System

Abstract

An object of the system according to the embodiment is to appropriately translate and reshape overseas newspaper articles for Japanese.SOLUTION: A system includes a reception part, a translation part, and a shaping part. The receiving unit receives an input from a user. The translation unit analyzes and translates the article received by the reception unit. The shaping unit shapes the article translated by the translation unit into a format suitable 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 format foreign newspaper articles for Japanese readers, and there is room for improvement.

[0005] The system according to the embodiment aims to translate and format foreign newspaper articles appropriately for Japanese readers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a translation unit, and a formatting unit. The reception unit receives input from a user. The translation unit analyzes and translates the article received by the reception unit. The formatting unit formats the article translated by the translation unit into a format suitable for Japanese readers. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately translate and format foreign newspaper articles 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 conversion system according to an embodiment of the present invention is a system that easily converts foreign newspaper articles into Japanese versions. In the conversion system, a user inputs a foreign newspaper article, and a generation AI analyzes the article, translates it into Japanese, and automatically formats it into a format suitable for Japanese readers. This mechanism makes it easy to convert foreign newspaper articles into Japanese versions. For example, in the conversion system, a user inputs an English newspaper article. This information is input to the generation AI. The generation AI then analyzes the input article and translates it into Japanese. The generation AI uses natural language processing technology to understand the content of the article and translate it into appropriate Japanese. For example, an English newspaper article is translated into Japanese. The translated article is automatically formatted into a format suitable for Japanese readers. The generation AI formats the content of the article into a format suitable for Japanese readers. For example, the AI ​​changes the article's layout and wording to make it easier for Japanese readers to understand. This mechanism makes it easy to convert foreign newspaper articles into Japanese versions. This allows the conversion system to read foreign newspaper articles in Japanese without requiring the user to perform complex operations. The conversion system translates an English newspaper article into Japanese and displays it in a format suitable for Japanese readers simply by the user inputting it. Users can read foreign newspaper articles in Japanese without having to perform complicated operations.

[0029] A conversion system according to an embodiment includes a reception unit, a translation unit, and a formatting unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, an English newspaper article input by the user in text format. The reception unit can also receive an article input by voice. For example, the reception unit converts the voice input into text data using voice recognition technology. The reception unit can also receive an article input by the user in image format. For example, the reception unit converts image data into text data using image recognition technology. The translation unit uses a generation AI to analyze and translate the article received by the reception unit. The translation is performed using, for example, natural language processing technology, but is not limited to, examples. For example, the generation AI analyzes the article using techniques such as morphological analysis, grammatical analysis, and semantic analysis, and translates it into appropriate Japanese. The translation unit can also use the generation AI to understand the content of the article and translate it into appropriate Japanese. For example, the generation AI understands the context of the article and translates it into natural Japanese. The formatting unit formats the article translated by the translation unit into a format suitable for Japanese readers. Formatting, for example, involves changing the layout and expressions of the article to a format that is easy for Japanese readers to understand, but is not limited to such an example. For example, the formatting unit adjusts the paragraph arrangement, font selection, image arrangement, etc. of the article. The formatting unit may also include a standard setting unit that sets standards for formatting the content of the article into a format suitable for Japanese readers. For example, the standard setting unit sets standards such as formatting rules and style guides. This allows the conversion system according to the embodiment to easily convert foreign newspaper articles into Japanese versions. This allows the user to read foreign newspaper articles in Japanese without performing complex operations.

[0030] The translation unit can analyze and translate articles using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the translation unit can analyze the words in an article using morphological analysis and translate them into appropriate Japanese. The translation unit can also analyze the sentence structure of an article using grammatical analysis and translate it into natural Japanese. The translation unit can also understand the content of an article using semantic analysis and translate it into appropriate Japanese. For example, the translation unit understands the context of an article and translates it into natural Japanese. In this way, the use of natural language processing technology improves the accuracy of article translation. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs an article into the generation AI, which analyzes and translates the article.

[0031] The formatting unit includes a standard setting unit that sets standards for formatting the translated article into a format appropriate for Japanese readers. The standard setting unit sets standards such as formatting rules and style guides. Examples of standards include, but are not limited to, paragraph placement, font selection, and image placement. For example, the standard setting unit sets paragraph placement to make the content of the article easier to read. The standard setting unit can also set font selection to improve the visibility of the article. The standard setting unit can also set image placement to make the content of the article easier to visually understand. Thus, by including the standard setting unit, the article can be formatted more appropriately. Some or all of the above-mentioned processing in the standard setting unit is performed using AI. For example, the standard setting unit inputs the standard settings into the AI, and the AI ​​sets the standards.

[0032] The formatting unit can format the layout and expression of an article into a format that is easy for Japanese people to understand. Adjustments to the layout and expression include, but are not limited to, for example, paragraph arrangement, font selection, and image arrangement. For example, the formatting unit adjusts the paragraph arrangement to make the content of the article easier to read. The formatting unit can also adjust the font selection to improve the visibility of the article. The formatting unit can also adjust the image arrangement to make the content of the article easier to visually understand. In this way, by changing the layout and expression of the article, it is formatted into a format that is easy for Japanese people to understand. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs adjustments to the layout and expression into the AI, and the AI ​​makes the adjustments.

[0033] The reception unit can analyze the user's past input history and select an appropriate input method. Analysis of the past input history includes, but is not limited to, analysis of log data and analysis of the user's behavioral patterns. For example, the reception unit can automatically display as candidates the types of newspapers and languages ​​that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of newspapers and languages ​​that will be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the past input history into AI, which then selects the input method.

[0034] When an article is input, the reception unit can filter the articles based on the user's current areas of interest. Identification of areas of interest can be based on, for example, survey results, past browsing history, etc., but is not limited to these examples. For example, the reception unit can preferentially display related articles based on the user's areas of interest (sports, politics, economics, etc.). The reception unit can also analyze the user's past browsing history and suggest articles related to the user's areas of interest. When the user inputs a specific keyword, the reception unit can filter and display articles related to the keyword. By filtering articles based on the user's areas of interest, highly relevant articles can be provided. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's areas of interest to AI, which then filters the articles.

[0035] When an article is input, the reception unit can select an appropriate input means according to the user's input method. The selection of the input method includes, but is not limited to, text input, voice input, image input, etc. For example, if the user selects voice input, the reception unit inputs the article using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze the article using image recognition technology and convert it into text. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input method to AI, which then selects the appropriate input means.

[0036] When inputting an article, the reception unit can prioritize inputting highly relevant articles based on the user's geographical location information. Examples of acquired geographical location information include, but are not limited to, GPS data and IP addresses. For example, the reception unit can prioritize displaying news articles related to the user's current location. Furthermore, the reception unit can also suggest articles about local events and happenings based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can prioritize displaying news articles related to the area the user is visiting. This allows for providing useful information to the user by providing highly relevant articles based on the geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs geographical location information to AI, which selects highly relevant articles.

[0037] The reception unit can analyze the user's social media activity when inputting an article and input related articles. Analysis of social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, display news articles related to accounts the user follows on social media. The reception unit can also analyze the content of the user's social media posts and suggest related news articles. The reception unit can also display related news articles based on the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide relevant articles to the user. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs social media activity to AI, which selects related articles.

[0038] When inputting an article, the reception unit can customize the input method by reflecting the user's past feedback. Examples of collected past feedback include, but are not limited to, survey results and user comments. For example, the reception unit preferentially suggests an input method for articles that the user has previously rated highly. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously expressed dissatisfaction with and suggest the optimal input method. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs past feedback to AI, and the AI ​​customizes the input method.

[0039] During translation, the translation unit can adjust the level of detail in the translation based on the importance of the article. Evaluation of importance includes, but is not limited to, for example, the urgency of the news and the level of interest to readers. For example, the translation unit provides a detailed translation for an important news article. The translation unit can also provide a concise translation for light reading material or entertainment articles. The translation unit can also provide a translation that includes background information and detailed explanations for feature articles. In this way, by adjusting the level of detail in the translation based on the importance of the article, an appropriate translation can be provided. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the importance of the article to the generation AI, and the generation AI adjusts the level of detail in the translation.

[0040] The translation unit can apply different translation algorithms depending on the article category during translation. Category classifications include, but are not limited to, news, sports, and entertainment. For example, in the case of a sports article, the translation unit applies an algorithm that accurately translates technical terms and player names. In addition, in the case of a political article, the translation unit can also apply an algorithm that accurately translates terms related to policies and laws. In addition, in the case of an economic article, the translation unit can also apply an algorithm that accurately translates terms related to economic indicators and market trends. In this way, by applying a translation algorithm depending on the article category, an appropriate translation can be provided. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the article category into the generation AI, which then applies an appropriate translation algorithm.

[0041] During translation, the translation unit can improve the accuracy of the translation by referring to the user's past translation results. Examples of collecting past translation results include, but are not limited to, translation history and user feedback. For example, the translation unit can refer to translation results that the user has previously rated highly and use similar expressions. The translation unit can also analyze the user's past translation results and apply an algorithm to avoid mistranslations. The translation unit can also improve the accuracy of the translation based on translation results that the user has previously corrected. In this way, the accuracy of the translation is improved by referring to the past translation results. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs past translation results into the generation AI, which then improves the accuracy of the translation.

[0042] During translation, the translation unit can determine the priority of translation based on the submission time of the article. Evaluation of the submission time includes, but is not limited to, for example, the submission date and time, urgency, etc. The translation unit, for example, prioritizes translating the latest news articles. The translation unit can also postpone articles that were submitted earlier. The translation unit can also prioritize translating articles with high urgency. In this way, by determining the priority of translation based on the submission time, appropriate translations can be provided. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the submission time to the generation AI, and the generation AI determines the priority of translation.

[0043] The translation unit can adjust the order of translation based on the relevance of articles during translation. Evaluation of relevance includes, but is not limited to, topic similarity and keyword co-occurrence. For example, the translation unit prioritizes translation of articles related to the user's field of interest. The translation unit can also prioritize translation of highly relevant articles based on the user's past browsing history. If the user inputs a specific keyword, the translation unit can also prioritize translation of articles related to the keyword. This allows for appropriate translation by adjusting the order of translation based on the relevance of articles. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the relevance of articles to the generation AI, which then adjusts the order of translation.

[0044] During translation, the translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise. Evaluation of the level of expertise includes, but is not limited to, the user's occupation, educational background, and past browsing history. For example, the translation unit can provide a translation that uses a lot of technical terminology to a user with high technical expertise. The translation unit can also provide a translation that uses simple expressions to a user with low technical expertise. The translation unit can also adjust the use of optimal technical terminology based on the user's past translation history. This allows for appropriate translations to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the user's level of expertise into the generation AI, which then adjusts the use of technical terminology.

[0045] During formatting, the formatting unit can select an appropriate layout based on the content of the article. Layout selection includes, but is not limited to, paragraph placement, font selection, and image placement. For example, in the case of a news article, the formatting unit provides a layout that emphasizes the headline. In addition, in the case of a feature article, the formatting unit can also provide a layout that makes extensive use of images and graphs. In addition, in the case of light reading material, the formatting unit can also provide a simple, easy-to-read layout. In this way, appropriate formatting can be provided by selecting the optimal layout based on the content of the article. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs the content of the article into AI, which then selects an appropriate layout.

[0046] During formatting, the formatting unit can apply different formatting algorithms depending on the article category. Category classifications include, but are not limited to, news, sports, and entertainment. For example, in the case of a sports article, the formatting unit provides a layout that emphasizes game results and player photos. In addition, in the case of a political article, the formatting unit can also provide a layout that emphasizes information about policies and laws. In addition, in the case of an economic article, the formatting unit can also provide a layout that emphasizes information about economic indicators and market trends. In this way, by applying a formatting algorithm depending on the article category, appropriate formatting can be provided. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs the article category to AI, which then applies an appropriate formatting algorithm.

[0047] The shaping unit can improve the accuracy of shaping by referring to the user's past shaping results. Examples of collecting past shaping results include, but are not limited to, shaping history and user feedback. For example, the shaping unit can provide a similar layout by referring to shaping results that the user has previously rated highly. The shaping unit can also analyze the user's past shaping results and apply an algorithm to avoid errors. The shaping unit can also improve the accuracy of shaping based on shaping results corrected by the user in the past. Thus, by referring to the past shaping results, the accuracy of shaping is improved. Some or all of the above-described processing in the shaping unit is performed using AI. For example, the shaping unit inputs past shaping results into AI, which then improves the accuracy of shaping.

[0048] The formatting unit can take into account the geographical distribution of the article when formatting. Evaluation of the geographical distribution includes, but is not limited to, regional data and geographical trends. For example, the formatting unit prioritizes formatting of news articles related to the region where the user is currently located. The formatting unit can also format articles about local events and happenings based on the user's geographical location information. If the user is traveling, the formatting unit can also prioritize formatting of news articles related to the region the user is visiting. This allows appropriate formatting to be provided by taking the geographical distribution of the article into consideration. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs geographical location information to AI, which then performs appropriate formatting.

[0049] The formatting unit can improve the accuracy of the formatting by referring to related literature of the article during formatting. References to related literature include, but are not limited to, academic papers, industry reports, etc. For example, the formatting unit can refer to literature related to the content of the article to provide accurate information. The formatting unit can also format the article by adding background information based on the related literature. The formatting unit can also refer to related literature and apply an algorithm to avoid errors. In this way, the accuracy of the formatting is improved by referring to related literature. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs related literature into AI, which then improves the accuracy of the formatting.

[0050] The formatting unit can format an article taking into account its market value. Market value evaluations include, but are not limited to, sales data, market trends, and the like. For example, the formatting unit prioritizes formatting of articles with high market value. The formatting unit can also postpone articles with low market value. The formatting unit can also adjust the layout and expression of the article based on the market value. This allows for appropriate formatting by taking market value into consideration. Some or all of the above-described processing in the formatting unit is performed using AI. For example, the formatting unit inputs market value into AI, which then performs appropriate formatting.

[0051] When setting the criteria, the criteria setting unit can select optimal criteria based on the content of the article. Content evaluation includes, for example, topic similarity, keyword co-occurrence, etc., but is not limited to these examples. For example, in the case of a news article, the criteria setting unit can set criteria that emphasize accuracy. In addition, in the case of a feature article, the criteria setting unit can also set criteria that emphasize detailed information. In addition, in the case of a light reading material, the criteria setting unit can also set criteria that emphasize readability. In this way, by selecting optimal criteria based on the content of the article, appropriate criteria can be provided. Some or all of the above-mentioned processing in the criteria setting unit is performed using AI. For example, the criteria setting unit inputs the content of the article into AI, and the AI ​​selects optimal criteria.

[0052] When setting standards, the standard setting unit can apply different standard setting algorithms depending on the category of the article. Category classifications include, but are not limited to, news, sports, and entertainment. For example, in the case of a sports article, the standard setting unit can set standards that emphasize information about game results and players. In addition, in the case of a political article, the standard setting unit can also set standards that emphasize information about policies and laws. In addition, in the case of an economic article, the standard setting unit can also set standards that emphasize information about economic indicators and market trends. In this way, by applying a standard setting algorithm depending on the category of the article, appropriate standards can be provided. Some or all of the above-mentioned processing in the standard setting unit is performed using AI. For example, the standard setting unit inputs the category of the article into AI, and the AI ​​applies an appropriate standard setting algorithm.

[0053] When setting the criteria, the criteria setting unit can set the criteria taking into account the geographic distribution of articles. Evaluation of geographic distribution includes, but is not limited to, regional data and geographic trends, for example. For example, the criteria setting unit prioritizes news articles related to the region where the user is currently located as the criteria. Furthermore, the criteria setting unit can also prioritize articles about local events and happenings based on the user's geographic location information. Furthermore, if the user is traveling, the criteria setting unit can prioritize news articles related to the region the user is visiting. This allows appropriate criteria to be provided by taking the geographic distribution of articles into consideration. Some or all of the above-described processing in the criteria setting unit is performed using AI. For example, the criteria setting unit inputs geographic location information to AI, which then sets appropriate criteria.

[0054] When setting standards, the standard setting unit can improve the accuracy of the standards by referring to related literature of the article. References to related literature include, but are not limited to, academic papers, industry reports, etc. For example, the standard setting unit can refer to literature related to the content of the article to set accurate standards. The standard setting unit can also set standards by adding background information of the article based on the related literature. The standard setting unit can also set standards to avoid errors by referring to related literature. In this way, the accuracy of the standards is improved by referring to related literature. Some or all of the above-mentioned processing in the standard setting unit is performed using AI. For example, the standard setting unit inputs related literature into AI, which then improves the accuracy of the standards.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can automatically adjust the difficulty level of the article based on the user's input. For example, if the user is a beginner, the reception unit can input the article using simple words and short sentences. On the other hand, if the user is an advanced user, the reception unit can input the article using technical terms and complex sentences. Furthermore, the reception unit can automatically select the topic of the article based on the user's input. For example, if the user is interested in sports, the reception unit will preferentially input articles related to sports. This makes it possible to input articles according to the user's skill level and interests.

[0057] The translation unit can analyze the user's past translation history and customize the translation style. For example, if the user has preferred formal translations in the past, the translation unit will translate in a formal style. On the other hand, if the user has preferred casual translations, the translation unit can also translate in a casual style. Furthermore, the translation unit can improve the accuracy of the translation based on user feedback. For example, if a user gives a high rating to a particular translation, the translation unit will use that style as a reference when translating. This allows the translation to be provided according to the user's preferences.

[0058] The formatter can automatically add visual elements based on the content of the article. For example, automatically inserting images or graphs related to the article. The formatter can also automatically adjust colors or font styles based on the content of the article. Furthermore, the formatter can add interactive elements based on the content of the article. For example, displaying related links or videos as the user reads the article. This improves the visual appeal of the article and deepens the user's understanding.

[0059] The formatter can automatically adjust the layout of an article depending on the user's device. For example, when viewing an article on a smartphone, the formatter can provide a portrait layout. When viewing an article on a tablet, the formatter can also provide a landscape layout. Furthermore, the formatter can automatically adjust the font size and image size depending on the screen size of the user's device. This allows articles to be viewed comfortably on any device.

[0060] The reception unit can analyze the user's past input history and select an appropriate input method. For example, it can automatically display as candidates the types of newspapers and languages ​​that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the types of newspapers and languages ​​that will be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, it is possible to provide the user with the optimal input method.

[0061] When an article is input, the reception unit can filter the articles based on the user's current areas of interest. For example, related articles can be preferentially displayed based on the user's areas of interest (sports, politics, economics, etc.). The reception unit can also analyze the user's past browsing history and suggest articles related to the user's areas of interest. Furthermore, when the user inputs a specific keyword, the reception unit can filter and display articles related to the keyword. In this way, highly relevant articles can be provided by filtering articles based on the user's areas of interest.

[0062] When an article is input, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the article is input using voice recognition technology. If the user selects text input, an interface that supports keyboard input can be provided. Furthermore, if the user selects image input, the reception unit can analyze the article using image recognition technology and convert it into text. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0063] When inputting an article, the reception unit can prioritize inputting highly relevant articles based on the user's geographical location information. For example, news articles related to the area where the user is currently located can be displayed preferentially. The reception unit can also suggest articles about local events and happenings based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can also prioritize displaying news articles related to the area the user is visiting. This makes it possible to provide useful information to the user by providing highly relevant articles based on the geographical location information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, etc. For example, the reception unit receives input of an English newspaper article in text format from the user. It is also possible to convert voice input into text data using voice recognition technology, or to convert image data into text data using image recognition technology. Step 2: The translation department uses the generation AI to analyze and translate the article received by the reception department. The translation is performed using natural language processing technology, analyzing the article using techniques such as morphological analysis, grammatical analysis, and semantic analysis, and translating it into appropriate Japanese. The generation AI can also understand the context of the article and translate it into natural-looking Japanese. Step 3: The formatting unit formats the article translated by the translation unit into a format suitable for Japanese readers. Formatting changes the layout and expression of the article to a format that is easy for Japanese people to understand. For example, it adjusts the arrangement of article paragraphs, font selection, image placement, etc. The formatting unit can also be equipped with a criteria setting unit that sets criteria for formatting the content of the article into a format suitable for Japanese readers.

[0066] (Example 2) A conversion system according to an embodiment of the present invention is a system that easily converts foreign newspaper articles into Japanese versions. In the conversion system, a user inputs a foreign newspaper article, and a generation AI analyzes the article, translates it into Japanese, and automatically formats it into a format suitable for Japanese readers. This mechanism makes it easy to convert foreign newspaper articles into Japanese versions. For example, in the conversion system, a user inputs an English newspaper article. This information is input to the generation AI. The generation AI then analyzes the input article and translates it into Japanese. The generation AI uses natural language processing technology to understand the content of the article and translate it into appropriate Japanese. For example, an English newspaper article is translated into Japanese. The translated article is automatically formatted into a format suitable for Japanese readers. The generation AI formats the content of the article into a format suitable for Japanese readers. For example, the AI ​​changes the article's layout and wording to make it easier for Japanese readers to understand. This mechanism makes it easy to convert foreign newspaper articles into Japanese versions. This allows the conversion system to read foreign newspaper articles in Japanese without requiring the user to perform complex operations. The conversion system translates an English newspaper article into Japanese and displays it in a format suitable for Japanese readers simply by the user inputting it. Users can read foreign newspaper articles in Japanese without having to perform complicated operations.

[0067] A conversion system according to an embodiment includes a reception unit, a translation unit, and a formatting unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, an English newspaper article input by the user in text format. The reception unit can also receive an article input by voice. For example, the reception unit converts the voice input into text data using voice recognition technology. The reception unit can also receive an article input by the user in image format. For example, the reception unit converts image data into text data using image recognition technology. The translation unit uses a generation AI to analyze and translate the article received by the reception unit. The translation is performed using, for example, natural language processing technology, but is not limited to, examples. For example, the generation AI analyzes the article using techniques such as morphological analysis, grammatical analysis, and semantic analysis, and translates it into appropriate Japanese. The translation unit can also use the generation AI to understand the content of the article and translate it into appropriate Japanese. For example, the generation AI understands the context of the article and translates it into natural Japanese. The formatting unit formats the article translated by the translation unit into a format suitable for Japanese readers. Formatting, for example, involves changing the layout and expressions of the article to a format that is easy for Japanese readers to understand, but is not limited to such an example. For example, the formatting unit adjusts the paragraph arrangement, font selection, image arrangement, etc. of the article. The formatting unit may also include a standard setting unit that sets standards for formatting the content of the article into a format suitable for Japanese readers. For example, the standard setting unit sets standards such as formatting rules and style guides. This allows the conversion system according to the embodiment to easily convert foreign newspaper articles into Japanese versions. This allows the user to read foreign newspaper articles in Japanese without performing complex operations.

[0068] The translation unit can analyze and translate articles using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the translation unit can analyze the words in an article using morphological analysis and translate them into appropriate Japanese. The translation unit can also analyze the sentence structure of an article using grammatical analysis and translate it into natural Japanese. The translation unit can also understand the content of an article using semantic analysis and translate it into appropriate Japanese. For example, the translation unit understands the context of an article and translates it into natural Japanese. In this way, the use of natural language processing technology improves the accuracy of article translation. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs an article into the generation AI, which analyzes and translates the article.

[0069] The formatting unit includes a standard setting unit that sets standards for formatting the translated article into a format appropriate for Japanese readers. The standard setting unit sets standards such as formatting rules and style guides. Examples of standards include, but are not limited to, paragraph placement, font selection, and image placement. For example, the standard setting unit sets paragraph placement to make the content of the article easier to read. The standard setting unit can also set font selection to improve the visibility of the article. The standard setting unit can also set image placement to make the content of the article easier to visually understand. Thus, by including the standard setting unit, the article can be formatted more appropriately. Some or all of the above-mentioned processing in the standard setting unit is performed using AI. For example, the standard setting unit inputs the standard settings into the AI, and the AI ​​sets the standards.

[0070] The formatting unit can format the layout and expression of an article into a format that is easy for Japanese people to understand. Adjustments to the layout and expression include, but are not limited to, for example, paragraph arrangement, font selection, and image arrangement. For example, the formatting unit adjusts the paragraph arrangement to make the content of the article easier to read. The formatting unit can also adjust the font selection to improve the visibility of the article. The formatting unit can also adjust the image arrangement to make the content of the article easier to visually understand. In this way, by changing the layout and expression of the article, it is formatted into a format that is easy for Japanese people to understand. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs adjustments to the layout and expression into the AI, and the AI ​​makes the adjustments.

[0071] The reception unit can estimate the user's emotions and adjust the timing of article input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can simplify article input, allowing the user to input the article in a short time. Furthermore, if the user is relaxed, the reception unit can provide detailed input options, allowing the user to customize the content of the article. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to input the article quickly. This allows for more appropriate input by adjusting the timing of article input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's emotions to AI, which then estimates the emotions and adjusts the input timing.

[0072] The reception unit can analyze the user's past input history and select an appropriate input method. Analysis of the past input history includes, but is not limited to, analysis of log data and analysis of the user's behavioral patterns. For example, the reception unit can automatically display as candidates the types of newspapers and languages ​​that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of newspapers and languages ​​that will be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the past input history into AI, which then selects the input method.

[0073] When an article is input, the reception unit can filter the articles based on the user's current areas of interest. Identification of areas of interest can be based on, for example, survey results, past browsing history, etc., but is not limited to these examples. For example, the reception unit can preferentially display related articles based on the user's areas of interest (sports, politics, economics, etc.). The reception unit can also analyze the user's past browsing history and suggest articles related to the user's areas of interest. When the user inputs a specific keyword, the reception unit can filter and display articles related to the keyword. By filtering articles based on the user's areas of interest, highly relevant articles can be provided. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's areas of interest to AI, which then filters the articles.

[0074] When an article is input, the reception unit can select an appropriate input means according to the user's input method. The selection of the input method includes, but is not limited to, text input, voice input, image input, etc. For example, if the user selects voice input, the reception unit inputs the article using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze the article using image recognition technology and convert it into text. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input method to AI, which then selects the appropriate input means.

[0075] The reception unit can estimate the user's emotions and determine the priority of articles to be input based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize displaying the latest news articles. Furthermore, if the user is relaxed, the reception unit can prioritize displaying interesting feature articles. Furthermore, if the user is stressed, the reception unit can prioritize displaying light reading material or entertainment articles. This allows for article prioritization based on the user's emotions, thereby providing more appropriate articles. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's emotions to AI, which then estimates the emotions and determines the priority of articles.

[0076] When inputting an article, the reception unit can prioritize inputting highly relevant articles based on the user's geographical location information. Examples of acquired geographical location information include, but are not limited to, GPS data and IP addresses. For example, the reception unit can prioritize displaying news articles related to the user's current location. Furthermore, the reception unit can also suggest articles about local events and happenings based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can prioritize displaying news articles related to the area the user is visiting. This allows for providing useful information to the user by providing highly relevant articles based on the geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs geographical location information to AI, which selects highly relevant articles.

[0077] The reception unit can analyze the user's social media activity when inputting an article and input related articles. Analysis of social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, display news articles related to accounts the user follows on social media. The reception unit can also analyze the content of the user's social media posts and suggest related news articles. The reception unit can also display related news articles based on the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide relevant articles to the user. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs social media activity to AI, which selects related articles.

[0078] When inputting an article, the reception unit can customize the input method by reflecting the user's past feedback. Examples of collected past feedback include, but are not limited to, survey results and user comments. For example, the reception unit preferentially suggests an input method for articles that the user has previously rated highly. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously expressed dissatisfaction with and suggest the optimal input method. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs past feedback to AI, and the AI ​​customizes the input method.

[0079] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is relaxed, the translation unit can use softer expressions to translate. If the user is in a hurry, the translation unit can also provide a concise and to-the-point translation. If the user is excited, the translation unit can also provide an expression that emphasizes the emotion. This allows for adjusting the translation expression according to the user's emotions, thereby providing a more appropriate translation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit is performed using the generation AI. For example, the translation unit inputs the user's emotions into the generation AI, which then estimates the emotions and adjusts the translation expression.

[0080] During translation, the translation unit can adjust the level of detail in the translation based on the importance of the article. Evaluation of importance includes, but is not limited to, for example, the urgency of the news and the level of interest to readers. For example, the translation unit provides a detailed translation for an important news article. The translation unit can also provide a concise translation for light reading material or entertainment articles. The translation unit can also provide a translation that includes background information and detailed explanations for feature articles. In this way, by adjusting the level of detail in the translation based on the importance of the article, an appropriate translation can be provided. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the importance of the article to the generation AI, and the generation AI adjusts the level of detail in the translation.

[0081] The translation unit can apply different translation algorithms depending on the article category during translation. Category classifications include, but are not limited to, news, sports, and entertainment. For example, in the case of a sports article, the translation unit applies an algorithm that accurately translates technical terms and player names. In addition, in the case of a political article, the translation unit can also apply an algorithm that accurately translates terms related to policies and laws. In addition, in the case of an economic article, the translation unit can also apply an algorithm that accurately translates terms related to economic indicators and market trends. In this way, by applying a translation algorithm depending on the article category, an appropriate translation can be provided. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the article category into the generation AI, which then applies an appropriate translation algorithm.

[0082] During translation, the translation unit can improve the accuracy of the translation by referring to the user's past translation results. Examples of collecting past translation results include, but are not limited to, translation history and user feedback. For example, the translation unit can refer to translation results that the user has previously rated highly and use similar expressions. The translation unit can also analyze the user's past translation results and apply an algorithm to avoid mistranslations. The translation unit can also improve the accuracy of the translation based on translation results that the user has previously corrected. In this way, the accuracy of the translation is improved by referring to the past translation results. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs past translation results into the generation AI, which then improves the accuracy of the translation.

[0083] The translation unit can 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 can provide a short, to-the-point translation. If the user is relaxed, the translation unit can also provide a longer translation with detailed explanations. If the user is excited, the translation unit can also translate using expressions that emphasize the emotions. This allows for an appropriate translation by adjusting the length of the translation according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the translation unit is performed using the generation AI. For example, the translation unit inputs the user's emotions into the generation AI, which then estimates the emotions and adjusts the length of the translation.

[0084] During translation, the translation unit can determine the priority of translation based on the submission time of the article. Evaluation of the submission time includes, but is not limited to, for example, the submission date and time, urgency, etc. The translation unit, for example, prioritizes translating the latest news articles. The translation unit can also postpone articles that were submitted earlier. The translation unit can also prioritize translating articles with high urgency. In this way, by determining the priority of translation based on the submission time, appropriate translations can be provided. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the submission time to the generation AI, and the generation AI determines the priority of translation.

[0085] The translation unit can adjust the order of translation based on the relevance of articles during translation. Evaluation of relevance includes, but is not limited to, topic similarity and keyword co-occurrence. For example, the translation unit prioritizes translation of articles related to the user's field of interest. The translation unit can also prioritize translation of highly relevant articles based on the user's past browsing history. If the user inputs a specific keyword, the translation unit can also prioritize translation of articles related to the keyword. This allows for appropriate translation by adjusting the order of translation based on the relevance of articles. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the relevance of articles to the generation AI, which then adjusts the order of translation.

[0086] During translation, the translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise. Evaluation of the level of expertise includes, but is not limited to, the user's occupation, educational background, and past browsing history. For example, the translation unit can provide a translation that uses a lot of technical terminology to a user with high technical expertise. The translation unit can also provide a translation that uses simple expressions to a user with low technical expertise. The translation unit can also adjust the use of optimal technical terminology based on the user's past translation history. This allows for appropriate translations to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the translation unit is performed using a generation AI. For example, the translation unit inputs the user's level of expertise into the generation AI, which then adjusts the use of technical terminology.

[0087] The shaping unit can estimate the user's emotions and adjust the shaping method based on the estimated user emotions. For example, if the user is relaxed, the shaping unit can provide a layout with soft colors. If the user is in a hurry, the shaping unit can also provide a simple, highly visible layout. If the user is excited, the shaping unit can also provide a visually stimulating layout. This allows appropriate shaping to be provided by adjusting the shaping method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the shaping unit is performed using AI. For example, the shaping unit inputs the user's emotions into AI, which then estimates the emotions and adjusts the shaping method.

[0088] During formatting, the formatting unit can select an appropriate layout based on the content of the article. Layout selection includes, but is not limited to, paragraph placement, font selection, and image placement. For example, in the case of a news article, the formatting unit provides a layout that emphasizes the headline. In addition, in the case of a feature article, the formatting unit can also provide a layout that makes extensive use of images and graphs. In addition, in the case of light reading material, the formatting unit can also provide a simple, easy-to-read layout. In this way, appropriate formatting can be provided by selecting the optimal layout based on the content of the article. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs the content of the article into AI, which then selects an appropriate layout.

[0089] During formatting, the formatting unit can apply different formatting algorithms depending on the article category. Category classifications include, but are not limited to, news, sports, and entertainment. For example, in the case of a sports article, the formatting unit provides a layout that emphasizes game results and player photos. In addition, in the case of a political article, the formatting unit can also provide a layout that emphasizes information about policies and laws. In addition, in the case of an economic article, the formatting unit can also provide a layout that emphasizes information about economic indicators and market trends. In this way, by applying a formatting algorithm depending on the article category, appropriate formatting can be provided. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs the article category to AI, which then applies an appropriate formatting algorithm.

[0090] The shaping unit can improve the accuracy of shaping by referring to the user's past shaping results. Examples of collecting past shaping results include, but are not limited to, shaping history and user feedback. For example, the shaping unit can provide a similar layout by referring to shaping results that the user has previously rated highly. The shaping unit can also analyze the user's past shaping results and apply an algorithm to avoid errors. The shaping unit can also improve the accuracy of shaping based on shaping results corrected by the user in the past. Thus, by referring to the past shaping results, the accuracy of shaping is improved. Some or all of the above-described processing in the shaping unit is performed using AI. For example, the shaping unit inputs past shaping results into AI, which then improves the accuracy of shaping.

[0091] The shaping unit can estimate the user's emotions and determine the priority of shaping based on the estimated user emotions. For example, if the user is excited, the shaping unit can prioritize shaping the latest news articles. Furthermore, if the user is relaxed, the shaping unit can prioritize shaping feature articles. Furthermore, if the user is stressed, the shaping unit can prioritize shaping light reading material or entertainment articles. By determining the priority of shaping according to the user's emotions, appropriate shaping can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the shaping unit is performed using AI. For example, the shaping unit inputs the user's emotions into AI, which then estimates the emotions and determines the priority of shaping.

[0092] The formatting unit can take into account the geographical distribution of the article when formatting. Evaluation of the geographical distribution includes, but is not limited to, regional data and geographical trends. For example, the formatting unit prioritizes formatting of news articles related to the region where the user is currently located. The formatting unit can also format articles about local events and happenings based on the user's geographical location information. If the user is traveling, the formatting unit can also prioritize formatting of news articles related to the region the user is visiting. This allows appropriate formatting to be provided by taking the geographical distribution of the article into consideration. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs geographical location information to AI, which then performs appropriate formatting.

[0093] The formatting unit can improve the accuracy of the formatting by referring to related literature of the article during formatting. References to related literature include, but are not limited to, academic papers, industry reports, etc. For example, the formatting unit can refer to literature related to the content of the article to provide accurate information. The formatting unit can also format the article by adding background information based on the related literature. The formatting unit can also refer to related literature and apply an algorithm to avoid errors. In this way, the accuracy of the formatting is improved by referring to related literature. Some or all of the above-mentioned processing in the formatting unit is performed using AI. For example, the formatting unit inputs related literature into AI, which then improves the accuracy of the formatting.

[0094] The formatting unit can format an article taking into account its market value. Market value evaluations include, but are not limited to, sales data, market trends, and the like. For example, the formatting unit prioritizes formatting of articles with high market value. The formatting unit can also postpone articles with low market value. The formatting unit can also adjust the layout and expression of the article based on the market value. This allows for appropriate formatting by taking market value into consideration. Some or all of the above-described processing in the formatting unit is performed using AI. For example, the formatting unit inputs market value into AI, which then performs appropriate formatting.

[0095] The standard setting unit can estimate the user's emotions and adjust the standard settings based on the estimated user emotions. For example, if the user is relaxed, the standard setting unit can set a soft standard. If the user is in a hurry, the standard setting unit can also set a quick standard. If the user is excited, the standard setting unit can also set a standard that emphasizes the emotion. This allows the standard settings to be adjusted according to the user's emotions, thereby providing appropriate standards. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the standard setting unit is performed using AI. For example, the standard setting unit inputs the user's emotions into AI, which then estimates the emotions and adjusts the standard settings.

[0096] When setting the criteria, the criteria setting unit can select optimal criteria based on the content of the article. Content evaluation includes, for example, topic similarity, keyword co-occurrence, etc., but is not limited to these examples. For example, in the case of a news article, the criteria setting unit can set criteria that emphasize accuracy. In addition, in the case of a feature article, the criteria setting unit can also set criteria that emphasize detailed information. In addition, in the case of a light reading material, the criteria setting unit can also set criteria that emphasize readability. In this way, by selecting optimal criteria based on the content of the article, appropriate criteria can be provided. Some or all of the above-mentioned processing in the criteria setting unit is performed using AI. For example, the criteria setting unit inputs the content of the article into AI, and the AI ​​selects optimal criteria.

[0097] When setting standards, the standard setting unit can apply different standard setting algorithms depending on the category of the article. Category classifications include, but are not limited to, news, sports, and entertainment. For example, in the case of a sports article, the standard setting unit can set standards that emphasize information about game results and players. In addition, in the case of a political article, the standard setting unit can also set standards that emphasize information about policies and laws. In addition, in the case of an economic article, the standard setting unit can also set standards that emphasize information about economic indicators and market trends. In this way, by applying a standard setting algorithm depending on the category of the article, appropriate standards can be provided. Some or all of the above-mentioned processing in the standard setting unit is performed using AI. For example, the standard setting unit inputs the category of the article into AI, and the AI ​​applies an appropriate standard setting algorithm.

[0098] The criteria setting unit can estimate the user's emotions and determine the priority of criteria based on the estimated user emotions. For example, if the user is excited, the criteria setting unit can prioritize the latest news articles as the criteria. Furthermore, if the user is relaxed, the criteria setting unit can prioritize feature articles as the criteria. Furthermore, if the user is stressed, the criteria setting unit can prioritize light reading material or entertainment articles as the criteria. Thus, by determining the priority of criteria according to the user's emotions, appropriate criteria can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the criteria setting unit is performed using AI. For example, the criteria setting unit inputs the user's emotions into AI, which then estimates the emotions and determines the priority of criteria.

[0099] When setting the criteria, the criteria setting unit can set the criteria taking into account the geographic distribution of articles. Evaluation of geographic distribution includes, but is not limited to, regional data and geographic trends, for example. For example, the criteria setting unit prioritizes news articles related to the region where the user is currently located as the criteria. Furthermore, the criteria setting unit can also prioritize articles about local events and happenings based on the user's geographic location information. Furthermore, if the user is traveling, the criteria setting unit can prioritize news articles related to the region the user is visiting. This allows appropriate criteria to be provided by taking the geographic distribution of articles into consideration. Some or all of the above-described processing in the criteria setting unit is performed using AI. For example, the criteria setting unit inputs geographic location information to AI, which then sets appropriate criteria.

[0100] When setting standards, the standard setting unit can improve the accuracy of the standards by referring to related literature of the article. References to related literature include, but are not limited to, academic papers, industry reports, etc. For example, the standard setting unit can refer to literature related to the content of the article to set accurate standards. The standard setting unit can also set standards by adding background information of the article based on the related literature. The standard setting unit can also set standards to avoid errors by referring to related literature. In this way, the accuracy of the standards is improved by referring to related literature. Some or all of the above-mentioned processing in the standard setting unit is performed using AI. For example, the standard setting unit inputs related literature into AI, which then improves the accuracy of the standards. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, formatting unit, and standard setting unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a user's input of an English newspaper article in text format. For example, in the case of voice input, the microphone 38B of the smart device 14 is used to convert the voice into text data. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and translates the article using a generation AI. The formatting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formats the translated article into a format suitable for Japanese readers. The standard setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets standards such as formatting rules and style guides. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, formatting unit, and standard setting unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's voice input of an English newspaper article. For example, in the case of voice input, the microphone 238 of the smart glasses 214 is used to convert the voice into text data. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and translates the article using a generative AI. The formatting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formats the translated article into a format suitable for Japanese readers. The standard setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets standards such as formatting rules and style guides. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, formatting unit, and standard setting unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a user's voice input of an English newspaper article. For example, in the case of voice input, the microphone 238 of the headset-type terminal 314 is used to convert the voice into text data. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and translates the article using a generative AI. The formatting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formats the translated article into a format suitable for Japanese readers. The standard setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets standards such as formatting rules and style guides. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, translation unit, formatting unit, and standard setting unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a user's voice input of an English newspaper article. For example, in the case of voice input, the microphone 238 of the robot 414 is used to convert the voice into text data. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and translates the article using a generative AI. The formatting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formats the translated article into a format suitable for Japanese readers. The standard setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets standards such as formatting rules and style guides.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can automatically adjust the difficulty level of the article based on the user's input. For example, if the user is a beginner, the reception unit can input the article using simple words and short sentences. On the other hand, if the user is an advanced user, the reception unit can input the article using technical terms and complex sentences. Furthermore, the reception unit can automatically select the topic of the article based on the user's input. For example, if the user is interested in sports, the reception unit will preferentially input articles related to sports. This makes it possible to input articles according to the user's skill level and interests.

[0103] The translation unit can analyze the user's past translation history and customize the translation style. For example, if the user has preferred formal translations in the past, the translation unit will translate in a formal style. On the other hand, if the user has preferred casual translations, the translation unit can also translate in a casual style. Furthermore, the translation unit can improve the accuracy of the translation based on user feedback. For example, if a user gives a high rating to a particular translation, the translation unit will use that style as a reference when translating. This allows the translation to be provided according to the user's preferences.

[0104] The formatter can automatically add visual elements based on the content of the article. For example, automatically inserting images or graphs related to the article. The formatter can also automatically adjust colors or font styles based on the content of the article. Furthermore, the formatter can add interactive elements based on the content of the article. For example, displaying related links or videos as the user reads the article. This improves the visual appeal of the article and deepens the user's understanding.

[0105] The formatter can automatically adjust the layout of an article depending on the user's device. For example, when viewing an article on a smartphone, the formatter can provide a portrait layout. When viewing an article on a tablet, the formatter can also provide a landscape layout. Furthermore, the formatter can automatically adjust the font size and image size depending on the screen size of the user's device. This allows articles to be viewed comfortably on any device.

[0106] The reception unit can estimate the user's emotions and customize the article input method based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple input method. If the user is relaxed, the reception unit can also provide detailed input options. Furthermore, if the user is excited, the reception unit can prioritize voice input to enable quick article input. This makes it possible to provide the optimal input method according to the user's emotions.

[0107] The reception unit can analyze the user's past input history and select an appropriate input method. For example, it can automatically display as candidates the types of newspapers and languages ​​that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the types of newspapers and languages ​​that will be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, it is possible to provide the user with the optimal input method.

[0108] When an article is input, the reception unit can filter the articles based on the user's current areas of interest. For example, related articles can be preferentially displayed based on the user's areas of interest (sports, politics, economics, etc.). The reception unit can also analyze the user's past browsing history and suggest articles related to the user's areas of interest. Furthermore, when the user inputs a specific keyword, the reception unit can filter and display articles related to the keyword. In this way, highly relevant articles can be provided by filtering articles based on the user's areas of interest.

[0109] When an article is input, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the article is input using voice recognition technology. If the user selects text input, an interface that supports keyboard input can be provided. Furthermore, if the user selects image input, the reception unit can analyze the article using image recognition technology and convert it into text. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0110] The reception unit can estimate the user's emotions and determine the priority of articles to be input based on the estimated user emotions. For example, if the user is excited, the latest news articles can be displayed with priority. Also, if the user is relaxed, interesting feature articles can be displayed with priority. Furthermore, if the user is stressed, light reading material or entertainment articles can be displayed with priority. In this way, by determining the priority of articles according to the user's emotions, more appropriate articles can be provided.

[0111] When inputting an article, the reception unit can prioritize inputting highly relevant articles based on the user's geographical location information. For example, news articles related to the area where the user is currently located can be displayed preferentially. The reception unit can also suggest articles about local events and happenings based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can also prioritize displaying news articles related to the area the user is visiting. This makes it possible to provide useful information to the user by providing highly relevant articles based on the geographical location information.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, etc. For example, the reception unit receives input of an English newspaper article in text format from the user. It is also possible to convert voice input into text data using voice recognition technology, or to convert image data into text data using image recognition technology. Step 2: The translation department uses the generation AI to analyze and translate the article received by the reception department. The translation is performed using natural language processing technology, analyzing the article using techniques such as morphological analysis, grammatical analysis, and semantic analysis, and translating it into appropriate Japanese. The generation AI can also understand the context of the article and translate it into natural-looking Japanese. Step 3: The formatting unit formats the article translated by the translation unit into a format suitable for Japanese readers. Formatting changes the layout and expression of the article to a format that is easy for Japanese people to understand. For example, it adjusts the arrangement of article paragraphs, font selection, image placement, etc. The formatting unit can also be equipped with a criteria setting unit that sets criteria for formatting the content of the article into a format suitable for Japanese readers.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[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 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.

[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 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.

[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 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.

[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] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 input from a user; a translation unit that analyzes and translates the article received by the reception unit; a formatting unit that formats the article translated by the translation unit into a format suitable for Japanese readers; Equipped with A system characterized by:

2. The translation unit Analyze and translate articles using natural language processing technology 2. The system of claim 1.

3. The shaping unit is It has a standards setting department that sets standards for formatting translated articles in an appropriate format for Japanese readers.

2. The system of claim 1.

4. The shaping unit is Formatting the layout and expressions of articles to make them easier for Japanese people to understand 2. The system of claim 1.

5. The reception unit Estimate user emotions and adjust the timing of article input based on the estimated user emotions 2. The system of claim 1.

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

7. The reception unit Filter articles based on the user's current interests as they are entered 2. The system of claim 1.

8. The reception unit When entering an article, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

9. The reception unit Estimate user sentiment and prioritize articles to be entered based on the estimated user sentiment 2. The system of claim 1.

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