system

Abstract

The system according to this embodiment aims to automate language translation and cultural adaptation, and to efficiently adjust content to suit each market. [Solution] The system according to the embodiment comprises a translation unit, an adjustment unit, and an integration unit. The translation unit automatically translates languages ​​and optimizes content considering cultural background. The adjustment unit adjusts the content optimized by the translation unit to suit the specific culture and preferences of each region. The adjustment unit adjusts the content adjusted by the adjustment unit to suit each market, including the story, characters, and settings of games and media. The integration unit seamlessly integrates with other tools. The adjustment unit adjusts product manuals and e-learning materials according to local standards.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that language translation alone has limitations in cultural adaptation, and it takes time and effort to adjust content for each national market.

[0005] The system according to the embodiment aims to automate language translation and cultural adaptation and efficiently perform content adjustment according to each market.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a translation unit, an adjustment unit, and an integration unit. The translation unit automatically translates languages ​​and optimizes content considering cultural background. The adjustment unit adjusts the content optimized by the translation unit to suit the specific culture and preferences of each region. The adjustment unit adjusts the content adjusted by the adjustment unit to suit each market, including the story, characters, and settings of games and media. The integration unit seamlessly integrates with other tools. The adjustment unit adjusts product manuals and e-learning materials according to local standards. [Effects of the Invention]

[0007] The system according to this embodiment can automate language translation and cultural adaptation, and efficiently adjust content to suit each market. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The content localization system according to an embodiment of the present invention is a system that uses AI to streamline multilingual and multicultural content localization and automates content adjustments for each country's market. First, the AI ​​automatically translates the language and optimizes the content considering the cultural background. Next, it adjusts advertising copy and design to suit the specific culture and preferences of each region. Furthermore, it adjusts the story, characters, and settings of games and media to suit each market. It also establishes an efficient work process by seamlessly integrating with other tools. Finally, it adjusts product manuals and e-learning materials according to local standards. This mechanism enables efficient and high-quality localization, strengthens competitiveness with customized experiences tailored to each market, and achieves multilingual support while maintaining identity. For example, the AI ​​automatically translates the language and optimizes the content considering the cultural background. In this process, the AI ​​uses natural language processing and cultural analysis to accurately understand the language and culture and perform appropriate translations. For example, in English-to-Japanese translation, it can not only replace words but also change expressions to suit Japanese culture and customs. This improves translation quality and avoids cultural misunderstandings. Next, advertising copy and design are adjusted to suit the specific culture and preferences of each region. For example, advertisements for the American market use casual and approachable language, while advertisements for the Japanese market use polite and courteous language. By adjusting content to the culture and preferences of each market in this way, more effective marketing becomes possible. Furthermore, the story, characters, and settings of games and media are adjusted to suit each market. For example, games for the American market feature storylines that emphasize action and adventure, while games for the Japanese market feature storylines that emphasize emotions and relationships. By adjusting the content of games and media to suit the culture and preferences of each market in this way, content that is accepted by a wider range of users can be provided. In addition, an efficient work process is established by seamlessly integrating with other tools. For example, by using translation memory, previously translated content can be reused to produce consistent translations.Furthermore, using a global content management tool allows for centralized management and efficient distribution of content for multiple languages ​​and markets. Finally, product manuals and e-learning materials are adapted to local standards. For example, a product manual for the Japanese market would include content based on Japanese standards and laws, while a product manual for the American market would include content based on American standards and laws. By adapting content to the standards of each market in this way, more relevant information can be provided. This enables the content localization system to perform efficient and high-quality localization, strengthen competitiveness with customized experiences tailored to each market, and achieve multilingual support while maintaining identity. As a result, the content localization system can efficiently provide multilingual content when companies aiming for global expansion need to create culturally sensitive content for each market. It also allows developers and marketers to collaborate to create content that considers both technical and cultural aspects. This can be expected to lead to business expansion in the global market.

[0029] The content localization system according to this embodiment comprises a translation unit, an adjustment unit, and an integration unit. The translation unit automatically translates languages ​​and optimizes content considering cultural background. The translation unit analyzes input text using, for example, natural language processing technology and generates appropriate translations. For example, in English-to-Japanese translation, the translation unit can not only replace words but also change expressions to suit Japanese culture and customs. The translation unit can also use cultural analysis technology to consider the cultural background of the target text and select appropriate expressions. For example, the translation unit can accurately translate idioms and expressions in a particular culture. The adjustment unit adjusts the content optimized by the translation unit to suit the specific culture and preferences of each region. For example, the adjustment unit adjusts advertising copy and design to suit the culture and preferences of each market. For example, the adjustment unit uses casual and friendly language in advertisements for the American market, while using polite and courteous language in advertisements for the Japanese market. The adjustment unit can also adjust the story, characters, and settings of games and media to suit each market. For example, the adjustment unit can develop storylines that emphasize action and adventure for games intended for the American market, while developing storylines that emphasize emotions and relationships for games intended for the Japanese market. Furthermore, the adjustment unit can also adjust product manuals and e-learning materials according to local standards. For example, the adjustment unit can include content based on Japanese standards and laws in product manuals for the Japanese market, while including content based on American standards and laws in product manuals for the American market. The integration unit seamlessly integrates with other tools. For example, the integration unit can reuse previously translated content using translation memory to ensure consistent translations. In addition, the integration unit can centrally manage and efficiently distribute content for multiple languages ​​and markets using a global content management tool. As a result, the content localization system according to this embodiment enables efficient and high-quality localization, strengthens competitiveness with customized experiences tailored to each market, and achieves multilingual support while maintaining identity.

[0030] The translation department automatically translates languages ​​and optimizes content by considering cultural context. For example, it uses natural language processing (NLP) technology to analyze input text and generate appropriate translations. Specifically, NLP technology includes morphological analysis, grammatical analysis, and semantic analysis, which are combined to understand the structure and meaning of the text. For example, in English-to-Japanese translation, it can not only replace words but also change expressions to suit Japanese culture and customs. For instance, when translating "How are you?" from English to Japanese, it can change it from "How are you?" to more natural expressions such as "How have you been lately?" or "How are things going?" depending on the situation. Furthermore, the translation department can use cultural analysis technology to consider the cultural background of the target text and select appropriate expressions. For example, it can accurately translate idioms and expressions in a specific culture. For instance, when translating "break the ice" from English to Japanese, it can change it from a direct translation of "break the ice" to a more appropriate expression such as "to lighten the mood." In addition, the translation department can generate more natural and fluent translations using generative AI. Generative AI has the ability to learn from large amounts of text data and generate appropriate translations that are relevant to the context. For example, it can learn from texts of various genres, such as news articles, technical documents, and literary works, and generate appropriate translations that are relevant to each context. This allows the translation department to provide high-quality translations that take cultural background and context into consideration, rather than simply mechanical translations.

[0031] The Coordination Department adjusts content optimized by the Translation Department to suit the specific culture and preferences of each region. For example, the Coordination Department adjusts advertising copy and design to match the culture and preferences of each market. Specifically, advertisements for the American market use casual and friendly language, while advertisements for the Japanese market use polite and courteous language. For example, if the advertising copy for the American market is "Get the best deal now!", it can be changed to "Get the best deals now!" for the Japanese market. The Coordination Department can also adjust the story, characters, and settings of games and media to suit each market. For example, a game for the American market may feature a story that emphasizes action and adventure, while a game for the Japanese market may feature a story that emphasizes emotions and relationships. For example, if a game character for the American market is a hero with strong leadership, it can be changed to a character that values ​​bonds with friends for the Japanese market. Furthermore, the Coordination Department can also adjust product manuals and e-learning materials according to local standards. For example, a product manual for the Japanese market would include information based on Japanese standards and laws, while a product manual for the American market would include information based on American standards and laws. This means that, for instance, a product manual for the Japanese market could state, "The power plug conforms to Japanese standards," while one for the American market could state, "The power plug conforms to American standards." This allows the adjustment department to provide content tailored to the culture, preferences, and standards of each market, resulting in content that is more user-friendly and easier to understand.

[0032] The integration unit seamlessly integrates with other tools. For example, by using translation memory, the integration unit can reuse previously translated content to produce consistent translations. Specifically, translation memory stores previously translated text and its corresponding translations in a database, and when creating new translations, it refers to past translations to maintain consistency. For example, in the translation of a product manual, if the same terminology or phrases are used repeatedly, translation memory can be used to reuse the same translation and provide a consistent translation. Furthermore, by using a global content management tool, the integration unit can centrally manage and efficiently distribute content for multiple languages ​​and markets. Specifically, the global content management tool centrally manages content for each market and distributes customized content for each market as needed. For example, advertisements and product information for each market can be distributed simultaneously to coincide with a product launch. In addition, the integration unit can use generative AI to automate the translation and adjustment processes and improve efficiency. Generative AI can learn from large amounts of data and automate the translation and adjustment processes. For example, generative AI can automatically translate input text and generate content optimized for each market. This enables the integration department to achieve efficient and high-quality localization and provide customized experiences tailored to each market.

[0033] The translation unit performs translations using natural language processing and cultural analysis. For example, it divides the input text into words using morphological analysis and understands the sentence structure through grammatical analysis. Furthermore, the translation unit performs semantic analysis to accurately grasp the meaning of the sentence. For example, when translating an English sentence into Japanese, the translation unit accurately analyzes the subject, predicate, and object of the sentence and converts them into appropriate Japanese sentences. In addition, the translation unit performs cultural analysis, taking into account the cultural background of the text being translated. For example, the translation unit can accurately translate idioms and expressions in a particular culture. As a result, by using natural language processing and cultural analysis, accurate and contextually appropriate translations are possible.

[0034] The adjustment unit can tailor advertising copy and design to the culture and preferences of each market. For example, the adjustment unit identifies the culture and preferences of each market based on regional survey data and user feedback, and adjusts the advertising copy and design accordingly. For instance, the adjustment unit might use casual and friendly language for advertisements targeting the American market, while using polite and courteous language for advertisements targeting the Japanese market. The adjustment unit can also adjust the colors and layout of the design to suit the preferences of each market. For example, the adjustment unit might use bright and colorful designs for advertisements targeting the American market, while using more subdued colors for advertisements targeting the Japanese market. By tailoring advertising copy and design to the culture and preferences of each market, more effective marketing becomes possible. Some or all of the above processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input regional survey data into AI, which can then analyze the culture and preferences of each market and adjust the advertising copy and design accordingly.

[0035] The adjustment unit can adjust the story, characters, and settings of games and media to match the culture and preferences of each market. For example, the adjustment unit identifies the culture and preferences of each market based on regional survey data and user feedback, and adjusts the content of games and media accordingly. For instance, the adjustment unit might develop a story emphasizing action and adventure for games aimed at the American market, while developing a story emphasizing emotions and relationships for games aimed at the Japanese market. The adjustment unit can also adjust the backgrounds and settings of characters to match the culture of each market. For example, the adjustment unit might feature characters with strong leadership qualities in games aimed at the American market, while featuring characters that emphasize cooperation in games aimed at the Japanese market. By adjusting the content of games and media to match the culture and preferences of each market, it is possible to provide content that is accepted by a wider range of users. Some or all of the above processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input regional survey data into AI, which can then analyze the culture and preferences of each market and adjust the content of games and media accordingly.

[0036] The integration unit can seamlessly integrate with other tools. For example, the integration unit can exchange data with other tools using API integration and integrate seamlessly. For example, the integration unit can reuse previously translated content using translation memory to produce consistent translations. The integration unit can also standardize data formats and ensure data compatibility across multiple tools. For example, the integration unit can centrally manage and efficiently distribute content for multiple languages ​​and markets using a global content management tool. This allows for the establishment of efficient work processes through seamless integration with other tools. Some or all of the above processes in the integration unit may be performed using AI, or not. For example, the integration unit can input API integration settings into AI, which can then automatically configure integration with other tools.

[0037] The adjustment unit can adapt product manuals and e-learning materials to local standards. For example, the adjustment unit investigates the laws, regulations, and industry standards of each market and adjusts the content of product manuals and e-learning materials based on them. For instance, the adjustment unit would include content based on Japanese standards and laws in a product manual for the Japanese market, while including content based on American standards and laws in a product manual for the American market. The adjustment unit can also adjust content according to user expectations. For example, the adjustment unit improves the content of product manuals and e-learning materials based on user feedback in a specific market. This allows for the provision of more appropriate information by adapting content to the standards of each market. Some or all of the above processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input data on the laws, regulations, and industry standards of each market into the AI, which can then automatically adjust the content.

[0038] The translation department can use dictionaries to prioritize the accurate translation of specific industry-specific or technical terms. For example, in content for the medical industry, the translation department can use a medical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a medical terminology dictionary to accurately translate specialized medical terms. Similarly, in content for the legal industry, the translation department can use a legal terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a legal terminology dictionary to accurately translate specialized legal terms. Furthermore, in content for the technology industry, the translation department can use a technical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a technical terminology dictionary to accurately translate specialized technical terms. This improves the accuracy of translations of specialized content by accurately translating specific industry-specific or technical terms. Some or all of the above processes in the translation department may be performed using AI, or not. For example, the translation department can input a specific industry-specific dictionary into a generating AI, which can then refer to the dictionary to perform accurate translations.

[0039] The translation unit can apply different translation algorithms depending on the context. For example, in business documents, the translation unit applies a formal translation algorithm. For instance, it analyzes the context of the business document and translates using appropriate formal expressions. The translation unit can also apply an informal translation algorithm to casual conversational texts. For example, it analyzes the context of the conversation and translates using appropriate informal expressions. Furthermore, the translation unit can apply a specialized translation algorithm to technical documents. For example, it analyzes the context of the technical document and translates using appropriate specialized expressions. This ensures that more accurate translations are provided by applying context-appropriate translation algorithms. Some or all of the above processes in the translation unit may be performed using AI, or not. For example, the translation unit can input contextual analysis data into a generating AI, which can then apply an appropriate translation algorithm based on the context.

[0040] The translation unit can use region-specific expressions by taking into account the user's geographical location. For example, the translation unit can identify the user's geographical location using a location acquisition method and select region-specific expressions based on that. For example, the translation unit will use American English expressions for users in the United States. It can also use British English expressions for users in the United Kingdom. Furthermore, it can use Australian English expressions for users in Australia. This ensures that more appropriate translations are provided by using region-specific expressions. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's geographical location information into a generating AI, which can then select region-specific expressions and perform the translation.

[0041] The translation unit can analyze a user's social media activity and prioritize translating relevant content. For example, the translation unit can analyze a user's posts and behavioral patterns using algorithms for analyzing social media activity. For instance, the translation unit can prioritize translating content related to hashtags frequently used by the user. It can also prioritize translating posts from influencers the user follows. Furthermore, the translation unit can prioritize translating content from groups and communities the user participates in. This ensures that more appropriate translations are provided by prioritizing the translation of relevant content based on the user's social media activity. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input the user's social media activity data into a generating AI, which can then identify and prioritize the translation of relevant content.

[0042] The adjustment unit can use data to reflect the latest trends and fashions in each market. For example, the adjustment unit can collect market data and user data and use it to identify the latest trends and fashions. For example, in the fashion industry, the adjustment unit can use designs that reflect the latest trends. In the technology industry, the adjustment unit can also use advertising copy that reflects the latest technologies. Furthermore, in the entertainment industry, the adjustment unit can use content that reflects the latest fashions. This enables more effective marketing by reflecting the latest trends and fashions in each market. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input market data into a generating AI, which can then identify the latest trends and fashions and adjust the content accordingly.

[0043] The adjustment unit can generate customized content for specific target audiences. For example, the adjustment unit can identify the attributes and needs of the target audience and customize the content accordingly. For instance, the adjustment unit might use casual and approachable language for younger audiences, polite and easy-to-understand language for older audiences, and professional and trustworthy language for business professionals. This allows for more effective marketing by generating customized content for specific target audiences. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input target audience data into a generating AI, which can then generate customized content.

[0044] The adjustment unit can reflect region-specific cultures and preferences by taking into account the user's geographical location information. For example, the adjustment unit can identify the user's geographical location information using a location information acquisition method and reflect region-specific cultures and preferences based on that information. For example, the adjustment unit can provide content that reflects American cultures and preferences to users in the United States. It can also provide content that reflects Japanese cultures and preferences to users in Japan. Furthermore, it can provide content that reflects French cultures and preferences to users in France. By reflecting region-specific cultures and preferences, more appropriate content can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI, and the generating AI can generate content that reflects region-specific cultures and preferences.

[0045] The adjustment unit can analyze a user's social media activity and prioritize relevant content. For example, the adjustment unit can analyze a user's posts and behavioral patterns using algorithms for analyzing social media activity. For instance, the adjustment unit can prioritize content related to hashtags frequently used by the user. It can also prioritize posts from influencers the user follows. Furthermore, the adjustment unit can prioritize content from groups and communities the user participates in. This ensures that more relevant content is provided by prioritizing content based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI, which can then identify and prioritize relevant content.

[0046] The integration unit can apply the optimal integration algorithm by referring to past integration data. For example, the integration unit can collect past integration data and select the optimal integration algorithm based on it. For example, the integration unit can analyze past integration data and apply the most effective algorithm. The integration unit can also apply an algorithm with fewer errors based on past integration data. Furthermore, the integration unit can apply an algorithm that delivers optimal performance by referring to past integration data. In this way, the optimal integration algorithm can be applied by referring to past integration data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI, which can then select and apply the optimal integration algorithm.

[0047] The integration unit can perform integration in accordance with specific industry standards and specifications. For example, the integration unit can perform integration in accordance with standards in the medical industry. For example, the integration unit can perform data integration based on standards in the medical industry. The integration unit can also perform integration in accordance with standards in the legal industry. For example, the integration unit can perform data integration based on standards in the legal industry. Furthermore, the integration unit can also perform integration in accordance with standards in the technology industry. For example, the integration unit can perform data integration based on standards in the technology industry. This enables more reliable integration by adhering to specific industry standards and specifications. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input data of specific industry standards and specifications into a generating AI, and the generating AI can perform integration in accordance with the standards.

[0048] The integration unit can prioritize the integration of region-specific tools, taking into account the user's geographical location information. For example, the integration unit can identify the user's geographical location using a location acquisition method and select region-specific tools based on that. For example, for a user in the United States, the integration unit will prioritize the integration of American tools. Similarly, for a user in Japan, the integration unit can prioritize the integration of Japanese tools. Furthermore, for a user in France, the integration unit can prioritize the integration of French tools. This allows for more appropriate integration by prioritizing the integration of region-specific tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI, which can then select and integrate region-specific tools.

[0049] The integration unit can analyze a user's social media activity and prioritize the integration of relevant tools. For example, the integration unit can analyze a user's posts and behavioral patterns using algorithms for analyzing social media activity. For example, the integration unit can prioritize the integration of tools that the user frequently uses. It can also prioritize the integration of tools recommended by influencers that the user follows. Furthermore, the integration unit can prioritize the integration of tools used in groups and communities that the user participates in. This allows for more appropriate integration by prioritizing the integration of relevant tools based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the user's social media activity data into a generating AI, which can then identify relevant tools and prioritize their integration.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The translation department can use dictionaries to prioritize the accurate translation of specific industry-specific or technical terms. For example, in content for the medical industry, the translation department can use a medical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a medical terminology dictionary to accurately translate specialized medical terms. Similarly, in content for the legal industry, the translation department can use a legal terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a legal terminology dictionary to accurately translate specialized legal terms. Furthermore, in content for the technology industry, the translation department can use a technical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a technical terminology dictionary to accurately translate specialized technical terms. This improves the accuracy of translations of specialized content by accurately translating specific industry-specific or technical terms. Some or all of the above processes in the translation department may be performed using AI, or not. For example, the translation department can input a specific industry-specific dictionary into a generating AI, which can then refer to the dictionary to perform accurate translations.

[0052] The translation unit can apply different translation algorithms depending on the context. For example, in business documents, the translation unit applies a formal translation algorithm. For instance, it analyzes the context of the business document and translates using appropriate formal expressions. The translation unit can also apply an informal translation algorithm to casual conversational texts. For example, it analyzes the context of the conversation and translates using appropriate informal expressions. Furthermore, the translation unit can apply a specialized translation algorithm to technical documents. For example, it analyzes the context of the technical document and translates using appropriate specialized expressions. This ensures that more accurate translations are provided by applying context-appropriate translation algorithms. Some or all of the above processes in the translation unit may be performed using AI, or not. For example, the translation unit can input contextual analysis data into a generating AI, which can then apply an appropriate translation algorithm based on the context.

[0053] The adjustment unit can use data to reflect the latest trends and fashions in each market. For example, the adjustment unit can collect market data and user data and use it to identify the latest trends and fashions. For example, in the fashion industry, the adjustment unit can use designs that reflect the latest trends. In the technology industry, the adjustment unit can also use advertising copy that reflects the latest technologies. Furthermore, in the entertainment industry, the adjustment unit can use content that reflects the latest fashions. This enables more effective marketing by reflecting the latest trends and fashions in each market. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input market data into a generating AI, which can then identify the latest trends and fashions and adjust the content accordingly.

[0054] The adjustment unit can generate customized content for specific target audiences. For example, the adjustment unit can identify the attributes and needs of the target audience and customize the content accordingly. For instance, the adjustment unit might use casual and approachable language for younger audiences, polite and easy-to-understand language for older audiences, and professional and trustworthy language for business professionals. This allows for more effective marketing by generating customized content for specific target audiences. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input target audience data into a generating AI, which can then generate customized content.

[0055] The integration unit can apply the optimal integration algorithm by referring to past integration data. For example, the integration unit can collect past integration data and select the optimal integration algorithm based on it. For example, the integration unit can analyze past integration data and apply the most effective algorithm. The integration unit can also apply an algorithm with fewer errors based on past integration data. Furthermore, the integration unit can apply an algorithm that delivers optimal performance by referring to past integration data. In this way, the optimal integration algorithm can be applied by referring to past integration data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI, which can then select and apply the optimal integration algorithm.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The translation unit automatically translates the language and optimizes the content considering the cultural context. For example, it uses natural language processing technology to analyze the input text and generate an appropriate translation. Furthermore, it can use cultural analysis technology to consider the cultural background of the target text and select appropriate expressions. Step 2: The adjustment department adapts the content optimized by the translation department to the specific culture and preferences of each region. For example, they adjust advertising copy and design to suit the culture and preferences of each market. They can also adapt the story, characters, and settings of games and media to suit each market. Furthermore, they can adapt product manuals and e-learning materials to meet local standards. Step 3: The integration unit seamlessly integrates with other tools. For example, translation memory can be used to reuse previously translated content and ensure consistent translations. Additionally, a global content management tool can be used to centrally manage and efficiently distribute content for multiple languages ​​and markets.

[0058] (Example of form 2) The content localization system according to an embodiment of the present invention is a system that uses AI to streamline multilingual and multicultural content localization and automates content adjustments for each country's market. First, the AI ​​automatically translates the language and optimizes the content considering the cultural background. Next, it adjusts advertising copy and design to suit the specific culture and preferences of each region. Furthermore, it adjusts the story, characters, and settings of games and media to suit each market. It also establishes an efficient work process by seamlessly integrating with other tools. Finally, it adjusts product manuals and e-learning materials according to local standards. This mechanism enables efficient and high-quality localization, strengthens competitiveness with customized experiences tailored to each market, and achieves multilingual support while maintaining identity. For example, the AI ​​automatically translates the language and optimizes the content considering the cultural background. In this process, the AI ​​uses natural language processing and cultural analysis to accurately understand the language and culture and perform appropriate translations. For example, in English-to-Japanese translation, it can not only replace words but also change expressions to suit Japanese culture and customs. This improves translation quality and avoids cultural misunderstandings. Next, advertising copy and design are adjusted to suit the specific culture and preferences of each region. For example, advertisements for the American market use casual and approachable language, while advertisements for the Japanese market use polite and courteous language. By adjusting content to the culture and preferences of each market in this way, more effective marketing becomes possible. Furthermore, the story, characters, and settings of games and media are adjusted to suit each market. For example, games for the American market feature storylines that emphasize action and adventure, while games for the Japanese market feature storylines that emphasize emotions and relationships. By adjusting the content of games and media to suit the culture and preferences of each market in this way, content that is accepted by a wider range of users can be provided. In addition, an efficient work process is established by seamlessly integrating with other tools. For example, by using translation memory, previously translated content can be reused to produce consistent translations.Furthermore, using a global content management tool allows for centralized management and efficient distribution of content for multiple languages ​​and markets. Finally, product manuals and e-learning materials are adapted to local standards. For example, a product manual for the Japanese market would include content based on Japanese standards and laws, while a product manual for the American market would include content based on American standards and laws. By adapting content to the standards of each market in this way, more relevant information can be provided. This enables the content localization system to perform efficient and high-quality localization, strengthen competitiveness with customized experiences tailored to each market, and achieve multilingual support while maintaining identity. As a result, the content localization system can efficiently provide multilingual content when companies aiming for global expansion need to create culturally sensitive content for each market. It also allows developers and marketers to collaborate to create content that considers both technical and cultural aspects. This can be expected to lead to business expansion in the global market.

[0059] The content localization system according to this embodiment comprises a translation unit, an adjustment unit, and an integration unit. The translation unit automatically translates languages ​​and optimizes content considering cultural background. The translation unit analyzes input text using, for example, natural language processing technology and generates appropriate translations. For example, in English-to-Japanese translation, the translation unit can not only replace words but also change expressions to suit Japanese culture and customs. The translation unit can also use cultural analysis technology to consider the cultural background of the target text and select appropriate expressions. For example, the translation unit can accurately translate idioms and expressions in a particular culture. The adjustment unit adjusts the content optimized by the translation unit to suit the specific culture and preferences of each region. For example, the adjustment unit adjusts advertising copy and design to suit the culture and preferences of each market. For example, the adjustment unit uses casual and friendly language in advertisements for the American market, while using polite and courteous language in advertisements for the Japanese market. The adjustment unit can also adjust the story, characters, and settings of games and media to suit each market. For example, the adjustment unit can develop storylines that emphasize action and adventure for games intended for the American market, while developing storylines that emphasize emotions and relationships for games intended for the Japanese market. Furthermore, the adjustment unit can also adjust product manuals and e-learning materials according to local standards. For example, the adjustment unit can include content based on Japanese standards and laws in product manuals for the Japanese market, while including content based on American standards and laws in product manuals for the American market. The integration unit seamlessly integrates with other tools. For example, the integration unit can reuse previously translated content using translation memory to ensure consistent translations. In addition, the integration unit can centrally manage and efficiently distribute content for multiple languages ​​and markets using a global content management tool. As a result, the content localization system according to this embodiment enables efficient and high-quality localization, strengthens competitiveness with customized experiences tailored to each market, and achieves multilingual support while maintaining identity.

[0060] The translation department automatically translates languages ​​and optimizes content by considering cultural context. For example, it uses natural language processing (NLP) technology to analyze input text and generate appropriate translations. Specifically, NLP technology includes morphological analysis, grammatical analysis, and semantic analysis, which are combined to understand the structure and meaning of the text. For example, in English-to-Japanese translation, it can not only replace words but also change expressions to suit Japanese culture and customs. For instance, when translating "How are you?" from English to Japanese, it can change it from "How are you?" to more natural expressions such as "How have you been lately?" or "How are things going?" depending on the situation. Furthermore, the translation department can use cultural analysis technology to consider the cultural background of the target text and select appropriate expressions. For example, it can accurately translate idioms and expressions in a specific culture. For instance, when translating "break the ice" from English to Japanese, it can change it from a direct translation of "break the ice" to a more appropriate expression such as "to lighten the mood." In addition, the translation department can generate more natural and fluent translations using generative AI. Generative AI has the ability to learn from large amounts of text data and generate appropriate translations that are relevant to the context. For example, it can learn from texts of various genres, such as news articles, technical documents, and literary works, and generate appropriate translations that are relevant to each context. This allows the translation department to provide high-quality translations that take cultural background and context into consideration, rather than simply mechanical translations.

[0061] The Coordination Department adjusts content optimized by the Translation Department to suit the specific culture and preferences of each region. For example, the Coordination Department adjusts advertising copy and design to match the culture and preferences of each market. Specifically, advertisements for the American market use casual and friendly language, while advertisements for the Japanese market use polite and courteous language. For example, if the advertising copy for the American market is "Get the best deal now!", it can be changed to "Get the best deals now!" for the Japanese market. The Coordination Department can also adjust the story, characters, and settings of games and media to suit each market. For example, a game for the American market may feature a story that emphasizes action and adventure, while a game for the Japanese market may feature a story that emphasizes emotions and relationships. For example, if a game character for the American market is a hero with strong leadership, it can be changed to a character that values ​​bonds with friends for the Japanese market. Furthermore, the Coordination Department can also adjust product manuals and e-learning materials according to local standards. For example, a product manual for the Japanese market would include information based on Japanese standards and laws, while a product manual for the American market would include information based on American standards and laws. This means that, for instance, a product manual for the Japanese market could state, "The power plug conforms to Japanese standards," while one for the American market could state, "The power plug conforms to American standards." This allows the adjustment department to provide content tailored to the culture, preferences, and standards of each market, resulting in content that is more user-friendly and easier to understand.

[0062] The integration unit seamlessly integrates with other tools. For example, by using translation memory, the integration unit can reuse previously translated content to produce consistent translations. Specifically, translation memory stores previously translated text and its corresponding translations in a database, and when creating new translations, it refers to past translations to maintain consistency. For example, in the translation of a product manual, if the same terminology or phrases are used repeatedly, translation memory can be used to reuse the same translation and provide a consistent translation. Furthermore, by using a global content management tool, the integration unit can centrally manage and efficiently distribute content for multiple languages ​​and markets. Specifically, the global content management tool centrally manages content for each market and distributes customized content for each market as needed. For example, advertisements and product information for each market can be distributed simultaneously to coincide with a product launch. In addition, the integration unit can use generative AI to automate the translation and adjustment processes and improve efficiency. Generative AI can learn from large amounts of data and automate the translation and adjustment processes. For example, generative AI can automatically translate input text and generate content optimized for each market. This enables the integration department to achieve efficient and high-quality localization and provide customized experiences tailored to each market.

[0063] The translation unit performs translations using natural language processing and cultural analysis. For example, it divides the input text into words using morphological analysis and understands the sentence structure through grammatical analysis. Furthermore, the translation unit performs semantic analysis to accurately grasp the meaning of the sentence. For example, when translating an English sentence into Japanese, the translation unit accurately analyzes the subject, predicate, and object of the sentence and converts them into appropriate Japanese sentences. In addition, the translation unit performs cultural analysis, taking into account the cultural background of the text being translated. For example, the translation unit can accurately translate idioms and expressions in a particular culture. As a result, by using natural language processing and cultural analysis, accurate and contextually appropriate translations are possible.

[0064] The adjustment unit can tailor advertising copy and design to the culture and preferences of each market. For example, the adjustment unit identifies the culture and preferences of each market based on regional survey data and user feedback, and adjusts the advertising copy and design accordingly. For instance, the adjustment unit might use casual and friendly language for advertisements targeting the American market, while using polite and courteous language for advertisements targeting the Japanese market. The adjustment unit can also adjust the colors and layout of the design to suit the preferences of each market. For example, the adjustment unit might use bright and colorful designs for advertisements targeting the American market, while using more subdued colors for advertisements targeting the Japanese market. By tailoring advertising copy and design to the culture and preferences of each market, more effective marketing becomes possible. Some or all of the above processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input regional survey data into AI, which can then analyze the culture and preferences of each market and adjust the advertising copy and design accordingly.

[0065] The adjustment unit can adjust the story, characters, and settings of games and media to match the culture and preferences of each market. For example, the adjustment unit identifies the culture and preferences of each market based on regional survey data and user feedback, and adjusts the content of games and media accordingly. For instance, the adjustment unit might develop a story emphasizing action and adventure for games aimed at the American market, while developing a story emphasizing emotions and relationships for games aimed at the Japanese market. The adjustment unit can also adjust the backgrounds and settings of characters to match the culture of each market. For example, the adjustment unit might feature characters with strong leadership qualities in games aimed at the American market, while featuring characters that emphasize cooperation in games aimed at the Japanese market. By adjusting the content of games and media to match the culture and preferences of each market, it is possible to provide content that is accepted by a wider range of users. Some or all of the above processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input regional survey data into AI, which can then analyze the culture and preferences of each market and adjust the content of games and media accordingly.

[0066] The integration unit can seamlessly integrate with other tools. For example, the integration unit can exchange data with other tools using API integration and integrate seamlessly. For example, the integration unit can reuse previously translated content using translation memory to produce consistent translations. The integration unit can also standardize data formats and ensure data compatibility across multiple tools. For example, the integration unit can centrally manage and efficiently distribute content for multiple languages ​​and markets using a global content management tool. This allows for the establishment of efficient work processes through seamless integration with other tools. Some or all of the above processes in the integration unit may be performed using AI, or not. For example, the integration unit can input API integration settings into AI, which can then automatically configure integration with other tools.

[0067] The adjustment unit can adapt product manuals and e-learning materials to local standards. For example, the adjustment unit investigates the laws, regulations, and industry standards of each market and adjusts the content of product manuals and e-learning materials based on them. For instance, the adjustment unit would include content based on Japanese standards and laws in a product manual for the Japanese market, while including content based on American standards and laws in a product manual for the American market. The adjustment unit can also adjust content according to user expectations. For example, the adjustment unit improves the content of product manuals and e-learning materials based on user feedback in a specific market. This allows for the provision of more appropriate information by adapting content to the standards of each market. Some or all of the above processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input data on the laws, regulations, and industry standards of each market into the AI, which can then automatically adjust the content.

[0068] The translation unit can estimate the user's emotions and adjust the translation's expression based on those estimated emotions. For example, the translation unit might use an emotion analysis algorithm to estimate the user's emotions. For instance, it might analyze the user's text or voice input to identify the intensity and type of emotion. The translation unit can also estimate emotions based on user feedback. For example, it might analyze user-provided feedback data to understand emotional tendencies. This allows the translation unit to adjust the translation's expression according to the user's emotions. For example, if the user is stressed, the translation unit might use simple and intuitive language. If the user is relaxed, the translation unit might use detailed and rich language. Furthermore, if the user is in a hurry, the translation unit might use concise and to-the-point language. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input user text into a generating AI, which can then estimate the sentiment and adjust the translation's expression.

[0069] The translation department can use dictionaries to prioritize the accurate translation of specific industry-specific or technical terms. For example, in content for the medical industry, the translation department can use a medical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a medical terminology dictionary to accurately translate specialized medical terms. Similarly, in content for the legal industry, the translation department can use a legal terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a legal terminology dictionary to accurately translate specialized legal terms. Furthermore, in content for the technology industry, the translation department can use a technical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a technical terminology dictionary to accurately translate specialized technical terms. This improves the accuracy of translations of specialized content by accurately translating specific industry-specific or technical terms. Some or all of the above processes in the translation department may be performed using AI, or not. For example, the translation department can input a specific industry-specific dictionary into a generating AI, which can then refer to the dictionary to perform accurate translations.

[0070] The translation unit can apply different translation algorithms depending on the context. For example, in business documents, the translation unit applies a formal translation algorithm. For instance, it analyzes the context of the business document and translates using appropriate formal expressions. The translation unit can also apply an informal translation algorithm to casual conversational texts. For example, it analyzes the context of the conversation and translates using appropriate informal expressions. Furthermore, the translation unit can apply a specialized translation algorithm to technical documents. For example, it analyzes the context of the technical document and translates using appropriate specialized expressions. This ensures that more accurate translations are provided by applying context-appropriate translation algorithms. Some or all of the above processes in the translation unit may be performed using AI, or not. For example, the translation unit can input contextual analysis data into a generating AI, which can then apply an appropriate translation algorithm based on the context.

[0071] The translation unit can estimate the user's emotions and determine translation priorities based on those estimated emotions. For example, the translation unit might use an emotion analysis algorithm to estimate the user's emotions. For instance, it might analyze the user's text or voice input to identify the intensity and type of emotion. The translation unit can also estimate emotions based on user feedback. For example, it might analyze user-provided feedback data to understand emotional tendencies. This allows the translation unit to determine translation priorities according to the user's emotions. For example, if the user is in a hurry, the translation unit will prioritize translating important information. If the user is relaxed, the translation unit can consider the overall context. Furthermore, if the user is stressed, the translation unit can prioritize translating concise and to-the-point information. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input user text into a generating AI, which can then estimate sentiment and determine translation priorities.

[0072] The translation unit can use region-specific expressions by taking into account the user's geographical location. For example, the translation unit can identify the user's geographical location using a location acquisition method and select region-specific expressions based on that. For example, the translation unit will use American English expressions for users in the United States. It can also use British English expressions for users in the United Kingdom. Furthermore, it can use Australian English expressions for users in Australia. This ensures that more appropriate translations are provided by using region-specific expressions. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's geographical location information into a generating AI, which can then select region-specific expressions and perform the translation.

[0073] The translation unit can analyze a user's social media activity and prioritize translating relevant content. For example, the translation unit can analyze a user's posts and behavioral patterns using algorithms for analyzing social media activity. For instance, the translation unit can prioritize translating content related to hashtags frequently used by the user. It can also prioritize translating posts from influencers the user follows. Furthermore, the translation unit can prioritize translating content from groups and communities the user participates in. This ensures that more appropriate translations are provided by prioritizing the translation of relevant content based on the user's social media activity. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input the user's social media activity data into a generating AI, which can then identify and prioritize the translation of relevant content.

[0074] The adjustment unit can estimate the user's emotions and adjust the expression of advertising copy and design based on the estimated emotions. For example, the adjustment unit estimates the user's emotions using an emotion analysis algorithm. For instance, it analyzes the user's text or voice input to identify the intensity and type of emotion. The adjustment unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the adjustment unit to adjust the expression of advertising copy and design according to the user's emotions. For example, if the user is relaxed, the adjustment unit uses soft colors and friendly language. If the user is excited, it uses vibrant colors and energetic language. Furthermore, if the user is stressed, it uses calm colors and simple language. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user text input into a generating AI, which can then estimate emotions and adjust the expression of the advertising copy and design.

[0075] The adjustment unit can use data to reflect the latest trends and fashions in each market. For example, the adjustment unit can collect market data and user data and use it to identify the latest trends and fashions. For example, in the fashion industry, the adjustment unit can use designs that reflect the latest trends. In the technology industry, the adjustment unit can also use advertising copy that reflects the latest technologies. Furthermore, in the entertainment industry, the adjustment unit can use content that reflects the latest fashions. This enables more effective marketing by reflecting the latest trends and fashions in each market. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input market data into a generating AI, which can then identify the latest trends and fashions and adjust the content accordingly.

[0076] The adjustment unit can generate customized content for specific target audiences. For example, the adjustment unit can identify the attributes and needs of the target audience and customize the content accordingly. For instance, the adjustment unit might use casual and approachable language for younger audiences, polite and easy-to-understand language for older audiences, and professional and trustworthy language for business professionals. This allows for more effective marketing by generating customized content for specific target audiences. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input target audience data into a generating AI, which can then generate customized content.

[0077] The adjustment unit can estimate the user's emotions and determine the priority of content to adjust based on the estimated emotions. The adjustment unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, it analyzes the user's text or voice input to identify the intensity and type of emotion. The adjustment unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the adjustment unit to determine the priority of content to adjust according to the user's emotions. For example, if the user is in a hurry, the adjustment unit prioritizes important information. If the user is relaxed, the adjustment unit can adjust considering the overall context. Furthermore, if the user is stressed, the adjustment unit can prioritize concise and to-the-point information. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user text input to a generating AI, which can then estimate emotions and determine the priority of content to adjust.

[0078] The adjustment unit can reflect region-specific cultures and preferences by taking into account the user's geographical location information. For example, the adjustment unit can identify the user's geographical location information using a location information acquisition method and reflect region-specific cultures and preferences based on that information. For example, the adjustment unit can provide content that reflects American cultures and preferences to users in the United States. It can also provide content that reflects Japanese cultures and preferences to users in Japan. Furthermore, it can provide content that reflects French cultures and preferences to users in France. By reflecting region-specific cultures and preferences, more appropriate content can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI, and the generating AI can generate content that reflects region-specific cultures and preferences.

[0079] The adjustment unit can analyze a user's social media activity and prioritize relevant content. For example, the adjustment unit can analyze a user's posts and behavioral patterns using algorithms for analyzing social media activity. For instance, the adjustment unit can prioritize content related to hashtags frequently used by the user. It can also prioritize posts from influencers the user follows. Furthermore, the adjustment unit can prioritize content from groups and communities the user participates in. This ensures that more relevant content is provided by prioritizing content based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI, which can then identify and prioritize relevant content.

[0080] The integration unit can estimate the user's emotions and select tools for integration based on the estimated emotions. For example, the integration unit estimates the user's emotions using an emotion analysis algorithm. For example, it analyzes the user's text or voice input to identify the intensity and type of emotion. The integration unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the integration unit to select appropriate tools according to the user's emotions. For example, if the user is relaxed, the integration unit selects an easy-to-use tool. If the user is in a hurry, the integration unit can select a tool that can be operated quickly. Furthermore, if the user is stressed, the integration unit can select a simple and intuitive tool. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI, or not. For example, the integration unit can input user text into a generative AI, which can then estimate emotions and select the appropriate tool.

[0081] The integration unit can apply the optimal integration algorithm by referring to past integration data. For example, the integration unit can collect past integration data and select the optimal integration algorithm based on it. For example, the integration unit can analyze past integration data and apply the most effective algorithm. The integration unit can also apply an algorithm with fewer errors based on past integration data. Furthermore, the integration unit can apply an algorithm that delivers optimal performance by referring to past integration data. In this way, the optimal integration algorithm can be applied by referring to past integration data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI, which can then select and apply the optimal integration algorithm.

[0082] The integration unit can perform integration in accordance with specific industry standards and specifications. For example, the integration unit can perform integration in accordance with standards in the medical industry. For example, the integration unit can perform data integration based on standards in the medical industry. The integration unit can also perform integration in accordance with standards in the legal industry. For example, the integration unit can perform data integration based on standards in the legal industry. Furthermore, the integration unit can also perform integration in accordance with standards in the technology industry. For example, the integration unit can perform data integration based on standards in the technology industry. This enables more reliable integration by adhering to specific industry standards and specifications. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input data of specific industry standards and specifications into a generating AI, and the generating AI can perform integration in accordance with the standards.

[0083] The integration unit can estimate the user's emotions and determine integration priorities based on the estimated emotions. For example, the integration unit estimates the user's emotions using emotion analysis algorithms. For instance, it analyzes the user's text or voice input to identify the intensity and type of emotion. The integration unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the integration unit to determine integration priorities according to the user's emotions. For example, if the user is in a hurry, the integration unit prioritizes important integrations. If the user is relaxed, the integration unit can perform integrations considering the overall context. Furthermore, if the user is stressed, the integration unit can prioritize concise and to-the-point integrations. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user text input into a generating AI, which can then estimate emotions and determine the integration priority.

[0084] The integration unit can prioritize the integration of region-specific tools, taking into account the user's geographical location information. For example, the integration unit can identify the user's geographical location using a location acquisition method and select region-specific tools based on that. For example, for a user in the United States, the integration unit will prioritize the integration of American tools. Similarly, for a user in Japan, the integration unit can prioritize the integration of Japanese tools. Furthermore, for a user in France, the integration unit can prioritize the integration of French tools. This allows for more appropriate integration by prioritizing the integration of region-specific tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI, which can then select and integrate region-specific tools.

[0085] The integration unit can analyze a user's social media activity and prioritize the integration of relevant tools. For example, the integration unit can analyze a user's posts and behavioral patterns using algorithms for analyzing social media activity. For example, the integration unit can prioritize the integration of tools that the user frequently uses. It can also prioritize the integration of tools recommended by influencers that the user follows. Furthermore, the integration unit can prioritize the integration of tools used in groups and communities that the user participates in. This allows for more appropriate integration by prioritizing the integration of relevant tools based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the user's social media activity data into a generating AI, which can then identify relevant tools and prioritize their integration.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The translation unit can estimate the user's emotions and adjust the translation's expression based on those estimated emotions. For example, the translation unit might use an emotion analysis algorithm to estimate the user's emotions. For instance, it might analyze the user's text or voice input to identify the intensity and type of emotion. The translation unit can also estimate emotions based on user feedback. For example, it might analyze user-provided feedback data to understand emotional tendencies. This allows the translation unit to adjust the translation's expression according to the user's emotions. For example, if the user is stressed, the translation unit might use simple and intuitive language. If the user is relaxed, the translation unit might use detailed and rich language. Furthermore, if the user is in a hurry, the translation unit might use concise and to-the-point language. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input user text into a generating AI, which can then estimate the sentiment and adjust the translation's expression.

[0088] The adjustment unit can estimate the user's emotions and adjust the expression of advertising copy and design based on the estimated emotions. For example, the adjustment unit estimates the user's emotions using an emotion analysis algorithm. For instance, it analyzes the user's text or voice input to identify the intensity and type of emotion. The adjustment unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the adjustment unit to adjust the expression of advertising copy and design according to the user's emotions. For example, if the user is relaxed, the adjustment unit uses soft colors and friendly language. If the user is excited, it uses vibrant colors and energetic language. Furthermore, if the user is stressed, it uses calm colors and simple language. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user text input into a generating AI, which can then estimate emotions and adjust the expression of the advertising copy and design.

[0089] The integration unit can estimate the user's emotions and select tools for integration based on the estimated emotions. For example, the integration unit estimates the user's emotions using an emotion analysis algorithm. For example, it analyzes the user's text or voice input to identify the intensity and type of emotion. The integration unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the integration unit to select appropriate tools according to the user's emotions. For example, if the user is relaxed, the integration unit selects an easy-to-use tool. If the user is in a hurry, the integration unit can select a tool that can be operated quickly. Furthermore, if the user is stressed, the integration unit can select a simple and intuitive tool. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI, or not. For example, the integration unit can input user text into a generative AI, which can then estimate emotions and select the appropriate tool.

[0090] The adjustment unit can estimate the user's emotions and determine the priority of content to adjust based on the estimated emotions. The adjustment unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, it analyzes the user's text or voice input to identify the intensity and type of emotion. The adjustment unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the adjustment unit to determine the priority of content to adjust according to the user's emotions. For example, if the user is in a hurry, the adjustment unit prioritizes important information. If the user is relaxed, the adjustment unit can adjust considering the overall context. Furthermore, if the user is stressed, the adjustment unit can prioritize concise and to-the-point information. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user text input to a generating AI, which can then estimate emotions and determine the priority of content to adjust.

[0091] The integration unit can estimate the user's emotions and determine integration priorities based on the estimated emotions. For example, the integration unit estimates the user's emotions using emotion analysis algorithms. For instance, it analyzes the user's text or voice input to identify the intensity and type of emotion. The integration unit can also estimate emotions based on user feedback. For example, it analyzes user-provided feedback data to understand emotional tendencies. This allows the integration unit to determine integration priorities according to the user's emotions. For example, if the user is in a hurry, the integration unit prioritizes important integrations. If the user is relaxed, the integration unit can perform integrations considering the overall context. Furthermore, if the user is stressed, the integration unit can prioritize concise and to-the-point integrations. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user text input into a generating AI, which can then estimate emotions and determine the integration priority.

[0092] The translation department can use dictionaries to prioritize the accurate translation of specific industry-specific or technical terms. For example, in content for the medical industry, the translation department can use a medical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a medical terminology dictionary to accurately translate specialized medical terms. Similarly, in content for the legal industry, the translation department can use a legal terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a legal terminology dictionary to accurately translate specialized legal terms. Furthermore, in content for the technology industry, the translation department can use a technical terminology dictionary to ensure accurate translation. For instance, the translation department can refer to a technical terminology dictionary to accurately translate specialized technical terms. This improves the accuracy of translations of specialized content by accurately translating specific industry-specific or technical terms. Some or all of the above processes in the translation department may be performed using AI, or not. For example, the translation department can input a specific industry-specific dictionary into a generating AI, which can then refer to the dictionary to perform accurate translations.

[0093] The translation unit can apply different translation algorithms depending on the context. For example, in business documents, the translation unit applies a formal translation algorithm. For instance, it analyzes the context of the business document and translates using appropriate formal expressions. The translation unit can also apply an informal translation algorithm to casual conversational texts. For example, it analyzes the context of the conversation and translates using appropriate informal expressions. Furthermore, the translation unit can apply a specialized translation algorithm to technical documents. For example, it analyzes the context of the technical document and translates using appropriate specialized expressions. This ensures that more accurate translations are provided by applying context-appropriate translation algorithms. Some or all of the above processes in the translation unit may be performed using AI, or not. For example, the translation unit can input contextual analysis data into a generating AI, which can then apply an appropriate translation algorithm based on the context.

[0094] The adjustment unit can use data to reflect the latest trends and fashions in each market. For example, the adjustment unit can collect market data and user data and use it to identify the latest trends and fashions. For example, in the fashion industry, the adjustment unit can use designs that reflect the latest trends. In the technology industry, the adjustment unit can also use advertising copy that reflects the latest technologies. Furthermore, in the entertainment industry, the adjustment unit can use content that reflects the latest fashions. This enables more effective marketing by reflecting the latest trends and fashions in each market. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input market data into a generating AI, which can then identify the latest trends and fashions and adjust the content accordingly.

[0095] The adjustment unit can generate customized content for specific target audiences. For example, the adjustment unit can identify the attributes and needs of the target audience and customize the content accordingly. For instance, the adjustment unit might use casual and approachable language for younger audiences, polite and easy-to-understand language for older audiences, and professional and trustworthy language for business professionals. This allows for more effective marketing by generating customized content for specific target audiences. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not. For example, the adjustment unit can input target audience data into a generating AI, which can then generate customized content.

[0096] The integration unit can apply the optimal integration algorithm by referring to past integration data. For example, the integration unit can collect past integration data and select the optimal integration algorithm based on it. For example, the integration unit can analyze past integration data and apply the most effective algorithm. The integration unit can also apply an algorithm with fewer errors based on past integration data. Furthermore, the integration unit can apply an algorithm that delivers optimal performance by referring to past integration data. In this way, the optimal integration algorithm can be applied by referring to past integration data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI, which can then select and apply the optimal integration algorithm.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The translation unit automatically translates the language and optimizes the content considering the cultural context. For example, it uses natural language processing technology to analyze the input text and generate an appropriate translation. Furthermore, it can use cultural analysis technology to consider the cultural background of the target text and select appropriate expressions. Step 2: The adjustment department adapts the content optimized by the translation department to the specific culture and preferences of each region. For example, they adjust advertising copy and design to suit the culture and preferences of each market. They can also adapt the story, characters, and settings of games and media to suit each market. Furthermore, they can adapt product manuals and e-learning materials to meet local standards. Step 3: The integration unit seamlessly integrates with other tools. For example, translation memory can be used to reuse previously translated content and ensure consistent translations. Additionally, a global content management tool can be used to centrally manage and efficiently distribute content for multiple languages ​​and markets.

[0099] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] Each of the multiple elements described above, including the translation unit, adjustment unit, and integration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the translation unit is implemented by the control unit 46A of the smart device 14, which analyzes the input text using natural language processing technology and generates an appropriate translation. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the content to suit the specific culture and preferences of the region. The integration unit is implemented by the control unit 46A of the smart device 14, which establishes an efficient work process by seamlessly integrating with other tools. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the translation unit, adjustment unit, and integration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the translation unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the input text using natural language processing technology and generates an appropriate translation. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the content to suit the specific culture and preferences of the region. The integration unit is implemented by the control unit 46A of the smart glasses 214, which establishes an efficient work process by seamlessly integrating with other tools. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the translation unit, adjustment unit, and integration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the translation unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the input text using natural language processing technology and generates an appropriate translation. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the content to suit the specific culture and preferences of the region. The integration unit is implemented by the control unit 46A of the headset terminal 314, which establishes an efficient work process by seamlessly integrating with other tools. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the translation unit, adjustment unit, and integration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the translation unit is implemented by the control unit 46A of the robot 414, which analyzes the input text using natural language processing technology and generates an appropriate translation. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the content to suit the specific culture and preferences of the region. The integration unit is implemented by the control unit 46A of the robot 414, which establishes an efficient work process by seamlessly integrating with other tools. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A translation unit that automatically translates languages ​​and optimizes content while considering cultural background, The aforementioned translation unit adjusts the optimized content to suit the specific culture and preferences of the region, The adjustment unit adjusts the content adjusted by the aforementioned adjustment unit to suit each market, including the story, characters, and settings of games and media, and An integration unit that seamlessly integrates with other tools, It includes an adjustment unit that adjusts product manuals and e-learning materials according to local standards. A system characterized by the following features. (Note 2) The aforementioned translation department, Translate using natural language processing and cultural analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, Ad copy and design are adjusted to suit the culture and preferences of each market. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, The story, characters, and settings of games and media are adjusted to suit the culture and preferences of each market. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned integration unit is Seamless integration with other tools The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Product manuals and e-learning materials are adapted to local standards. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned translation department, Use dictionaries to prioritize accurate translation of specific industry-specific or technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned translation department, Apply different translation algorithms depending on the context. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned translation department, It estimates the user's emotions and determines translation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned translation department, Use region-specific terminology, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned translation department, Analyze users' social media activity and prioritize translating relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The adjustment unit is, We estimate the user's emotions and adjust the ad copy and design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, We use data that reflects the latest trends and fashions in each market. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, Generate customized content for a specific target audience. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, It estimates user sentiment and prioritizes content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, Reflecting region-specific culture and preferences by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, Analyze users' social media activity and prioritize relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integration unit is Select a tool to estimate user emotions and integrate them based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned integration unit is Referencing historical integration data, apply the optimal integration algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned integration unit is Perform integration in accordance with specific industry standards and specifications. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned integration unit is It estimates user sentiment and determines integration priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned integration unit is Prioritize integrating region-specific tools, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned integration unit is Analyze users' social media activity and prioritize the integration of relevant tools. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A translation unit that automatically translates languages ​​and optimizes content while considering cultural background, The aforementioned translation unit adjusts the optimized content to suit the specific culture and preferences of the region, The adjustment unit adjusts the content adjusted by the aforementioned adjustment unit to suit each market, including the story, characters, and settings of games and media, and An integration unit that seamlessly integrates with other tools, It includes an adjustment unit that adjusts product manuals and e-learning materials according to local standards. A system characterized by the following features.

2. The aforementioned translation department, Translate using natural language processing and cultural analysis. The system according to feature 1.

3. The adjustment unit is, Ad copy and design are adjusted to suit the culture and preferences of each market. The system according to feature 1.

4. The adjustment unit is, The story, characters, and settings of games and media are adjusted to suit the culture and preferences of each market. The system according to feature 1.

5. The aforementioned integration unit is Seamless integration with other tools The system according to feature 1.

6. The adjustment unit is, Product manuals and e-learning materials are adapted to local standards. The system according to feature 1.

7. The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system according to feature 1.

8. The aforementioned translation department, Use dictionaries to prioritize accurate translation of specific industry-specific or technical terms. The system according to feature 1.

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