way, the provision method can be improved by reflecting user feedback.

[0057] The providing unit can select an appropriate delivery method by taking into consideration the user's geographical location information at the time of delivery. The providing unit uses AI to select an appropriate delivery method by taking into consideration the user's geographical location information at the time of delivery. For example, if the user is in a specific area, the providing unit can preferentially provide blog articles related to that area. Also, if the user is traveling, the providing unit can preferentially provide blog articles related to the user's travel destination. Furthermore, if the user is participating in a specific event, the providing unit can preferentially provide blog articles related to that event. In this way, the optimal delivery method can be selected by taking into consideration the user's geographical location information.

[0058] The providing unit can analyze the user's social media activity and customize the content provided at the time of providing. The providing unit uses AI to analyze the user's social media activity and customize the content provided at the time of providing. For example, the providing unit can prioritize providing blog articles shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related blog articles. Furthermore, the providing unit can provide related blog articles by referring to the activity of the user's friends on social media. In this way, the content provided can be customized by analyzing the user's social media activity.

[0059] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. The providing unit uses AI to customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit can prioritize providing blog articles that the user has previously rated highly. The providing unit can also prioritize providing blog articles for which the user has previously provided feedback. Furthermore, the providing unit analyzes the user's past feedback and suggests the optimal delivery method. This makes it possible to customize the delivery method by reflecting the user's past feedback.

[0060] The optimization unit can optimize the optimization algorithm by referring to past optimization data during optimization. The optimization unit uses AI to optimize the optimization algorithm by referring to past optimization data during optimization. For example, the optimization unit selects the optimal algorithm based on past optimization data. The optimization unit can also analyze past optimization data to improve the accuracy of the algorithm. Furthermore, the optimization unit can set optimal parameters by referring to past optimization data. This makes it possible to optimize the optimization algorithm by referring to past optimization data.

[0061] The optimization unit can update the optimization data by reflecting user feedback during optimization. The optimization unit uses AI to update the optimization data by reflecting user feedback during optimization. For example, the optimization unit updates the optimization data based on feedback provided by the user. The optimization unit can also analyze the user feedback and improve the optimization algorithm. Furthermore, the optimization unit reflects user feedback in real time and updates the optimization data. This allows the optimization data to be updated by reflecting user feedback.

[0062] The optimization unit can integrate information from different data sources to enrich the optimization data during optimization. The optimization unit uses AI to integrate information from different data sources to enrich the optimization data during optimization. For example, the optimization unit integrates information from different data sources to enrich the optimization data. The optimization unit can also analyze information from different data sources to improve the optimization algorithm. Furthermore, the optimization unit refers to information from different data sources to set optimal parameters. This allows the optimization data to be enriched by integrating information from different data sources.

[0063] The optimization unit can weight the optimization data based on the time of blog posting during optimization. The optimization unit uses AI to weight the optimization data based on the time of blog posting during optimization. For example, the optimization unit sets a high weighting of the optimization data for the most recent blog posts. The optimization unit can also set a high weighting of the optimization data for blog posts published during a period in which the user is particularly interested. Furthermore, the optimization unit sets a high weighting of the optimization data for blog posts related to a specific event. In this way, the optimization data can be weighted based on the time of blog posting.

[0064] The optimization unit can integrate information from different data sources to enrich the optimization data during optimization. The optimization unit uses AI to integrate information from different data sources to enrich the optimization data during optimization. For example, the optimization unit integrates information from different data sources to enrich the optimization data. The optimization unit can also analyze information from different data sources to improve the optimization algorithm. Furthermore, the optimization unit refers to information from different data sources to set optimal parameters. This allows the optimization data to be enriched by integrating information from different data sources.

[0065] The optimization unit can update the optimization data by reflecting user feedback during optimization. The optimization unit uses AI to update the optimization data by reflecting user feedback during optimization. For example, the optimization unit updates the optimization data based on feedback provided by the user. The optimization unit can also analyze the user feedback and improve the optimization algorithm. Furthermore, the optimization unit reflects user feedback in real time and updates the optimization data. This allows the optimization data to be updated by reflecting user feedback.

[0066] The feedback receiving unit can select the optimal feedback receiving method by referring to the user's past feedback history when receiving feedback. The feedback receiving unit uses AI to select the optimal feedback receiving method by referring to the user's past feedback history when receiving feedback. For example, the feedback receiving unit provides the optimal feedback form based on the form of feedback provided by the user in the past. The feedback receiving unit can also preferentially suggest feedback methods that the user has given high ratings to in the past. Furthermore, the feedback receiving unit analyzes the user's past feedback history and suggests the optimal feedback receiving method. In this way, the optimal feedback receiving method can be selected by referring to the user's past feedback history.

[0067] The feedback receiving unit can customize the feedback content based on the user's current areas of interest when receiving the feedback. The feedback receiving unit uses AI to customize the feedback content based on the user's current areas of interest when receiving the feedback. For example, the feedback receiving unit provides feedback items related to topics that the user has recently been interested in. The feedback receiving unit can also provide related feedback items based on the user's past browsing history. Furthermore, the feedback receiving unit analyzes the user's social media activity and customizes the feedback items based on the user's areas of interest. This allows the feedback content to be customized based on the user's current areas of interest.

[0068] The feedback receiving unit can select the optimal reception method in consideration of the user's geographical location information when receiving feedback. The feedback receiving unit uses AI to select the optimal reception method in consideration of the user's geographical location information when receiving feedback. For example, if the user is in a specific area, the feedback receiving unit can provide feedback items related to the area. Also, if the user is traveling, the feedback receiving unit can provide feedback items related to the travel destination. Furthermore, if the user is participating in a specific event, the feedback receiving unit can provide feedback items related to the event. This makes it possible to select the optimal reception method in consideration of the user's geographical location information.

[0069] The feedback receiving unit can analyze the user's social media activity and customize the feedback content when receiving the feedback. The feedback receiving unit uses AI to analyze the user's social media activity and customize the feedback content when receiving the feedback. For example, the feedback receiving unit provides feedback items related to content shared by the user on social media. The feedback receiving unit can also analyze the content posted by the user on social media and provide related feedback items. Furthermore, the feedback receiving unit provides related feedback items with reference to the activity of the user's friends on social media. This makes it possible to customize the feedback content by analyzing the user's social media activity.

[0070] The feedback receiving unit can customize the feedback receiving method by reflecting the user's past feedback when receiving feedback. The feedback receiving unit uses AI to customize the feedback receiving method by reflecting the user's past feedback when receiving feedback. For example, the feedback receiving unit preferentially suggests feedback methods that the user has given high ratings to in the past. The feedback receiving unit can also provide an optimal feedback form based on the form of feedback the user has provided in the past. Furthermore, the feedback receiving unit analyzes the user's past feedback history and suggests the optimal feedback receiving method. This makes it possible to customize the feedback receiving method by reflecting the user's past feedback.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The conversion system can analyze a user's past browsing history and provide relevant blog articles with priority. For example, it can suggest related blog articles based on the categories of blogs the user has previously viewed. It can also provide blog articles that the user has previously rated highly. Furthermore, it can predict and suggest blog articles that will be used during specific time periods based on the user's past browsing history. This makes it possible to provide the most suitable blog articles based on the user's past browsing history.

[0073] The conversion system can provide highly relevant blog articles with priority given to the user's geographical location information. For example, if the user is in a specific area, blog articles related to that area can be provided with priority. Also, if the user is traveling, blog articles related to the travel destination can be provided with priority. Furthermore, if the user is participating in a specific event, blog articles related to that event can be provided with priority. In this way, the optimal blog articles can be provided with priority given to the user's geographical location information.

[0074] The conversion system can analyze a user's social media activity and provide relevant blog articles. For example, it can prioritize providing blog articles that the user has shared on social media. It can also analyze the content of a user's social media posts and provide relevant blog articles. It can also provide relevant blog articles by taking into account the activities of the user's friends on social media. This makes it possible to provide the most appropriate blog articles based on the user's social media activity.

[0075] The conversion system can customize the reception method by reflecting the user's past feedback. For example, it can preferentially suggest feedback methods that the user has given a high rating in the past. It can also provide the optimal feedback form based on the feedback formats the user has provided in the past. Furthermore, it can analyze the user's past feedback history and suggest the optimal feedback reception method. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0076] The conversion system can integrate information from different data sources to enrich the optimization data. For example, it can integrate information from different data sources to enrich the optimization data. It can also analyze information from different data sources to improve the optimization algorithm. It can also refer to information from different data sources to set optimal parameters. This allows it to integrate information from different data sources to enrich the optimization data.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit accepts the URL of the overseas blog that the user wants to convert. For example, the user can simply enter the URL of a specific blog post. Step 2: The translation unit uses the generation AI to analyze the URL received by the reception unit and translate the blog content into Japanese. For example, the generation AI analyzes the text of a blog post and translates it into appropriate Japanese. For example, the generation AI translates a travel blog post written in English into Japanese. Step 3: The adjustment department adjusts the content translated by the translation department for Japanese readers. For example, instead of translating English expressions directly into Japanese, they replace them with expressions that are easier for Japanese people to understand. They can also add appropriate background information to take cultural differences into account. This makes the translated blog post easier for Japanese people to understand.

[0079] (Example 2) A system according to an embodiment of the present invention is a system that easily converts overseas blogs into Japanese versions. In this system, a user inputs the URL of the overseas blog they wish to convert, and a generation AI analyzes the URL and translates the blog content into Japanese. The translated content is adjusted to take into account expressions and cultural backgrounds appropriate for Japanese readers. This allows overseas blogs to be provided in a format that is easy for Japanese readers to understand. This allows the system to provide overseas blogs in a format that is easy for Japanese readers to understand. For example, a user can easily view a blog post translated into Japanese simply by inputting the URL of a specific blog post. This makes it easier for Japanese people to understand information about travel destinations.

[0080] A conversion system according to an embodiment includes a reception unit, a translation unit, and an adjustment unit. The reception unit receives the URL of a foreign blog that a user wants to convert. For example, a user simply inputs the URL of a specific blog post. The translation unit uses a generation AI to analyze the URL received by the reception unit and translate the blog post content into Japanese. For example, the generation AI analyzes the text of the blog post and translates it into appropriate Japanese. For example, the generation AI translates a travel blog post written in English into Japanese. The adjustment unit adjusts the content translated by the translation unit for Japanese audiences. For example, instead of translating English expressions directly into Japanese, the adjustment unit replaces them with expressions that are easier for Japanese people to understand. Appropriate background information can also be added, taking cultural differences into consideration. This makes the translated blog post easier for Japanese people to understand. As a result, the conversion system according to an embodiment can easily convert foreign blogs into Japanese audiences.

[0081] The conversion system includes a providing unit that provides the translated content to the user. The providing unit provides the translated content to the user. For example, the providing unit can provide the translated content as a web page or a PDF. In this way, the translated content can be provided to the user.

[0082] The conversion system includes an optimization unit that optimizes the translation algorithm. The optimization unit optimizes the translation algorithm. For example, the optimization unit updates the learning data of the generation AI to improve the accuracy of the translation. This allows the translation algorithm to be optimized.

[0083] The conversion system includes a feedback receiving unit that receives feedback from users. The feedback receiving unit receives feedback from users. For example, the feedback receiving unit collects opinions and points for improvement from users and uses them to improve the system. This allows the conversion system to receive feedback from users.

[0084] The providing unit can provide the translated content as a web page or PDF. The providing unit provides the translated content as a web page or PDF. For example, the providing unit provides the translated content as a web page in HTML format. The providing unit can also provide the translated content in PDF format. This allows the translated content to be provided as a web page or PDF.

[0085] The optimization unit updates the learning data of the generation AI and can improve the accuracy of translation. The optimization unit updates the learning data of the generation AI and improves the accuracy of translation. For example, the optimization unit periodically updates the learning data of the generation AI to reflect the latest data. The optimization unit can also update the learning data based on feedback from users. This updates the learning data of the generation AI and can improve the accuracy of translation.

[0086] The feedback receiving unit can collect opinions or improvements from users and use them to improve the system. The feedback receiving unit can collect opinions and improvements from users and use them to improve the system. For example, the feedback receiving unit can collect opinions from users in the form of a questionnaire. The feedback receiving unit can also collect improvements from users in the form of comments. In this way, the opinions and improvements from users can be collected and used to improve the system.

[0087] The reception unit can estimate the user's emotions and adjust the timing of URL reception based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the timing of URL reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Also, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick URL input. In this way, the timing of URL reception can be adjusted based on the user's emotions.

[0088] The reception unit can analyze the user's past URL input history and select the optimal reception method. The reception unit analyzes the user's past URL input history and selects the optimal reception method. For example, the reception unit automatically displays URLs that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit predicts and suggests URLs to be used during specific time periods based on the user's past input history. This makes it possible to analyze the user's past URL input history and select the optimal reception method.

[0089] The reception unit can filter URLs based on the user's current areas of interest when receiving the URLs. The reception unit filters URLs based on the user's current areas of interest when receiving the URLs. For example, the reception unit preferentially receives URLs related to keywords recently searched by the user. The reception unit can also filter related URLs based on categories of blogs the user has previously viewed. Furthermore, the reception unit analyzes the user's social media activity and filters URLs based on the user's areas of interest. This makes it possible to filter URLs based on the user's current areas of interest.

[0090] The reception unit can select the optimal reception means depending on the user's input method when receiving a URL. The reception unit selects the optimal reception means depending on the user's input method when receiving a URL. For example, when the user inputs a URL by voice, the reception unit uses voice recognition technology to receive the URL. Also, when the user inputs a URL using text, the reception unit can provide a text input interface. Furthermore, when the user inputs a URL using an image, the reception unit analyzes the URL using image recognition technology and receives it. This makes it possible to select the optimal reception means depending on the user's input method.

[0091] The reception unit can estimate the user's emotion and determine the priority of URLs to be received based on the estimated user's emotion. The reception unit can estimate the user's emotion and determine the priority of URLs to be received based on the estimated user's emotion. For example, if the user is excited, the reception unit can preferentially receive entertainment-related URLs. Also, if the user is relaxed, the reception unit can preferentially receive URLs of relaxing content. Furthermore, if the user is stressed, the reception unit can preferentially receive URLs of content that helps relieve stress. In this way, the priority of URLs can be determined based on the user's emotion.

[0092] When receiving a URL, the reception unit can preferentially receive highly relevant URLs taking into account the user's geographical location information. When receiving a URL, the reception unit preferentially receives highly relevant URLs taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can preferentially receive URLs of blogs related to that area. Also, if the user is traveling, the reception unit can preferentially receive URLs of blogs related to the user's travel destination. Furthermore, if the user is participating in a specific event, the reception unit can preferentially receive URLs of blogs related to the event. In this way, it is possible to preferentially receive highly relevant URLs taking into account the user's geographical location information.

[0093] The reception unit can analyze the user's social media activity and receive related URLs when receiving a URL. The reception unit analyzes the user's social media activity and receives related URLs when receiving a URL. For example, the reception unit preferentially receives URLs of blogs shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and receive URLs of related blogs. Furthermore, the reception unit receives URLs of related blogs by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related URLs can be received.

[0094] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a URL. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a URL. For example, the reception unit preferentially receives URLs of blogs that the user has previously rated highly. The reception unit can also preferentially receive URLs of blogs for which the user has previously provided feedback. Furthermore, the reception unit analyzes the user's past feedback and suggests the optimal reception method. This makes it possible to customize the reception method by reflecting the user's past feedback.

[0095] The translation unit can estimate the user's emotions and adjust the way the translation is expressed based on the estimated user's emotions. The translation unit uses a generation AI to estimate the user's emotions and adjust the way the translation is expressed based on the estimated user's emotions. For example, if the user is relaxed, the translation unit can translate using softer expressions. If the user is in a hurry, the translation unit can also translate using concise and to-the-point expressions. Furthermore, if the user is excited, the translation unit can translate using energetic expressions. This makes it possible to adjust the way the translation is expressed based on the user's emotions.

[0096] The translation unit can adjust the level of detail of the translation based on the importance of the blog during translation. The translation unit uses the generative AI to adjust the level of detail of the translation based on the importance of the blog during translation. For example, the translation unit provides a detailed translation for a blog that contains important information. The translation unit can also provide a concise translation for a blog that contains general information. Furthermore, the translation unit provides a detailed translation for a blog that the user is particularly interested in. This makes it possible to adjust the level of detail of the translation based on the importance of the blog.

[0097] The translation unit can apply different translation algorithms depending on the blog category during translation. The translation unit uses generative AI to apply different translation algorithms depending on the blog category during translation. For example, in the case of a travel blog, the translation unit appropriately translates travel-related terminology. In addition, in the case of a technology blog, the translation unit can accurately translate technical terminology. Furthermore, in the case of a cooking blog, the translation unit appropriately translates cooking-related terminology. This makes it possible to apply different translation algorithms depending on the blog category.

[0098] The translation unit can improve the accuracy of translation by referring to the user's past translation results when translating. The translation unit uses generative AI to improve the accuracy of translation by referring to the user's past translation results when translating. For example, the translation unit translates by referring to translation results that the user has previously given high ratings. The translation unit can also improve the accuracy of translation based on feedback that the user has previously provided. Furthermore, the translation unit analyzes the user's past translation history and suggests the optimal translation method. This makes it possible to improve the accuracy of translation by referring to the user's past translation results.

[0099] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. The translation unit uses generative AI to estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. For example, if the user is in a hurry, the translation unit will provide a short, to-the-point translation. If the user is relaxed, the translation unit can also provide a longer translation with detailed explanations. Furthermore, if the user is excited, the translation unit will provide a translation with visually stimulating effects. This makes it possible to adjust the length of the translation based on the user's emotions.

[0100] The translation unit can determine the priority of translations based on when the blog was posted during translation. The translation unit uses generative AI to determine the priority of translations based on when the blog was posted during translation. For example, the translation unit prioritizes translating the most recent blog posts. The translation unit can also prioritize translating blog posts from a period in which the user is particularly interested. Furthermore, the translation unit prioritizes translating blog posts related to a specific event. This makes it possible to determine the priority of translations based on when the blog was posted.

[0101] The translation unit can adjust the order of translations based on the relevance of blogs during translation. The translation unit uses generative AI to adjust the order of translations based on the relevance of blogs during translation. For example, the translation unit prioritizes translating blog articles in categories that the user is particularly interested in. The translation unit can also prioritize translating highly relevant blog articles based on the user's past browsing history. Furthermore, the translation unit analyzes the user's social media activity and prioritizes translating highly relevant blog articles. This makes it possible to adjust the order of translations based on the relevance of blogs.

[0102] The translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise during translation. The translation unit uses generative AI to adjust the use of technical terminology in the translation according to the user's level of expertise during translation. For example, if the user has specialized knowledge, the translation unit will perform a translation that makes heavy use of technical terminology. In addition, if the user is a beginner, the translation unit can also perform a simple translation that avoids technical terminology. Furthermore, the translation unit suggests the optimal use of technical terminology based on the user's past translation history. This makes it possible to adjust the use of technical terminology in the translation according to the user's level of expertise.

[0103] The adjustment unit can estimate the user's emotions and change the adjustment method based on the estimated user's emotions. The adjustment unit uses AI to estimate the user's emotions and change the adjustment method based on the estimated user's emotions. For example, the adjustment unit adjusts the sound using softer expressions when the user is relaxed. The adjustment unit can also adjust the sound using concise and to-the-point expressions when the user is in a hurry. Furthermore, the adjustment unit adjusts the sound using energetic expressions when the user is excited. In this way, the adjustment method can be changed based on the user's emotions.

[0104] The adjustment unit can improve the accuracy of the adjustment by taking into account the cultural background of the blog when making adjustments. The adjustment unit uses AI to improve the accuracy of the adjustment by taking into account the cultural background of the blog when making adjustments. For example, the adjustment unit replaces English expressions with expressions that are easy for Japanese people to understand, rather than simply translating them into Japanese. The adjustment unit can also add appropriate background information by taking cultural differences into account. Furthermore, the adjustment unit explains the cultural background so that it is easy for Japanese people to understand. This makes it possible to improve the accuracy of the adjustment by taking into account the cultural background of the blog.

[0105] The adjustment unit can improve the accuracy of adjustment by referring to the user's past adjustment results when making adjustments. The adjustment unit uses AI to improve the accuracy of adjustment by referring to the user's past adjustment results when making adjustments. For example, the adjustment unit makes adjustments by referring to adjustment results that the user has given high ratings to in the past. The adjustment unit can also improve the accuracy of adjustment based on feedback that the user has provided in the past. Furthermore, the adjustment unit analyzes the user's past adjustment history and suggests the optimal adjustment method. This makes it possible to improve the accuracy of adjustment by referring to the user's past adjustment results.

[0106] The adjustment unit can apply different adjustment algorithms depending on the category of the blog during adjustment. The adjustment unit uses AI to apply different adjustment algorithms depending on the category of the blog during adjustment. For example, in the case of a travel blog, the adjustment unit appropriately adjusts travel-related terminology. In addition, in the case of a technology blog, the adjustment unit can accurately adjust technical terminology. Furthermore, in the case of a cooking blog, the adjustment unit appropriately adjusts cooking-related terminology. This makes it possible to apply different adjustment algorithms depending on the category of the blog.

[0107] The adjustment unit can estimate the user's emotions and determine the priority of adjustment based on the estimated user's emotions. The adjustment unit uses AI to estimate the user's emotions and determine the priority of adjustment based on the estimated user's emotions. For example, if the user is excited, the adjustment unit can prioritize entertainment-related adjustment. Also, if the user is relaxed, the adjustment unit can prioritize adjusting relaxing content. Furthermore, if the user is stressed, the adjustment unit prioritizes adjusting content that helps relieve stress. In this way, the priority of adjustment can be determined based on the user's emotions.

[0108] The adjustment unit can determine the priority of adjustment based on the time of blog posting during adjustment. The adjustment unit uses AI to determine the priority of adjustment based on the time of blog posting during adjustment. For example, the adjustment unit prioritizes adjusting the latest blog posts. The adjustment unit can also prioritize adjusting blog posts from a period in which the user is particularly interested. Furthermore, the adjustment unit prioritizes adjusting blog posts related to a specific event. This makes it possible to determine the priority of adjustment based on the time of blog posting.

[0109] The adjustment unit can adjust the adjustment order based on the relevance of the blogs during adjustment. The adjustment unit uses AI to adjust the adjustment order based on the relevance of the blogs during adjustment. For example, the adjustment unit prioritizes blog articles in categories in which the user is particularly interested. The adjustment unit can also prioritize highly relevant blog articles based on the user's past browsing history. Furthermore, the adjustment unit analyzes the user's social media activity and prioritizes highly relevant blog articles. This makes it possible to adjust the adjustment order based on the relevance of the blogs.

[0110] The adjustment unit can adjust the use of technical terms in the adjustment according to the user's level of expertise during adjustment. The adjustment unit uses AI to adjust the use of technical terms in the adjustment according to the user's level of expertise during adjustment. For example, if the user has technical knowledge, the adjustment unit makes adjustments that use a lot of technical terms. In addition, if the user is a beginner, the adjustment unit can also make simple adjustments that avoid technical terms. Furthermore, the adjustment unit suggests the optimal use of technical terms based on the user's past adjustment history. This makes it possible to adjust the use of technical terms in the adjustment according to the user's level of expertise.

[0111] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. The providing unit uses AI to estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide information using soft expressions. Also, if the user is in a hurry, the providing unit can provide information using concise and to-the-point expressions. Furthermore, if the user is excited, the providing unit can provide information using energetic expressions. In this way, the method of providing information can be adjusted based on the user's emotions.

[0112] The provision unit can select an appropriate delivery method by referring to the user's past browsing history when providing the content. The provision unit uses AI to select an appropriate delivery method by referring to the user's past browsing history when providing the content. For example, the provision unit prioritizes providing blog articles that the user has previously rated highly. The provision unit can also provide related blog articles based on the categories of blogs that the user has previously viewed. Furthermore, the provision unit analyzes the user's past browsing history and suggests the optimal delivery method. This makes it possible to select the optimal delivery method by referring to the user's past browsing history.

[0113] The providing unit can customize the provided content based on the user's current areas of interest at the time of providing the content. The providing unit uses AI to customize the provided content based on the user's current areas of interest at the time of providing the content. For example, the providing unit can provide related blog articles based on keywords recently searched by the user. The providing unit can also provide related blog articles based on categories of blogs that the user has previously viewed. Furthermore, the providing unit analyzes the user's social media activity and provides blog articles based on the user's areas of interest. This makes it possible to customize the provided content based on the user's current areas of interest.

[0114] The provision unit can improve the provision method by reflecting user feedback at the time of provision. The provision unit uses AI to improve the provision method by reflecting user feedback at the time of provision. For example, the provision unit improves the provision method based on feedback provided by the user in the past. The provision unit can also analyze the user's past feedback and propose the optimal provision method. Furthermore, the provision unit reflects user feedback in real time and adjusts the provision method. In this way, the provision method can be improved by reflecting user feedback.

[0115] The providing unit can estimate the user's emotions and determine the priority of content to be provided based on the estimated user's emotions. The providing unit uses AI to estimate the user's emotions and determine the priority of content to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide entertainment-related blog articles with priority. Also, if the user is relaxed, the providing unit can provide blog articles with relaxing content with priority. Furthermore, if the user is stressed, the providing unit can provide blog articles with content that helps relieve stress with priority. In this way, the priority of content to be provided can be determined based on the user's emotions.

[0116] The providing unit can select an appropriate delivery method by taking into consideration the user's geographical location information at the time of delivery. The providing unit uses AI to select an appropriate delivery method by taking into consideration the user's geographical location information at the time of delivery. For example, if the user is in a specific area, the providing unit can preferentially provide blog articles related to that area. Also, if the user is traveling, the providing unit can preferentially provide blog articles related to the user's travel destination. Furthermore, if the user is participating in a specific event, the providing unit can preferentially provide blog articles related to that event. In this way, the optimal delivery method can be selected by taking into consideration the user's geographical location information.

[0117] The providing unit can analyze the user's social media activity and customize the content provided at the time of providing. The providing unit uses AI to analyze the user's social media activity and customize the content provided at the time of providing. For example, the providing unit can prioritize providing blog articles shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related blog articles. Furthermore, the providing unit can provide related blog articles by referring to the activity of the user's friends on social media. In this way, the content provided can be customized by analyzing the user's social media activity.

[0118] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. The providing unit uses AI to customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit can prioritize providing blog articles that the user has previously rated highly. The providing unit can also prioritize providing blog articles for which the user has previously provided feedback. Furthermore, the providing unit analyzes the user's past feedback and suggests the optimal delivery method. This makes it possible to customize the delivery method by reflecting the user's past feedback.

[0119] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated user's emotions. The optimization unit uses AI to estimate the user's emotions and adjust the optimization method based on the estimated user's emotions. For example, if the user is relaxed, the optimization unit can optimize using soft expressions. Also, if the user is in a hurry, the optimization unit can optimize using concise and to-the-point expressions. Furthermore, if the user is excited, the optimization unit can optimize using energetic expressions. In this way, the optimization method can be adjusted based on the user's emotions.

[0120] The optimization unit can optimize the optimization algorithm by referring to past optimization data during optimization. The optimization unit uses AI to optimize the optimization algorithm by referring to past optimization data during optimization. For example, the optimization unit selects the optimal algorithm based on past optimization data. The optimization unit can also analyze past optimization data to improve the accuracy of the algorithm. Furthermore, the optimization unit can set optimal parameters by referring to past optimization data. This makes it possible to optimize the optimization algorithm by referring to past optimization data.

[0121] The optimization unit can update the optimization data by reflecting user feedback during optimization. The optimization unit uses AI to update the optimization data by reflecting user feedback during optimization. For example, the optimization unit updates the optimization data based on feedback provided by the user. The optimization unit can also analyze the user feedback and improve the optimization algorithm. Furthermore, the optimization unit reflects user feedback in real time and updates the optimization data. This allows the optimization data to be updated by reflecting user feedback.

[0122] The optimization unit can integrate information from different data sources to enrich the optimization data during optimization. The optimization unit uses AI to integrate information from different data sources to enrich the optimization data during optimization. For example, the optimization unit integrates information from different data sources to enrich the optimization data. The optimization unit can also analyze information from different data sources to improve the optimization algorithm. Furthermore, the optimization unit refers to information from different data sources to set optimal parameters. This allows the optimization data to be enriched by integrating information from different data sources.

[0123] The optimization unit can estimate the user's emotions and adjust the frequency of optimization based on the estimated user's emotions. The optimization unit uses AI to estimate the user's emotions and adjust the frequency of optimization based on the estimated user's emotions. For example, the optimization unit sets the frequency of optimization low when the user is relaxed. The optimization unit can also set the frequency of optimization high when the user is in a hurry. Furthermore, the optimization unit adjusts the frequency of optimization when the user is excited. In this way, the frequency of optimization can be adjusted based on the user's emotions.

[0124] The optimization unit can weight the optimization data based on the time of blog posting during optimization. The optimization unit uses AI to weight the optimization data based on the time of blog posting during optimization. For example, the optimization unit sets a high weighting of the optimization data for the most recent blog posts. The optimization unit can also set a high weighting of the optimization data for blog posts published during a period in which the user is particularly interested. Furthermore, the optimization unit sets a high weighting of the optimization data for blog posts related to a specific event. In this way, the optimization data can be weighted based on the time of blog posting.

[0125] The optimization unit can integrate information from different data sources to enrich the optimization data during optimization. The optimization unit uses AI to integrate information from different data sources to enrich the optimization data during optimization. For example, the optimization unit integrates information from different data sources to enrich the optimization data. The optimization unit can also analyze information from different data sources to improve the optimization algorithm. Furthermore, the optimization unit refers to information from different data sources to set optimal parameters. This allows the optimization data to be enriched by integrating information from different data sources.

[0126] The optimization unit can update the optimization data by reflecting user feedback during optimization. The optimization unit uses AI to update the optimization data by reflecting user feedback during optimization. For example, the optimization unit updates the optimization data based on feedback provided by the user. The optimization unit can also analyze the user feedback and improve the optimization algorithm. Furthermore, the optimization unit reflects user feedback in real time and updates the optimization data. This allows the optimization data to be updated by reflecting user feedback.

[0127] The feedback receiving unit can estimate the user's emotions and adjust the method of receiving feedback based on the estimated user emotions. The feedback receiving unit uses AI to estimate the user's emotions and adjust the method of receiving feedback based on the estimated user emotions. For example, if the user is relaxed, the feedback receiving unit provides an interface that requests detailed feedback. Also, if the user is in a hurry, the feedback receiving unit can provide a concise feedback form. Furthermore, if the user is excited, the feedback receiving unit provides feedback options that allow the user to express emotions. This makes it possible to adjust the method of receiving feedback based on the user's emotions.

[0128] The feedback receiving unit can select the optimal feedback receiving method by referring to the user's past feedback history when receiving feedback. The feedback receiving unit uses AI to select the optimal feedback receiving method by referring to the user's past feedback history when receiving feedback. For example, the feedback receiving unit provides the optimal feedback form based on the form of feedback provided by the user in the past. The feedback receiving unit can also preferentially suggest feedback methods that the user has given high ratings to in the past. Furthermore, the feedback receiving unit analyzes the user's past feedback history and suggests the optimal feedback receiving method. In this way, the optimal feedback receiving method can be selected by referring to the user's past feedback history.

[0129] The feedback receiving unit can customize the feedback content based on the user's current areas of interest when receiving the feedback. The feedback receiving unit uses AI to customize the feedback content based on the user's current areas of interest when receiving the feedback. For example, the feedback receiving unit provides feedback items related to topics that the user has recently been interested in. The feedback receiving unit can also provide related feedback items based on the user's past browsing history. Furthermore, the feedback receiving unit analyzes the user's social media activity and customizes the feedback items based on the user's areas of interest. This allows the feedback content to be customized based on the user's current areas of interest.

[0130] The feedback receiving unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback receiving unit uses AI to estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is excited, the feedback receiving unit can preferentially receive entertainment-related feedback. Also, if the user is relaxed, the feedback receiving unit can preferentially receive feedback on relaxing content. Furthermore, if the user is stressed, the feedback receiving unit can preferentially receive feedback on content that helps relieve stress. In this way, the priority of feedback can be determined based on the user's emotions.

[0131] The feedback receiving unit can select the optimal reception method in consideration of the user's geographical location information when receiving feedback. The feedback receiving unit uses AI to select the optimal reception method in consideration of the user's geographical location information when receiving feedback. For example, if the user is in a specific area, the feedback receiving unit can provide feedback items related to the area. Also, if the user is traveling, the feedback receiving unit can provide feedback items related to the travel destination. Furthermore, if the user is participating in a specific event, the feedback receiving unit can provide feedback items related to the event. This makes it possible to select the optimal reception method in consideration of the user's geographical location information.

[0132] The feedback receiving unit can analyze the user's social media activity and customize the feedback content when receiving the feedback. The feedback receiving unit uses AI to analyze the user's social media activity and customize the feedback content when receiving the feedback. For example, the feedback receiving unit provides feedback items related to content shared by the user on social media. The feedback receiving unit can also analyze the content posted by the user on social media and provide related feedback items. Furthermore, the feedback receiving unit provides related feedback items with reference to the activity of the user's friends on social media. This makes it possible to customize the feedback content by analyzing the user's social media activity.

[0133] The feedback receiving unit can customize the feedback receiving method by reflecting the user's past feedback when receiving feedback. The feedback receiving unit uses AI to customize the feedback receiving method by reflecting the user's past feedback when receiving feedback. For example, the feedback receiving unit preferentially suggests feedback methods that the user has given high ratings to in the past. The feedback receiving unit can also provide an optimal feedback form based on the form of feedback the user has provided in the past. Furthermore, the feedback receiving unit analyzes the user's past feedback history and suggests the optimal feedback receiving method. This makes it possible to customize the feedback receiving method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, adjustment unit, provision unit, optimization unit, and feedback reception unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows a user to input the URL of an overseas blog they wish to convert. The translation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the URL using a generation AI and translates the blog content into Japanese. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the translated content for Japanese users. The provision unit is realized by the output device 40 of the smart device 14, and provides the translated content to the user as a web page or PDF. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the translation algorithm. The feedback reception unit is realized by the reception device 38 of the smart device 14, and accepts feedback from the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, adjustment unit, provision unit, optimization unit, and feedback reception unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and allows the user to voice-input the URL of the overseas blog they wish to convert. The translation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the URL using a generation AI and translates the blog content into Japanese. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the translated content for Japanese users. The provision unit is realized by the speaker 240 of the smart glasses 214, and provides the translated content to the user as voice or text. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the translation algorithm. The feedback reception unit is realized by the microphone 238 of the smart glasses 214, and receives voice feedback from the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, adjustment unit, provision unit, optimization unit, and feedback reception unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and allows the user to voice-input the URL of the overseas blog they wish to convert. The translation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the URL using a generation AI and translates the blog content into Japanese. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the translated content for Japanese users. The provision unit is realized by the display 343 of the headset-type terminal 314, and visually presents the translated content to the user. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the translation algorithm. The feedback reception unit is realized by the microphone 238 of the headset-type terminal 314, and receives voice feedback from the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, adjustment unit, provision unit, optimization unit, and feedback reception unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and allows the user to voice-input the URL of the overseas blog they wish to convert. The translation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the URL using a generation AI and translates the blog content into Japanese. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the translated content for Japanese users. The provision unit is realized by the speaker 240 of the robot 414, and provides the translated content to the user as voice or text. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the translation algorithm. The feedback reception unit is realized by the microphone 238 of the robot 414, and receives voice feedback from the user.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The conversion system can estimate the user's emotions and adjust the translation tone based on the estimated emotions. For example, if the user is relaxed, the translation can be done in a soft tone. If the user is in a hurry, the translation can be done in a concise and to-the-point tone. Furthermore, if the user is excited, the translation can be done in an energetic tone. In this way, it is possible to provide a translation tone that matches the user's emotions.

[0136] The conversion system can analyze a user's past browsing history and provide relevant blog articles with priority. For example, it can suggest related blog articles based on the categories of blogs the user has previously viewed. It can also provide blog articles that the user has previously rated highly. Furthermore, it can predict and suggest blog articles that will be used during specific time periods based on the user's past browsing history. This makes it possible to provide the most suitable blog articles based on the user's past browsing history.

[0137] The conversion system can estimate the user's emotions and adjust the level of translation detail based on the estimated emotions. For example, if the user is relaxed, a detailed translation can be provided. If the user is in a hurry, a concise translation can be provided. Furthermore, if the user is excited, a translation with visually stimulating effects can be provided. In this way, the level of translation detail can be provided according to the user's emotions.

[0138] The conversion system can provide highly relevant blog articles with priority given to the user's geographical location information. For example, if the user is in a specific area, blog articles related to that area can be provided with priority. Also, if the user is traveling, blog articles related to the travel destination can be provided with priority. Furthermore, if the user is participating in a specific event, blog articles related to that event can be provided with priority. In this way, the optimal blog articles can be provided with priority given to the user's geographical location information.

[0139] The conversion system can estimate the user's emotions and determine the priority of blog articles to be provided based on the estimated emotions. For example, if the user is excited, entertainment-related blog articles can be provided preferentially. If the user is relaxed, blog articles with relaxing content can be provided preferentially. Furthermore, if the user is stressed, blog articles with content that helps relieve stress can be provided preferentially. In this way, the priority of blog articles to be provided can be determined based on the user's emotions.

[0140] The conversion system can analyze a user's social media activity and provide relevant blog articles. For example, it can prioritize providing blog articles that the user has shared on social media. It can also analyze the content of a user's social media posts and provide relevant blog articles. It can also provide relevant blog articles by taking into account the activities of the user's friends on social media. This makes it possible to provide the most appropriate blog articles based on the user's social media activity.

[0141] The conversion system can estimate the user's emotions and adjust the feedback acceptance method based on the estimated emotions. For example, if the user is relaxed, an interface that requests detailed feedback can be provided. If the user is in a hurry, a simple feedback form can be provided. Furthermore, if the user is excited, feedback options that allow the user to express emotions can be provided. This makes it possible to adjust the feedback acceptance method based on the user's emotions.

[0142] The conversion system can customize the reception method by reflecting the user's past feedback. For example, it can preferentially suggest feedback methods that the user has given a high rating in the past. It can also provide the optimal feedback form based on the feedback formats the user has provided in the past. Furthermore, it can analyze the user's past feedback history and suggest the optimal feedback reception method. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0143] The conversion system can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is relaxed, the optimization can be performed using soft expressions. If the user is in a hurry, the optimization can be performed using concise and to-the-point expressions. Furthermore, if the user is excited, the optimization can be performed using energetic expressions. In this way, the optimization method can be adjusted based on the user's emotions.

[0144] The conversion system can integrate information from different data sources to enrich the optimization data. For example, it can integrate information from different data sources to enrich the optimization data. It can also analyze information from different data sources to improve the optimization algorithm. It can also refer to information from different data sources to set optimal parameters. This allows it to integrate information from different data sources to enrich the optimization data.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit accepts the URL of the overseas blog that the user wants to convert. For example, the user can simply enter the URL of a specific blog post. Step 2: The translation unit uses the generation AI to analyze the URL received by the reception unit and translate the blog content into Japanese. For example, the generation AI analyzes the text of a blog post and translates it into appropriate Japanese. For example, the generation AI translates a travel blog post written in English into Japanese. Step 3: The adjustment department adjusts the content translated by the translation department for Japanese readers. For example, instead of translating English expressions directly into Japanese, they replace them with expressions that are easier for Japanese people to understand. They can also add appropriate background information to take cultural differences into account. This makes the translated blog post easier for Japanese people to understand.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives a URL; a translation unit that analyzes the URL received by the reception unit and translates the content of the blog; an adjustment unit that adjusts the content translated by the translation unit to suit Japanese people; A system characterized by:

2. A providing unit is provided to provide the translated content to the user.

2. The system of claim 1.

3. Equipped with a translation algorithm optimization section 2. The system of claim 1.

4. A feedback receiving unit is provided to receive feedback from users.

2. The system of claim 1.

5. The providing unit Providing translated content as a web page or PDF 3. The system of claim 2.

6. Equipped with an optimization unit that updates the learning data of the generation AI and improves translation accuracy 2. The system of claim 1.

7. Equipped with a feedback reception section that collects opinions or improvements from users and helps improve the system 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the timing of URL reception based on the estimated user emotions.

2. The system of claim 1.

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