system

Abstract

The system according to this embodiment aims to translate Japanese input into English and utilize the English-based information generated by the AI. [Solution] The system according to this embodiment comprises a reception unit, a translation unit, a transmission unit, a receiving unit, and a providing unit. The reception unit receives Japanese input from the user. The translation unit translates the Japanese received by the reception unit into English. The transmission unit sends the English question translated by the translation unit to the generating AI. The receiving unit receives the English answer from the generating AI. The translation unit translates the English answer received by the receiving unit back into Japanese. The providing unit provides the Japanese answer translated by the translation unit to the user.

Description

Technical Field

[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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, it is difficult to utilize English-based information of generative AI for Japanese input, and there may be a risk of being inferior in terms of the quality and quantity of information.

[0005] The system according to the embodiment aims to translate Japanese input into English and utilize English-based information of generative AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a translation unit, a transmission unit, a receiving unit, and a providing unit. The reception unit receives Japanese input from the user. The translation unit translates the Japanese input received by the reception unit into English. The transmission unit sends the English question translated by the translation unit to the generating AI. The receiving unit receives the English answer from the generating AI. The translation unit translates the English answer received by the receiving unit back into Japanese. The providing unit provides the Japanese answer translated by the translation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can translate Japanese input into English and utilize the English-based information generated by the AI. [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 numbered 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 applicable to the communication I / F include wireless communication standards including 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 three or more matters are expressed by connecting them with "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, 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 translation system according to an embodiment of the present invention is a system that automatically translates between Japanese and English using generative AI. When a user inputs in Japanese, this translation system automatically translates the Japanese input into English and asks the generative AI a question. The generative AI responds in English, and the system automatically translates that response back into Japanese and provides it to the user. This mechanism allows even users who only use the Japanese version to utilize the information available from the English-based generative AI. For example, if a user inputs the question "Please tell me about the latest technology trends" in Japanese, this question is automatically translated into English by the system. Next, the translated English question is sent to the generative AI, which generates an answer such as "The latest technology trends include AI, blockchain, and quantum computing." to the question "What are the latest technology trends?". The generated English answer is automatically translated back into Japanese by the system and provided to the user in the form of "The latest technology trends include AI, blockchain, and quantum computing." As a result, users can utilize the information from the English-based generative AI simply by inputting a question in Japanese. For example, by simply inputting "Please tell me about the latest technology trends" in Japanese, they can obtain a detailed answer based on English information. This mechanism allows users who only use the Japanese version to leverage the wealth of information available from the English-based AI-generated content. For example, they can easily obtain information in Japanese that is only available in English, such as technology trends and the latest research findings. This allows users to access more information and enhance their competitiveness. The translation system translates questions entered by the user in Japanese into English, and then translates the AI-generated English answers back into Japanese, enabling users to utilize English-based information.

[0029] The translation system according to this embodiment comprises a reception unit, a translation unit, a transmission unit, a receiving unit, and a providing unit. The reception unit receives Japanese input from the user. For example, the user inputs "Please tell me about the latest technology trends." The translation unit translates the Japanese input received by the reception unit into English. For example, the translation unit translates "Please tell me about the latest technology trends" into "What are the latest technology trends?". The transmission unit sends the English question translated by the translation unit to the generating AI. The generating AI receives the question in English and generates an answer to that question. For example, the generating AI generates an answer such as "The latest technology trends include AI, blockchain, and quantum computing.". The reception unit receives the English answer from the generating AI. The translation unit translates the English answer received by the reception unit into Japanese. For example, the translation unit translates "The latest technology trends include AI, blockchain, and quantum computing." into "The latest technology trends include AI, blockchain, and quantum computing.". The providing unit provides the Japanese answer translated by the translation unit to the user. For example, the provider might provide information to the user in the form of, "The latest technology trends include AI, blockchain, and quantum computing." This allows the translation system, according to the embodiment, to translate the user's Japanese input into English, and then translate the AI's generated English response back into Japanese, enabling the user to utilize English-based information.

[0030] The reception desk receives Japanese input from users. For example, a user might input "Please tell me about the latest technology trends." The reception desk accurately receives the Japanese text entered by the user and converts it into a format that can be processed within the system. Specifically, the reception desk analyzes the text entered through the user interface and checks for grammar and syntax. This allows for appropriate correction of typos, grammatical errors, and other mistakes before proceeding to the next step. Furthermore, the reception desk saves the user's input history and can more accurately understand the user's intent by referring to past inputs. For example, if a user has previously asked a question about "technology trends," the reception desk can refer to that history to recognize that the current input is similar and process it appropriately. The reception desk can also categorize user input and quickly process questions belonging to specific categories using predefined templates. This allows the reception desk to efficiently and accurately receive user input and smoothly process it for the next translation unit.

[0031] The translation department translates Japanese text received by the reception department into English. For example, the translation department translates "Please tell me about the latest technology trends" into "What are the latest technology trends?". The translation department uses natural language processing technology to accurately convert the input Japanese text into English. Specifically, the translation department performs morphological analysis to break down the Japanese sentence into words and phrases and analyze the meaning of each. Next, it selects the appropriate English expression while considering the context. For example, the phrase "latest technology trends" is translated as "latest technology trends," but in this case, it is necessary to accurately understand the relationship between "latest" and "technology trends." Furthermore, the translation department consults dictionaries and databases to provide appropriate translations for specialized and technical terms. This allows it to translate even questions containing technical content into accurate and natural English. In addition, the translation department has a feedback function to evaluate the quality of the translation results, and can continuously improve the translation algorithm based on user feedback. This allows the translation department to always provide high-quality translation results and meet the needs of users.

[0032] The transmitting unit sends the English question, translated by the translation unit, to the generating AI. The generating AI receives the question in English and generates an answer to it. For example, the generating AI might generate an answer such as "The latest technology trends include AI, blockchain, and quantum computing." The transmitting unit is responsible for converting the English question received from the translation unit into an appropriate format and sending it to the generating AI. Specifically, the transmitting unit formats the data into a format that the generating AI can easily understand and adds necessary metadata. For example, by adding the type and priority of the question and relevant contextual information, the transmitting unit enables the generating AI to generate a more appropriate answer. The transmitting unit also encrypts the data and performs error checking to ensure the reliability of the communication. This ensures that the data being transmitted is not tampered with and reaches the generating AI accurately. Furthermore, the transmitting unit monitors the response time from the generating AI and performs retransmission or error handling as needed. This allows the transmitting unit to communicate smoothly with the generating AI and obtain quick and accurate answers.

[0033] The receiving unit receives English responses from the generating AI. The receiving unit is responsible for receiving the response data sent from the generating AI, converting it into an appropriate format, and passing it on to the next process. Specifically, the receiving unit analyzes the data received from the generating AI and extracts the content of the response. For example, if it receives the response "The latest technology trends include AI, blockchain, and quantum computing," it accurately analyzes its content and passes it on to the translation unit. The receiving unit also checks the integrity of the received data and requests the data again if an error occurs. This allows the receiving unit to provide accurate and reliable data to the next process. Furthermore, the receiving unit can temporarily store the received data and refer to past response data as needed. This allows the receiving unit to efficiently receive responses from the generating AI and smoothly process them on to the translation unit.

[0034] The service provider will provide users with Japanese answers translated by the translation service provider. For example, the service provider might provide answers such as, "The latest technology trends include AI, blockchain, and quantum computing." The service provider is responsible for presenting answers to users in an appropriate format. Specifically, the service provider will display answers through the user interface to ensure easy understanding. For example, it can provide answers not only in text format but also in voice using speech synthesis technology. The service provider will also collect user feedback to improve the quality and delivery method of the answers. For example, it will provide a function that allows users to rate their satisfaction with the answers, and use the evaluation results to improve the entire system. Furthermore, the service provider will support multiple devices and platforms, allowing users to receive answers from any device. This enables the service provider to provide users with quick and appropriate answers, improving user convenience.

[0035] The translation unit includes a dictionary unit that utilizes a specialized terminology dictionary. The translation unit uses the specialized terminology dictionary to improve the accuracy of translations. For example, the specialized terminology dictionary includes technical terms and industry-specific terms, and using it improves the accuracy of translations. The dictionary unit, for example, regularly updates the latest technical terms so that the translation unit can always perform translations based on the most up-to-date information. The dictionary unit can also add or correct specialized terms based on user feedback. As a result, the translation unit improves the accuracy of translations by utilizing the specialized terminology dictionary. Some or all of the above processing in the dictionary unit may be performed using AI, for example, or not. For example, the dictionary unit can have AI perform the addition or correction of specialized terms.

[0036] The translation unit includes an evaluation unit that assesses the quality of the translation. The evaluation unit has criteria for evaluating the accuracy and fluency of the translation, and evaluates the quality of the translation based on these criteria. For example, the evaluation unit evaluates the grammatical accuracy and naturalness of the translated text. The evaluation unit can also evaluate the quality of the translation based on user feedback. As a result, the translation unit improves the accuracy of the translation by evaluating the quality of the translation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can have AI perform the translation quality evaluation.

[0037] The system includes an optimization unit to improve system performance. The optimization unit has algorithms for optimizing the system's processing speed and resource usage, and improves system performance based on these algorithms. For example, the optimization unit performs data caching and parallel processing to improve the system's processing speed. The optimization unit can also stop unnecessary processes or adjust resource allocation to optimize resource usage. This improves system performance. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can have AI execute algorithms for improving system performance.

[0038] The transmitting unit can send an English question to the generating AI. For example, the transmitting unit sends an English question translated by the translation unit to the generating AI. This allows the generating AI to receive the question in English and generate an answer to it. Some or all of the above processing in the transmitting unit may be performed using an AI, for example, or not using an AI. For example, the transmitting unit can have an AI perform the process of sending an English question to the generating AI.

[0039] The receiving unit can receive English responses from the generating AI. The receiving unit receives English responses from the generating AI. For example, the receiving unit receives an English response generated by the generating AI. The receiving unit then passes the English response from the generating AI to the translation unit, which can translate the response into Japanese. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can have the AI ​​perform the process of receiving English responses from the generating AI.

[0040] The reception desk can analyze the user's past input history and suggest the optimal Japanese input method. For example, the reception desk can automatically display as suggestions questions that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions that the user will use at specific times of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can have AI perform the process of analyzing the user's past input history.

[0041] The input system can present input suggestions based on the user's current context when they are typing in Japanese. For example, if the user is typing at night, the input system will present question suggestions related to nighttime. It can also present question suggestions related to the user's location if the user is in a specific location. Furthermore, if the user is typing during a specific event, the input system can present question suggestions related to that event. This allows the system to assist the user's input by presenting input suggestions based on the current context. Some or all of the above processing in the input system may be performed using AI, for example, or without AI. For example, the input system could have AI perform the process of analyzing the user's current context and presenting input suggestions.

[0042] The translation unit can adjust the level of detail in the translation based on the frequency and importance of technical terms. For example, if there are many technical terms, the unit will provide a detailed translation. Conversely, if there are few technical terms, the unit can provide a concise translation. Furthermore, the unit can add annotations to highly important technical terms. This allows for the provision of more appropriate translations by adjusting the level of detail based on the frequency and importance of technical terms. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can have AI perform the process of evaluating the frequency and importance of technical terms.

[0043] The translation unit can apply different translation algorithms depending on the context during translation. For example, in a technical context, the unit may apply a translation algorithm specialized in technical terms. In the context of everyday conversation, the unit may also apply a translation algorithm that prioritizes natural expression. Furthermore, in the context of legal documents, the unit may apply a translation algorithm that prioritizes accuracy. By applying different translation algorithms depending on the context, a more appropriate translation can be provided. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can have AI perform the process of applying different translation algorithms depending on the context.

[0044] The sending unit can determine the priority of sending questions based on their importance at the time of transmission. For example, the sending unit will send important questions first. It can also send general questions with normal priority. Furthermore, it can postpone less urgent questions. This allows important questions to be sent first by determining the priority of sending based on the importance of the questions. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can have AI perform the process of evaluating the importance of questions and determining the priority of sending.

[0045] The transmitting unit can apply different transmission protocols depending on the category of the question during transmission. For example, the transmitting unit can apply a specialized transmission protocol to technical questions. It can also apply a standard transmission protocol to general questions. Furthermore, it can apply a security-focused transmission protocol to legal questions. This allows for more appropriate transmission by applying different transmission protocols depending on the category of the question. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or not. For example, the transmitting unit can have AI perform the process of applying different transmission protocols depending on the category of the question.

[0046] The receiving unit can determine the priority of responses based on their importance upon receipt. For example, it may prioritize important responses. It can also prioritize general responses. Furthermore, it may postpone less urgent responses. This allows for the priority of important responses by determining the priority of responses based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit could have AI perform the process of evaluating the importance of responses and determining the priority of responses.

[0047] The receiving unit can apply different receiving protocols depending on the category of the response upon reception. For example, the receiving unit can apply a specialized receiving protocol to technical responses. It can also apply a standard receiving protocol to general responses. Furthermore, it can apply a security-focused receiving protocol to legal responses. This allows for more appropriate reception by applying different receiving protocols depending on the category of the response. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can have AI perform the process of applying different receiving protocols depending on the category of the response.

[0048] The information provider can adjust the level of detail displayed based on the importance of the answer at the time of delivery. For example, it can display important answers in detail. It can also display general answers concisely. Furthermore, it can display only the main points of less urgent answers. This allows for the priority provision of important information by adjusting the level of detail based on the importance of the answer. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can have AI perform the process of evaluating the importance of the answer and adjusting the level of detail displayed.

[0049] The information provider can apply different display algorithms depending on the category of the response at the time of delivery. For example, the provider can apply a specialized display algorithm to technical responses. It can also apply a standard display algorithm to general responses. Furthermore, it can apply an accuracy-focused display algorithm to legal responses. This allows for more appropriate information to be provided by applying different display algorithms depending on the category of the response. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can have AI perform the process of applying different display algorithms depending on the category of the response.

[0050] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows for more appropriate information to be provided by selecting a display method that takes the user's device information into consideration. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the process of analyzing the user's device information and selecting the optimal display method.

[0051] The data provider can improve the accuracy of its display by referring to relevant literature and databases during the data provision process. For example, when displaying technical terms, the data provider can refer to relevant specialized literature. It can also refer to a wide range of databases when displaying general terms. Furthermore, when displaying information related to a specific field, the data provider can refer to databases in that field. This improves the accuracy of the display by referring to relevant literature and databases. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can have AI perform the process of referring to relevant literature and databases.

[0052] The dictionary section can improve the accuracy of technical terms by referring to past translation history. For example, the dictionary section can identify frequently used technical terms from past translation history and improve their accuracy. The dictionary section can also analyze past translation history and correct technical terms that are frequently mistranslated. Furthermore, the dictionary section can evaluate the frequency of use of technical terms based on past translation history and improve their accuracy. In this way, the accuracy of technical terms is improved by referring to past translation history. Some or all of the above processes in the dictionary section may be performed using AI, for example, or not using AI. For example, the dictionary section can have AI perform the process of analyzing past translation history.

[0053] The dictionary section can improve the accuracy of technical terms by referencing relevant literature and databases. For example, when translating technical terms, the dictionary section can refer to relevant specialized literature. It can also refer to a wide range of databases when translating general terms. Furthermore, when translating about a specific field, the dictionary section can refer to databases in that field. This improves the accuracy of technical terms by referencing relevant literature and databases. Some or all of the above processes in the dictionary section may be performed using AI, for example, or not. For example, the dictionary section can have AI perform the process of referencing relevant literature and databases.

[0054] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data. For example, the evaluation unit can identify frequently used evaluation criteria from past evaluation data and optimize the algorithm. The evaluation unit can also analyze past evaluation data and correct criteria that frequently result in misevaluations. Furthermore, the evaluation unit can evaluate the frequency of use of evaluation criteria based on past evaluation data and optimize the algorithm. This improves the accuracy of the evaluation algorithm by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have AI perform the process of analyzing past evaluation data.

[0055] The evaluation unit can improve the accuracy of its evaluation by referring to relevant data. For example, the evaluation unit can refer to relevant data to confirm the accuracy of the evaluation before the evaluation. The evaluation unit can also refer to relevant data to maintain the accuracy of the evaluation during the evaluation. Furthermore, the evaluation unit can refer to relevant data to evaluate the accuracy of the evaluation after the evaluation. In this way, the accuracy of the evaluation is improved by referring to relevant data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have AI perform the process of referring to relevant data.

[0056] The optimization unit can optimize the optimization algorithm by referring to past performance data. For example, the optimization unit can identify frequently used optimization parameters from past performance data and optimize the algorithm. The optimization unit can also analyze past performance data and correct parameters with poor performance. Furthermore, the optimization unit can evaluate the frequency of use of optimization parameters based on past performance data and optimize the algorithm. This improves the accuracy of the optimization algorithm by referring to past performance data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can have AI perform the process of analyzing past performance data.

[0057] The optimization unit can improve the accuracy of optimization by referring to relevant data. For example, the optimization unit can check the accuracy of optimization by referring to relevant data before optimization. The optimization unit can also maintain the accuracy of optimization by referring to relevant data during optimization. Furthermore, the optimization unit can evaluate the accuracy of optimization by referring to relevant data after optimization. In this way, the accuracy of optimization is improved by referring to relevant data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI perform the process of referring to relevant data.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The translation unit can also include a search unit that automatically searches for relevant information based on user input. For example, if a user enters "Please tell me about the latest technology trends," the search unit can automatically search for articles and papers on the latest technology trends related to that question and provide them to the translation unit. This allows the user to obtain more information and improves the accuracy of the answer to the question. The search unit can also prioritize searching for relevant information based on the user's past search history. For example, if a user has asked many questions about AI in the past, the search unit can prioritize searching for the latest information on AI. Furthermore, the search unit can also search for relevant information based on the user's current location. For example, if a user is in a specific region, the search unit can search for technology trends related to that region.

[0060] The translation unit may also include a style selection unit that selects an appropriate translation style based on the user's input. For example, if the user is translating a business document, the style selection unit may select a formal translation style. If the user is translating everyday conversation, it may select a casual translation style. This allows the system to provide an appropriate translation style according to the user's input. The style selection unit can also select an appropriate translation style based on the user's past translation history. For example, if the user has translated many formal documents in the past, it can prioritize selecting a formal translation style. Furthermore, the style selection unit can also select an appropriate translation style based on the user's current situation. For example, if the user is in a hurry, it can select a concise and to-the-point translation style.

[0061] The system may also include a feedback collection unit to gather user feedback and use it to improve the system. For example, the feedback collection unit can collect user evaluations of translation results and use that feedback to improve the system. This allows the system to be improved according to user needs. The feedback collection unit can also adjust the translation algorithm based on user feedback. For example, if a user gives a low rating to a particular translation result, the translation algorithm can be improved. Furthermore, the feedback collection unit can add new features based on user feedback. For example, if a user requests a voice input function, that function can be added based on that feedback.

[0062] The reception unit can also include a suggestion unit that automatically proposes relevant information based on the user's input. For example, if a user inputs "Tell me about the latest technology trends," the suggestion unit can automatically suggest other questions and information related to that question. This allows the user to obtain more information and deepen their understanding of the question. The suggestion unit can also prioritize suggesting relevant information based on the user's past input history. For example, if a user has asked many questions about AI in the past, the suggestion unit can prioritize suggesting the latest information on AI. Furthermore, the suggestion unit can also suggest relevant information based on the user's current location. For example, if a user is in a specific region, the suggestion unit can suggest technology trends related to that region.

[0063] The information provider can also include a display unit that automatically displays relevant information based on user input. For example, if a user inputs "Tell me about the latest technology trends," the display unit can automatically display other questions and information related to that question. This allows the user to obtain more information and deepen their understanding of the question. The display unit can also prioritize displaying relevant information based on the user's past input history. For example, if a user has asked many questions about AI in the past, the display unit can prioritize displaying the latest information about AI. Furthermore, the display unit can also display relevant information based on the user's current location. For example, if a user is in a specific region, the display unit can display technology trends related to that region.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk accepts the user's Japanese input. For example, the user might type, "Please tell me about the latest technology trends." Step 2: The translation department translates the Japanese text received by the reception department into English. For example, the translation department translates "Please tell me about the latest technology trends" to "What are the latest technology trends?". Step 3: The sending unit sends the English question, translated by the translation unit, to the generating AI. Step 4: The receiving unit receives the English response from the generating AI. For example, the generating AI might produce a response such as "The latest technology trends include AI, blockchain, and quantum computing." Step 5: The translation unit translates the English response received by the receiving unit into Japanese. For example, the translation unit translates "The latest technology trends include AI, blockchain, and quantum computing." to "The latest technology trends include AI, blockchain, and quantum computing." Step 6: The provider team provides the user with the Japanese answer translated by the translation team. For example, the provider team might provide the user with something like, "The latest technology trends include AI, blockchain, and quantum computing."

[0066] (Example of form 2) The translation system according to an embodiment of the present invention is a system that automatically translates between Japanese and English using generative AI. When a user inputs in Japanese, this translation system automatically translates the Japanese input into English and asks the generative AI a question. The generative AI responds in English, and the system automatically translates that response back into Japanese and provides it to the user. This mechanism allows even users who only use the Japanese version to utilize the information available from the English-based generative AI. For example, if a user inputs the question "Please tell me about the latest technology trends" in Japanese, this question is automatically translated into English by the system. Next, the translated English question is sent to the generative AI, which generates an answer such as "The latest technology trends include AI, blockchain, and quantum computing." to the question "What are the latest technology trends?". The generated English answer is automatically translated back into Japanese by the system and provided to the user in the form of "The latest technology trends include AI, blockchain, and quantum computing." As a result, users can utilize the information from the English-based generative AI simply by inputting a question in Japanese. For example, by simply inputting "Please tell me about the latest technology trends" in Japanese, they can obtain a detailed answer based on English information. This mechanism allows users who only use the Japanese version to leverage the wealth of information available from the English-based AI-generated content. For example, they can easily obtain information in Japanese that is only available in English, such as technology trends and the latest research findings. This allows users to access more information and enhance their competitiveness. The translation system translates questions entered by the user in Japanese into English, and then translates the AI-generated English answers back into Japanese, enabling users to utilize English-based information.

[0067] The translation system according to this embodiment comprises a reception unit, a translation unit, a transmission unit, a receiving unit, and a providing unit. The reception unit receives Japanese input from the user. For example, the user inputs "Please tell me about the latest technology trends." The translation unit translates the Japanese input received by the reception unit into English. For example, the translation unit translates "Please tell me about the latest technology trends" into "What are the latest technology trends?". The transmission unit sends the English question translated by the translation unit to the generating AI. The generating AI receives the question in English and generates an answer to that question. For example, the generating AI generates an answer such as "The latest technology trends include AI, blockchain, and quantum computing.". The reception unit receives the English answer from the generating AI. The translation unit translates the English answer received by the reception unit into Japanese. For example, the translation unit translates "The latest technology trends include AI, blockchain, and quantum computing." into "The latest technology trends include AI, blockchain, and quantum computing.". The providing unit provides the Japanese answer translated by the translation unit to the user. For example, the provider might provide information to the user in the form of, "The latest technology trends include AI, blockchain, and quantum computing." This allows the translation system, according to the embodiment, to translate the user's Japanese input into English, and then translate the AI's generated English response back into Japanese, enabling the user to utilize English-based information.

[0068] The reception desk receives Japanese input from users. For example, a user might input "Please tell me about the latest technology trends." The reception desk accurately receives the Japanese text entered by the user and converts it into a format that can be processed within the system. Specifically, the reception desk analyzes the text entered through the user interface and checks for grammar and syntax. This allows for appropriate correction of typos, grammatical errors, and other mistakes before proceeding to the next step. Furthermore, the reception desk saves the user's input history and can more accurately understand the user's intent by referring to past inputs. For example, if a user has previously asked a question about "technology trends," the reception desk can refer to that history to recognize that the current input is similar and process it appropriately. The reception desk can also categorize user input and quickly process questions belonging to specific categories using predefined templates. This allows the reception desk to efficiently and accurately receive user input and smoothly process it for the next translation unit.

[0069] The translation department translates Japanese text received by the reception department into English. For example, the translation department translates "Please tell me about the latest technology trends" into "What are the latest technology trends?". The translation department uses natural language processing technology to accurately convert the input Japanese text into English. Specifically, the translation department performs morphological analysis to break down the Japanese sentence into words and phrases and analyze the meaning of each. Next, it selects the appropriate English expression while considering the context. For example, the phrase "latest technology trends" is translated as "latest technology trends," but in this case, it is necessary to accurately understand the relationship between "latest" and "technology trends." Furthermore, the translation department consults dictionaries and databases to provide appropriate translations for specialized and technical terms. This allows it to translate even questions containing technical content into accurate and natural English. In addition, the translation department has a feedback function to evaluate the quality of the translation results, and can continuously improve the translation algorithm based on user feedback. This allows the translation department to always provide high-quality translation results and meet the needs of users.

[0070] The transmitting unit sends the English question, translated by the translation unit, to the generating AI. The generating AI receives the question in English and generates an answer to it. For example, the generating AI might generate an answer such as "The latest technology trends include AI, blockchain, and quantum computing." The transmitting unit is responsible for converting the English question received from the translation unit into an appropriate format and sending it to the generating AI. Specifically, the transmitting unit formats the data into a format that the generating AI can easily understand and adds necessary metadata. For example, by adding the type and priority of the question and relevant contextual information, the transmitting unit enables the generating AI to generate a more appropriate answer. The transmitting unit also encrypts the data and performs error checking to ensure the reliability of the communication. This ensures that the data being transmitted is not tampered with and reaches the generating AI accurately. Furthermore, the transmitting unit monitors the response time from the generating AI and performs retransmission or error handling as needed. This allows the transmitting unit to communicate smoothly with the generating AI and obtain quick and accurate answers.

[0071] The receiving unit receives English responses from the generating AI. The receiving unit is responsible for receiving the response data sent from the generating AI, converting it into an appropriate format, and passing it on to the next process. Specifically, the receiving unit analyzes the data received from the generating AI and extracts the content of the response. For example, if it receives the response "The latest technology trends include AI, blockchain, and quantum computing," it accurately analyzes its content and passes it on to the translation unit. The receiving unit also checks the integrity of the received data and requests the data again if an error occurs. This allows the receiving unit to provide accurate and reliable data to the next process. Furthermore, the receiving unit can temporarily store the received data and refer to past response data as needed. This allows the receiving unit to efficiently receive responses from the generating AI and smoothly process them on to the translation unit.

[0072] The service provider will provide users with Japanese answers translated by the translation service provider. For example, the service provider might provide answers such as, "The latest technology trends include AI, blockchain, and quantum computing." The service provider is responsible for presenting answers to users in an appropriate format. Specifically, the service provider will display answers through the user interface to ensure easy understanding. For example, it can provide answers not only in text format but also in voice using speech synthesis technology. The service provider will also collect user feedback to improve the quality and delivery method of the answers. For example, it will provide a function that allows users to rate their satisfaction with the answers, and use the evaluation results to improve the entire system. Furthermore, the service provider will support multiple devices and platforms, allowing users to receive answers from any device. This enables the service provider to provide users with quick and appropriate answers, improving user convenience.

[0073] The translation unit includes a dictionary unit that utilizes a specialized terminology dictionary. The translation unit uses the specialized terminology dictionary to improve the accuracy of translations. For example, the specialized terminology dictionary includes technical terms and industry-specific terms, and using it improves the accuracy of translations. The dictionary unit, for example, regularly updates the latest technical terms so that the translation unit can always perform translations based on the most up-to-date information. The dictionary unit can also add or correct specialized terms based on user feedback. As a result, the translation unit improves the accuracy of translations by utilizing the specialized terminology dictionary. Some or all of the above processing in the dictionary unit may be performed using AI, for example, or not. For example, the dictionary unit can have AI perform the addition or correction of specialized terms.

[0074] The translation unit includes an evaluation unit that assesses the quality of the translation. The evaluation unit has criteria for evaluating the accuracy and fluency of the translation, and evaluates the quality of the translation based on these criteria. For example, the evaluation unit evaluates the grammatical accuracy and naturalness of the translated text. The evaluation unit can also evaluate the quality of the translation based on user feedback. As a result, the translation unit improves the accuracy of the translation by evaluating the quality of the translation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can have AI perform the translation quality evaluation.

[0075] The system includes an optimization unit to improve system performance. The optimization unit has algorithms for optimizing the system's processing speed and resource usage, and improves system performance based on these algorithms. For example, the optimization unit performs data caching and parallel processing to improve the system's processing speed. The optimization unit can also stop unnecessary processes or adjust resource allocation to optimize resource usage. This improves system performance. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can have AI execute algorithms for improving system performance.

[0076] The transmitting unit can send an English question to the generating AI. For example, the transmitting unit sends an English question translated by the translation unit to the generating AI. This allows the generating AI to receive the question in English and generate an answer to it. Some or all of the above processing in the transmitting unit may be performed using an AI, for example, or not using an AI. For example, the transmitting unit can have an AI perform the process of sending an English question to the generating AI.

[0077] The receiving unit can receive English responses from the generating AI. The receiving unit receives English responses from the generating AI. For example, the receiving unit receives an English response generated by the generating AI. The receiving unit then passes the English response from the generating AI to the translation unit, which can translate the response into Japanese. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can have the AI ​​perform the process of receiving English responses from the generating AI.

[0078] The reception unit can estimate the user's emotions and adjust the Japanese input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable rapid Japanese input. This allows for a more appropriate input environment by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can have a generative AI perform user emotion estimation.

[0079] The reception desk can analyze the user's past input history and suggest the optimal Japanese input method. For example, the reception desk can automatically display as suggestions questions that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions that the user will use at specific times of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can have AI perform the process of analyzing the user's past input history.

[0080] The input system can present input suggestions based on the user's current context when they are typing in Japanese. For example, if the user is typing at night, the input system will present question suggestions related to nighttime. It can also present question suggestions related to the user's location if the user is in a specific location. Furthermore, if the user is typing during a specific event, the input system can present question suggestions related to that event. This allows the system to assist the user's input by presenting input suggestions based on the current context. Some or all of the above processing in the input system may be performed using AI, for example, or without AI. For example, the input system could have AI perform the process of analyzing the user's current context and presenting input suggestions.

[0081] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This allows for a more appropriate input environment by changing the interface design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can have a generative AI perform user emotion estimation.

[0082] The translation unit can estimate the user's emotions and adjust the tone and style of the Japanese-to-English translation based on the estimated emotions. For example, if the user is relaxed, the translation unit will translate in a casual tone. If the user is tense, the translation unit can also translate in a formal tone. Furthermore, if the user is in a hurry, the translation unit can provide a concise and to-the-point translation. This allows for more appropriate translations by adjusting the tone and style of the translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can have a generative AI perform user emotion estimation.

[0083] The translation unit can adjust the level of detail in the translation based on the frequency and importance of technical terms. For example, if there are many technical terms, the unit will provide a detailed translation. Conversely, if there are few technical terms, the unit can provide a concise translation. Furthermore, the unit can add annotations to highly important technical terms. This allows for the provision of more appropriate translations by adjusting the level of detail based on the frequency and importance of technical terms. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can have AI perform the process of evaluating the frequency and importance of technical terms.

[0084] The translation unit can apply different translation algorithms depending on the context during translation. For example, in a technical context, the unit may apply a translation algorithm specialized in technical terms. In the context of everyday conversation, the unit may also apply a translation algorithm that prioritizes natural expression. Furthermore, in the context of legal documents, the unit may apply a translation algorithm that prioritizes accuracy. By applying different translation algorithms depending on the context, a more appropriate translation can be provided. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can have AI perform the process of applying different translation algorithms depending on the context.

[0085] The transmitting unit can estimate the user's emotions and adjust the timing of transmission based on the estimated emotions. For example, if the user is relaxed, the transmitting unit will transmit at a normal time. It can also transmit quickly if the user is in a hurry. Furthermore, if the user is stressed, the transmitting unit can transmit at an appropriate time. This allows for more appropriate transmission timing by adjusting the transmission timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitting unit may be performed using AI, or not. For example, the transmitting unit can have a generative AI perform user emotion estimation.

[0086] The sending unit can determine the priority of sending questions based on their importance at the time of transmission. For example, the sending unit will send important questions first. It can also send general questions with normal priority. Furthermore, it can postpone less urgent questions. This allows important questions to be sent first by determining the priority of sending based on the importance of the questions. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can have AI perform the process of evaluating the importance of questions and determining the priority of sending.

[0087] The transmitting unit can apply different transmission protocols depending on the category of the question during transmission. For example, the transmitting unit can apply a specialized transmission protocol to technical questions. It can also apply a standard transmission protocol to general questions. Furthermore, it can apply a security-focused transmission protocol to legal questions. This allows for more appropriate transmission by applying different transmission protocols depending on the category of the question. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or not. For example, the transmitting unit can have AI perform the process of applying different transmission protocols depending on the category of the question.

[0088] The receiving unit can estimate the user's emotions and adjust the timing of reception based on the estimated emotions. For example, if the user is relaxed, the receiving unit will receive at a normal time. It can also receive quickly if the user is in a hurry. Furthermore, if the user is stressed, the receiving unit can receive at an appropriate time. This allows for more appropriate reception by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the receiving unit may be performed using AI, or not. For example, the receiving unit can have a generative AI perform user emotion estimation.

[0089] The receiving unit can determine the priority of responses based on their importance upon receipt. For example, it may prioritize important responses. It can also prioritize general responses. Furthermore, it may postpone less urgent responses. This allows for the priority of important responses by determining the priority of responses based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit could have AI perform the process of evaluating the importance of responses and determining the priority of responses.

[0090] The receiving unit can apply different receiving protocols depending on the category of the response upon reception. For example, the receiving unit can apply a specialized receiving protocol to technical responses. It can also apply a standard receiving protocol to general responses. Furthermore, it can apply a security-focused receiving protocol to legal responses. This allows for more appropriate reception by applying different receiving protocols depending on the category of the response. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can have AI perform the process of applying different receiving protocols depending on the category of the response.

[0091] The service provider can estimate the user's emotions and adjust the display method of the response based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. Furthermore, if the user is stressed, the service provider can provide a simple and highly visible display method. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generative AI perform user emotion estimation.

[0092] The information provider can adjust the level of detail displayed based on the importance of the answer at the time of delivery. For example, it can display important answers in detail. It can also display general answers concisely. Furthermore, it can display only the main points of less urgent answers. This allows for the priority provision of important information by adjusting the level of detail based on the importance of the answer. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can have AI perform the process of evaluating the importance of the answer and adjusting the level of detail displayed.

[0093] The information provider can apply different display algorithms depending on the category of the response at the time of delivery. For example, the provider can apply a specialized display algorithm to technical responses. It can also apply a standard display algorithm to general responses. Furthermore, it can apply an accuracy-focused display algorithm to legal responses. This allows for more appropriate information to be provided by applying different display algorithms depending on the category of the response. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can have AI perform the process of applying different display algorithms depending on the category of the response.

[0094] The service provider can estimate the user's emotions and adjust the length of the display based on the estimated emotions. For example, if the user is in a hurry, the service provider can display a short, concise display. If the user is relaxed, the service provider can display a longer display with detailed explanations. Furthermore, if the user is excited, the service provider can display a display with visually stimulating effects. By adjusting the length of the display according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generative AI perform user emotion estimation.

[0095] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows for more appropriate information to be provided by selecting a display method that takes the user's device information into consideration. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the process of analyzing the user's device information and selecting the optimal display method.

[0096] The data provider can improve the accuracy of its display by referring to relevant literature and databases during the data provision process. For example, when displaying technical terms, the data provider can refer to relevant specialized literature. It can also refer to a wide range of databases when displaying general terms. Furthermore, when displaying information related to a specific field, the data provider can refer to databases in that field. This improves the accuracy of the display by referring to relevant literature and databases. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can have AI perform the process of referring to relevant literature and databases.

[0097] The dictionary can estimate the user's emotions and select technical terms based on those emotions. For example, if the user is relaxed, the dictionary can select casual technical terms. If the user is tense, it can select formal technical terms. Furthermore, if the user is in a hurry, it can select concise and to-the-point technical terms. This allows for more appropriate translations by selecting technical terms according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dictionary may be performed using AI, for example, or not using AI. For example, the dictionary can have a generative AI perform user emotion estimation.

[0098] The dictionary section can improve the accuracy of technical terms by referring to past translation history. For example, the dictionary section can identify frequently used technical terms from past translation history and improve their accuracy. The dictionary section can also analyze past translation history and correct technical terms that are frequently mistranslated. Furthermore, the dictionary section can evaluate the frequency of use of technical terms based on past translation history and improve their accuracy. In this way, the accuracy of technical terms is improved by referring to past translation history. Some or all of the above processes in the dictionary section may be performed using AI, for example, or not using AI. For example, the dictionary section can have AI perform the process of analyzing past translation history.

[0099] The dictionary can estimate the user's emotions and prioritize technical terms based on those emotions. For example, if the user is relaxed, the dictionary may prioritize casual technical terms. If the user is stressed, it may prioritize formal technical terms. Furthermore, if the user is in a hurry, it may prioritize concise and to-the-point technical terms. This allows for more accurate translations by prioritizing technical terms according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dictionary may be performed using AI, for example, or not using AI. For example, the dictionary can have a generative AI perform user emotion estimation.

[0100] The dictionary section can improve the accuracy of technical terms by referencing relevant literature and databases. For example, when translating technical terms, the dictionary section can refer to relevant specialized literature. It can also refer to a wide range of databases when translating general terms. Furthermore, when translating about a specific field, the dictionary section can refer to databases in that field. This improves the accuracy of technical terms by referencing relevant literature and databases. Some or all of the above processes in the dictionary section may be performed using AI, for example, or not. For example, the dictionary section can have AI perform the process of referencing relevant literature and databases.

[0101] The evaluation unit can estimate the user's emotions and adjust the translation evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can apply casual evaluation criteria. If the user is tense, the evaluation unit can also apply formal evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can apply concise and to-the-point evaluation criteria. This allows for more appropriate translation evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can have a generative AI perform user emotion estimation.

[0102] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data. For example, the evaluation unit can identify frequently used evaluation criteria from past evaluation data and optimize the algorithm. The evaluation unit can also analyze past evaluation data and correct criteria that frequently result in misevaluations. Furthermore, the evaluation unit can evaluate the frequency of use of evaluation criteria based on past evaluation data and optimize the algorithm. This improves the accuracy of the evaluation algorithm by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have AI perform the process of analyzing past evaluation data.

[0103] The evaluation unit can estimate the user's emotions and adjust the frequency of evaluations based on the estimated emotions. For example, if the user is relaxed, the evaluation unit will perform evaluations at a normal frequency. It can also perform evaluations quickly if the user is in a hurry. Furthermore, if the user is stressed, the evaluation unit can perform evaluations at an appropriate frequency. This allows for more accurate translation evaluations by adjusting the frequency of evaluations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 evaluation unit may be performed using AI, or not. For example, the evaluation unit can have a generative AI perform user emotion estimation.

[0104] The evaluation unit can improve the accuracy of its evaluation by referring to relevant data. For example, the evaluation unit can refer to relevant data to confirm the accuracy of the evaluation before the evaluation. The evaluation unit can also refer to relevant data to maintain the accuracy of the evaluation during the evaluation. Furthermore, the evaluation unit can refer to relevant data to evaluate the accuracy of the evaluation after the evaluation. In this way, the accuracy of the evaluation is improved by referring to relevant data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have AI perform the process of referring to relevant data.

[0105] The optimization unit can estimate the user's emotions and adjust the system's optimization parameters based on the estimated emotions. For example, if the user is relaxed, the optimization unit uses normal optimization parameters. It can also use rapid optimization parameters if the user is in a hurry. Furthermore, if the user is stressed, the optimization unit can select appropriate optimization parameters. This allows for more appropriate system optimization by adjusting the optimization parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 optimization unit may be performed using AI, or not. For example, the optimization unit can have a generative AI perform user emotion estimation.

[0106] The optimization unit can optimize the optimization algorithm by referring to past performance data. For example, the optimization unit can identify frequently used optimization parameters from past performance data and optimize the algorithm. The optimization unit can also analyze past performance data and correct parameters with poor performance. Furthermore, the optimization unit can evaluate the frequency of use of optimization parameters based on past performance data and optimize the algorithm. This improves the accuracy of the optimization algorithm by referring to past performance data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can have AI perform the process of analyzing past performance data.

[0107] The optimization unit can estimate the user's emotions and adjust the optimization frequency based on the estimated emotions. For example, if the user is relaxed, the optimization unit will optimize at a normal frequency. It can also optimize quickly if the user is in a hurry. Furthermore, if the user is stressed, the optimization unit can optimize at an appropriate frequency. This allows for more appropriate system optimization by adjusting the optimization frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 optimization unit may be performed using AI, or not. For example, the optimization unit can have a generative AI perform user emotion estimation.

[0108] The optimization unit can improve the accuracy of optimization by referring to relevant data. For example, the optimization unit can check the accuracy of optimization by referring to relevant data before optimization. The optimization unit can also maintain the accuracy of optimization by referring to relevant data during optimization. Furthermore, the optimization unit can evaluate the accuracy of optimization by referring to relevant data after optimization. In this way, the accuracy of optimization is improved by referring to relevant data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI perform the process of referring to relevant data.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The translation system can also include a voice input unit that accepts user voice input. The voice input unit can convert the user's voice input into text and pass it to the translation unit. For example, if a user voice-inputs "Tell me about the latest technology trends," the voice input unit will convert the voice into text and pass it to the translation unit. This allows users to ask questions using voice input, enabling more intuitive operation. The voice input unit can also analyze the user's voice tone and speed to estimate their emotions. For example, if a user is in a hurry, the voice input unit can determine this based on the speed of their voice and process the request quickly. Furthermore, if a user is relaxed, the voice input unit can determine this based on their voice tone and translate in a more casual tone.

[0111] The translation unit can also include a search unit that automatically searches for relevant information based on user input. For example, if a user enters "Please tell me about the latest technology trends," the search unit can automatically search for articles and papers on the latest technology trends related to that question and provide them to the translation unit. This allows the user to obtain more information and improves the accuracy of the answer to the question. The search unit can also prioritize searching for relevant information based on the user's past search history. For example, if a user has asked many questions about AI in the past, the search unit can prioritize searching for the latest information on AI. Furthermore, the search unit can also search for relevant information based on the user's current location. For example, if a user is in a specific region, the search unit can search for technology trends related to that region.

[0112] The translation unit may also include a style selection unit that selects an appropriate translation style based on the user's input. For example, if the user is translating a business document, the style selection unit may select a formal translation style. If the user is translating everyday conversation, it may select a casual translation style. This allows the system to provide an appropriate translation style according to the user's input. The style selection unit can also select an appropriate translation style based on the user's past translation history. For example, if the user has translated many formal documents in the past, it can prioritize selecting a formal translation style. Furthermore, the style selection unit can also select an appropriate translation style based on the user's current situation. For example, if the user is in a hurry, it can select a concise and to-the-point translation style.

[0113] The system may also include a feedback collection unit to gather user feedback and use it to improve the system. For example, the feedback collection unit can collect user evaluations of translation results and use that feedback to improve the system. This allows the system to be improved according to user needs. The feedback collection unit can also adjust the translation algorithm based on user feedback. For example, if a user gives a low rating to a particular translation result, the translation algorithm can be improved. Furthermore, the feedback collection unit can add new features based on user feedback. For example, if a user requests a voice input function, that function can be added based on that feedback.

[0114] The sending unit can further estimate the user's emotions and adjust the content of the message based on those emotions. For example, if the user is stressed, the sending unit can take that emotion into consideration and provide a concise and to-the-point message. Conversely, if the user is relaxed, it can provide a message that includes detailed information. This allows for more appropriate information to be provided by adjusting the content of the message according to the user's emotions. Furthermore, the sending unit can also adjust the timing of the message based on the user's emotions. For example, if the user is in a hurry, the message can be sent quickly. Conversely, if the user is relaxed, the message can be sent at a normal timing. This allows for more appropriate information to be provided by adjusting the timing of the message according to the user's emotions.

[0115] The receiving unit can further estimate the user's emotions and adjust the content of the message based on those emotions. For example, if the user is stressed, the receiving unit can take that emotion into consideration and provide concise and to-the-point content. Conversely, if the user is relaxed, it can provide content that includes detailed information. This allows for more appropriate information to be provided by adjusting the content according to the user's emotions. Furthermore, the receiving unit can also adjust the timing of the message based on the user's emotions. For example, if the user is in a hurry, the message can be received quickly. Conversely, if the user is relaxed, the message can be received at a normal timing. This allows for more appropriate information to be provided by adjusting the timing of the message according to the user's emotions.

[0116] The reception unit can also include a suggestion unit that automatically proposes relevant information based on the user's input. For example, if a user inputs "Tell me about the latest technology trends," the suggestion unit can automatically suggest other questions and information related to that question. This allows the user to obtain more information and deepen their understanding of the question. The suggestion unit can also prioritize suggesting relevant information based on the user's past input history. For example, if a user has asked many questions about AI in the past, the suggestion unit can prioritize suggesting the latest information on AI. Furthermore, the suggestion unit can also suggest relevant information based on the user's current location. For example, if a user is in a specific region, the suggestion unit can suggest technology trends related to that region.

[0117] The reception desk can further estimate the user's emotions and automatically complete the input content based on the estimated emotions. For example, if the user is stressed, the reception desk can take that emotion into consideration and automatically complete the input content in a concise and to-the-point manner. Conversely, if the user is relaxed, it can automatically complete the input content to include detailed information. This allows for more appropriate information to be provided by completing the input content according to the user's emotions. Furthermore, the reception desk can adjust the timing of input based on the user's emotions. For example, if the user is in a hurry, the input can be completed quickly. Conversely, if the user is relaxed, the input can be completed at a normal timing. This allows for more appropriate information to be provided by adjusting the timing of input according to the user's emotions.

[0118] The information provider can further estimate the user's emotions and adjust the displayed content based on those emotions. For example, if the user is stressed, the information provider can take that emotion into consideration and provide concise and to-the-point content. Conversely, if the user is relaxed, it can provide content that includes detailed information. This allows for more appropriate information to be provided by adjusting the displayed content according to the user's emotions. Furthermore, the information provider can also adjust the timing of the display based on the user's emotions. For example, if the user is in a hurry, the information can be displayed quickly. Conversely, if the user is relaxed, the information can be displayed at the normal timing. This allows for more appropriate information to be provided by adjusting the timing of the display according to the user's emotions.

[0119] The information provider can also include a display unit that automatically displays relevant information based on user input. For example, if a user inputs "Tell me about the latest technology trends," the display unit can automatically display other questions and information related to that question. This allows the user to obtain more information and deepen their understanding of the question. The display unit can also prioritize displaying relevant information based on the user's past input history. For example, if a user has asked many questions about AI in the past, the display unit can prioritize displaying the latest information about AI. Furthermore, the display unit can also display relevant information based on the user's current location. For example, if a user is in a specific region, the display unit can display technology trends related to that region.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk accepts the user's Japanese input. For example, the user might type, "Please tell me about the latest technology trends." Step 2: The translation department translates the Japanese text received by the reception department into English. For example, the translation department translates "Please tell me about the latest technology trends" to "What are the latest technology trends?". Step 3: The sending unit sends the English question, translated by the translation unit, to the generating AI. Step 4: The receiving unit receives the English response from the generating AI. For example, the generating AI might produce a response such as "The latest technology trends include AI, blockchain, and quantum computing." Step 5: The translation unit translates the English response received by the receiving unit into Japanese. For example, the translation unit translates "The latest technology trends include AI, blockchain, and quantum computing." to "The latest technology trends include AI, blockchain, and quantum computing." Step 6: The provider team provides the user with the Japanese answer translated by the translation team. For example, the provider team might provide the user with something like, "The latest technology trends include AI, blockchain, and quantum computing."

[0122] 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.

[0123] 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.

[0124] 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.

[0125] For example, the reception unit is implemented by the reception device 38 of the smart device 14. For example, the translation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is implemented by the communication I / F 44 of the smart device 14. For example, the receiving unit is implemented by the communication I / F 26 of the data processing device 12. For example, the providing unit is implemented by the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214. For example, the translation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is implemented by the communication I / F 44 of the smart glasses 214. For example, the reception unit is implemented by the communication I / F 26 of the data processing device 12. For example, the delivery unit is implemented by the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314. For example, the translation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is implemented by the communication I / F 44 of the headset terminal 314. For example, the receiving unit is implemented by the communication I / F 26 of the data processing device 12. For example, the providing unit is implemented by the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] For example, the reception unit is implemented by the microphone 238 of the robot 414. For example, the translation unit is implemented by the specific processing unit 290 of the data processing unit 12. For example, the transmission unit is implemented by the communication I / F 44 of the robot 414. For example, the reception unit is implemented by the communication I / F 26 of the data processing unit 12. For example, the delivery unit is implemented by the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A reception desk that accepts Japanese input from users, The translation department translates the Japanese received by the reception department into English. A transmission unit that sends the English question translated by the aforementioned translation unit to the generating AI, A receiving unit that receives English responses from the generating AI, A translation unit that translates the English response received by the receiving unit into Japanese, The system includes a provisioning unit that provides the user with the Japanese response translated by the aforementioned translation unit. A system characterized by the following features. (Note 2) The translation department, A dictionary section is provided to utilize a specialized terminology dictionary. The system described in Appendix 1, characterized by the features described herein. (Note 3) The translation department, It includes an evaluation section to assess the quality of translations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system is We have an optimization department to improve system performance. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transmitting unit Send an English question to the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The receiving unit is Receive an English response from a generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the Japanese input interface based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal Japanese input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When typing in Japanese, the system will suggest input candidates based on the user's current context. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned translation department, It estimates the user's emotions and adjusts the tone and style of the Japanese-to-English translation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned translation department, During translation, adjust the level of detail based on the frequency and importance of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned translation department, During translation, different translation algorithms are applied depending on the context. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned transmitting unit It estimates the user's emotions and adjusts the timing of sending messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned transmitting unit When sending, the system prioritizes sending based on the importance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned transmitting unit When sending, different sending protocols are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The receiving unit is It estimates the user's emotions and adjusts the timing of reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The receiving unit is Upon receiving a response, the system prioritizes its reception based on its perceived importance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The receiving unit is When receiving data, apply different receiving protocols depending on the category of the response. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the information, adjust the level of detail displayed based on the importance of the response. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the data, different display algorithms will be applied depending on the category of the response. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the data, we refer to relevant literature and databases to improve the accuracy of the display. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dictionary section is, The system estimates the user's emotions and selects technical terms based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned dictionary section is, In the dictionary section, we improve the accuracy of specialized terminology by referring to past translation history. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned dictionary section is, It estimates the user's emotions and prioritizes technical terms based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned dictionary section is, In the dictionary section, we improve the accuracy of technical terms by referring to relevant literature and databases. The system described in Appendix 2, characterized by the features described herein. (Note 30) The evaluation unit, It estimates the user's emotions and adjusts the translation evaluation criteria based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The evaluation unit, In the evaluation unit, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 3, characterized by the features described herein. (Note 32) The evaluation unit, It estimates the user's emotions and adjusts the frequency of evaluations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The evaluation unit, The evaluation department improves the accuracy of evaluations by referring to relevant data. The system described in Appendix 3, characterized by the features described herein. (Note 34) The optimization unit, The system estimates the user's emotions and adjusts the system's optimization parameters based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The optimization unit, In the optimization unit, the optimization algorithm is optimized by referring to past performance data. The system described in Appendix 4, characterized by the features described herein. (Note 36) The optimization unit, It estimates the user's emotions and adjusts the optimization frequency based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The optimization unit, The optimization unit improves the accuracy of optimization by referring to relevant data. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0194] 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 reception desk that accepts Japanese input from users, The translation department translates the Japanese received by the reception department into English. A transmission unit that sends the English question translated by the translation unit to the generating AI, A receiving unit that receives English responses from the generating AI, A translation unit that translates the English response received by the receiving unit into Japanese, The system includes a provisioning unit that provides the user with the Japanese response translated by the aforementioned translation unit. A system characterized by the following features.

2. The aforementioned translation department, A dictionary section is provided to utilize a specialized terminology dictionary. The system according to feature 1.

3. The aforementioned translation department, It includes an evaluation section to assess the quality of translations. The system according to feature 1.

4. The system is We have an optimization department to improve system performance. The system according to feature 1.

5. The aforementioned transmitting unit Send an English question to the AI ​​generator. The system according to feature 1.

6. The receiving unit is Receive an English response from the AI ​​generator. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the Japanese input interface based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal Japanese input method. The system according to feature 1.

9. The aforementioned reception unit is When typing in Japanese, the system will suggest input candidates based on the user's current context. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system according to feature 1.

Images