System

Abstract

An object of the system according to the embodiment is to efficiently perform translation, explanation, and real-time interpretation of technical terms.SOLUTION: A system according to an embodiment includes a translation unit, an explanation unit, an interpretation unit, a learning unit, and a guide unit. The translation unit translates the technical term into another language. The explanation unit provides an explanation according to the context based on the technical term translated by the translation unit. The interpretation unit performs real-time interpretation based on the commentary provided by the commentary unit. The learning unit learns the technical term based on the interpretation result provided by the interpretation unit. The guide unit provides a technical term guide for the user based on the technical term learned by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately translate or explain technical terms or provide real-time interpretation, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently translate, explain, and interpret technical terms in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a translation unit, an explanation unit, an interpretation unit, a learning unit, and a guide unit. The translation unit translates technical terms into other languages. The explanation unit provides context-appropriate explanations based on the technical terms translated by the translation unit. The interpretation unit provides real-time interpretation based on the explanations provided by the explanation unit. The learning unit learns technical terms based on the interpretation results provided by the interpretation unit. The guide unit provides a technical terminology guide for users based on the technical terms learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently translate, explain, and interpret technical terms in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to provide comprehensive translation, explanation, interpretation, learning, and guidance for technical terms. This system translates technical terms into other languages, provides context-sensitive explanations, performs real-time interpretation, learns technical terms, and provides a technical terminology guide for users. This reduces the time required to understand content due to the increasing complexity of internal systems and improves work efficiency. For example, the system can translate technical terms in fields such as medicine, law, technology, and business into other languages. The system can also analyze sentences and paragraphs containing technical terms and provide context-sensitive explanations. Furthermore, the system can provide real-time interpretation for meetings and other events that contain a large amount of technical terminology. The system can improve its interpretation of technical terms by allowing users to learn new technical terms and their definitions. Finally, the system can provide a glossary for each field, as well as the meanings and usage of technical terms. This helps users understand specialized knowledge and improves work efficiency.

[0029] A technical terminology translation system according to an embodiment includes a translation unit, an explanation unit, an interpretation unit, a learning unit, and a guide unit. The translation unit translates technical terms into other languages. For example, the translation unit can translate technical terms in fields such as medicine, law, technology, and business into other languages. The translation unit can also translate technical terms using a generation AI. For example, the generation AI translates terms in a specific technical field into an appropriate language. The explanation unit provides context-appropriate explanations based on the technical terms translated by the translation unit. For example, the explanation unit can analyze sentences or paragraphs containing technical terms and provide context-appropriate explanations. The explanation unit can also use the generation AI to provide context-appropriate explanations of the technical terms. For example, the generation AI can analyze sentences or paragraphs containing technical terms, explain the terms, and provide related information and background information. The interpretation unit provides real-time interpretation based on the explanations provided by the explanation unit. For example, the interpretation unit can provide real-time interpretation for meetings containing a large amount of technical terms. The interpretation unit can also use the generation AI to provide real-time interpretation. For example, the generation AI can interpret technical terms in real time and provide appropriate translations in situations such as meetings containing a large amount of technical terminology. The learning unit learns technical terms based on the interpretation results provided by the interpretation unit. For example, the learning unit can improve the interpretation of technical terms by having a user teach the learning unit new technical terms and their definitions. The learning unit can also use the generation AI to learn technical terms. For example, the generation AI can improve the interpretation of technical terms by having a user teach the learning unit new technical terms and their definitions. The guide unit provides a technical terminology guide for users based on the technical terms learned by the learning unit. For example, the guide unit can provide a glossary for each field, as well as the meanings and usages of technical terms. The guide unit can also provide a technical terminology guide using the generation AI. For example, the generation AI can provide a guide on technical terms in a specific field, allowing users to easily search for glossaries for each field, as well as the meanings and usages of technical terms. This allows the technical terminology translation system according to the embodiment to help users understand specialized knowledge.

[0030] The translation unit can translate technical terms in the medical, legal, technical, and business fields into other languages. The medical, legal, technical, and business fields include, but are not limited to, medical specialties, laws and precedents, technical specifications, and business terms. The translation unit can, for example, translate technical terms in the medical field into other languages. The translation unit can also translate technical terms in the legal field into other languages. The translation unit can also translate technical terms in the technical field into other languages. The translation unit can also translate technical terms in the business field into other languages. This enables the translation of technical terms specialized in specific fields. Some or all of the above-described processing in the translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the translation unit can input technical terms in the medical field into a generation AI, which can then translate the terms into an appropriate language.

[0031] The explanation unit can analyze sentences and texts containing technical terms and provide explanations appropriate to the context. The explanation unit, for example, uses natural language processing technology to analyze sentences and texts containing technical terms. For example, the explanation unit performs grammatical analysis and semantic analysis to provide explanations appropriate to the context. The explanation unit can also use a generation AI to provide explanations appropriate to the context of technical terms. For example, the generation AI analyzes sentences and texts containing technical terms, explains the terms, and provides related information and background. This provides explanations appropriate to the context, thereby deepening understanding of the technical terms. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the explanation unit can input sentences and texts containing technical terms into a generation AI, which can explain the terms and provide related information and background.

[0032] The interpretation unit can provide real-time interpretation for conferences and other events that contain a lot of technical terminology. The interpretation unit provides real-time interpretation for conferences and other events that contain a lot of technical terminology. For example, the interpretation unit uses real-time interpretation technology to translate technical terminology into an appropriate language. The interpretation unit can also provide real-time interpretation using a generation AI. For example, the generation AI interprets technical terminology in real time for conferences and other events that contain a lot of technical terminology and provides an appropriate translation. This provides real-time interpretation to support understanding of technical terminology in conferences and other events. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, the generation AI. For example, the interpretation unit can input audio data of a conference that contains a lot of technical terminology into the generation AI, and the generation AI can provide interpretation in real time.

[0033] The learning unit can improve the interpretation of technical terms by teaching new technical terms and their definitions to the learning unit. For example, the user inputs new technical terms and their definitions to the learning unit through an interface. For example, the learning unit provides a dedicated interface for the user to input new technical terms and their definitions. The learning unit can also use a generation AI to learn technical terms. For example, the user teaches the generation AI new technical terms and their definitions, and the generation AI uses the information to improve the interpretation of technical terms. This allows the accuracy of the interpretation of technical terms to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI. For example, the learning unit can input new technical terms and their definitions input by the user to the generation AI, and the generation AI can learn the information to improve the interpretation of technical terms.

[0034] The guide unit can provide a glossary for each field, and the meanings and usages of technical terms. For example, the guide unit can provide glossaries for each field, such as medical, legal, technical, and business glossaries. The guide unit can also use a generation AI to provide a terminology guide. For example, the generation AI can provide a guide on technical terms for a specific field, allowing users to easily search for glossaries for each field, the meanings and usages of technical terms, etc. This allows users to easily search for and understand technical terms. Some or all of the above-described processing in the guide unit can be performed using, or without, the generation AI. For example, the guide unit can input glossaries for each field, the meanings and usages of technical terms, and the usages of technical terms into the generation AI, and the generation AI can provide a guide based on that information.

[0035] During translation, the translation unit can determine translation priorities based on the frequency of use of technical terms. For example, the translation unit measures the frequency of use of technical terms and determines translation priorities based on that frequency. For example, the translation unit prioritizes translation of frequently used technical terms. The translation unit can also postpone translation of less frequently used technical terms. The translation unit can also translate frequently used technical terms first and present the translated version to the user. This enables efficient translation by prioritizing translation of frequently used technical terms. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input data on the frequency of use of technical terms into the generation AI, and the generation AI can determine translation priorities based on the frequency.

[0036] The translation unit can apply different translation algorithms to different fields of terminology during translation. For example, the translation unit applies a medical translation algorithm to medical terminology. For example, the translation unit performs translation using an algorithm specialized for medical terminology. The translation unit can also apply a legal translation algorithm to legal terminology. The translation unit can also apply a technical translation algorithm to technical terminology. The translation unit can also apply a business translation algorithm to business terminology. This improves translation accuracy by applying the optimal translation algorithm for each field. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input medical terminology into a generation AI, which then applies a medical translation algorithm to perform translation.

[0037] The translation unit can improve the accuracy of translation by referring to the user's past translation history during translation. The translation unit, for example, refers to the user's past translation history and improves the accuracy of the translation based on that history. For example, the translation unit can refer to technical terms that the user has translated in the past to improve the translation accuracy of the same terms. The translation unit can also learn frequently used expressions from the user's past translation history and reflect them in the translation. The translation unit can also analyze the user's past translation history to maintain consistency in the translation. In this way, by referring to the past translation history, the consistency and accuracy of the translation are improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input the user's past translation history data into the generation AI, and the generation AI can improve the accuracy of the translation based on that history.

[0038] During translation, the translation unit can determine the priority of translation based on the submission date of technical terms. The translation unit, for example, measures the submission date of technical terms and determines the priority of translation based on that date. For example, the translation unit prioritizes translation of recently submitted technical terms. The translation unit can also postpone the translation of technical terms that were submitted earlier. The translation unit can also dynamically adjust the priority of translation based on the submission date. This enables efficient translation by determining the priority of translation based on the submission date. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input data on the submission date of technical terms into the generation AI, and the generation AI can determine the priority of translation based on that date.

[0039] The translation unit can adjust the order of translation based on the relevance of technical terms during translation. The translation unit, for example, measures the relevance of technical terms and adjusts the order of translation based on the relevance. For example, the translation unit prioritizes translating highly relevant technical terms. The translation unit can also postpone translating less relevant technical terms. The translation unit can also dynamically adjust the order of translation based on the relevance of technical terms. This enables efficient translation by adjusting the order of translation based on the relevance of technical terms. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI, for example. For example, the translation unit can input relevance data of technical terms into a generation AI, and the generation AI can adjust the order of translation based on the relevance.

[0040] During translation, the translation unit can adjust the use of technical terms in the translation according to the user's level of expertise. For example, the translation unit evaluates the user's level of expertise and adjusts the use of technical terms in the translation according to that level. For example, the translation unit uses detailed technical terms for users with a high level of expertise. The translation unit can also use simple technical terms for users with a low level of expertise. The translation unit can also dynamically adjust the use of technical terms in the translation according to the user's level of expertise. This enables more appropriate translation by providing a translation according to the user's level of expertise. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the translation according to that level.

[0041] The explanation unit can adjust the level of detail of the explanation based on the importance of the technical term when providing the explanation. The explanation unit, for example, measures the importance of the technical term and adjusts the level of detail of the explanation based on the importance. For example, the explanation unit provides a detailed explanation for technical terminology with high importance. The explanation unit can also provide a concise explanation for technical terminology with low importance. The explanation unit can also dynamically adjust the level of detail of the explanation based on the importance of the technical term. This enables efficient explanation by adjusting the level of detail of the explanation based on the importance of the technical term. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, a generation AI. For example, the explanation unit can input importance data of technical terms into the generation AI, and the generation AI can adjust the level of detail of the explanation based on the importance.

[0042] The explanation unit can apply different explanation algorithms depending on the category of the technical term when providing explanations. For example, the explanation unit applies a medical-specialized explanation algorithm to technical terminology in the medical field. For example, the explanation unit provides explanations using an algorithm specialized for medical terminology. The explanation unit can also apply a legal-specialized explanation algorithm to technical terminology in the legal field. The explanation unit can also apply a technical-specialized explanation algorithm to technical terminology in the technical field. The explanation unit can also apply a business-specialized explanation algorithm to technical terminology in the business field. In this way, by applying an explanation algorithm according to the category of the technical terminology, the accuracy of the explanations is improved. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the explanation unit can input technical terminology in the medical field into a generation AI, which can then provide explanations by applying a medical-specialized explanation algorithm.

[0043] When providing an explanation, the explanation unit can improve the accuracy of the explanation by referring to the user's past explanation results. For example, the explanation unit refers to the user's past explanation results and improves the accuracy of the explanation based on the results. For example, the explanation unit improves the accuracy of the explanation of the same term by referring to explanations the user has received in the past. The explanation unit can also learn frequently used expressions from the user's past explanation history and reflect them in the explanation. The explanation unit can also analyze the user's past explanation history to maintain consistency in the explanation. In this way, by referring to the past explanation results, the consistency and accuracy of the explanation are improved. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the explanation unit can input the user's past explanation result data into the generation AI, and the generation AI can improve the accuracy of the explanation based on the results.

[0044] The explanation unit can determine the priority of the explanation based on the submission date of the technical term when providing the explanation. The explanation unit, for example, measures the submission date of the technical term and determines the priority of the explanation based on that date. For example, the explanation unit prioritizes explanation of recently submitted technical term. The explanation unit can also postpone technical terminology submitted earlier. The explanation unit can also dynamically adjust the priority of the explanation based on the submission date. This enables efficient explanation by determining the priority of the explanation based on the submission date. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, a generation AI. For example, the explanation unit can input data on the submission date of the technical term into the generation AI, and the generation AI can determine the priority of the explanation based on that date.

[0045] The explanation unit can adjust the order of explanations based on the relevance of technical terms during explanation. The explanation unit, for example, measures the relevance of technical terms and adjusts the order of explanations based on the relevance. For example, the explanation unit prioritizes explanations of highly relevant technical terms. The explanation unit can also postpone explanations of less relevant technical terms. The explanation unit can also dynamically adjust the order of explanations based on the relevance of technical terms. This enables efficient explanations by adjusting the order of explanations based on the relevance of technical terms. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, a generation AI. For example, the explanation unit can input relevance data of technical terms into a generation AI, and the generation AI can adjust the order of explanations based on the relevance.

[0046] The commentary unit can adjust the use of technical terms in the commentary according to the user's level of expertise during commentary. For example, the commentary unit evaluates the user's level of expertise and adjusts the use of technical terms in the commentary according to that level. For example, the commentary unit uses detailed technical terms for users with a high level of expertise. The commentary unit can also use simple technical terms for users with a low level of expertise. The commentary unit can also dynamically adjust the use of technical terms in the commentary according to the user's level of expertise. This enables more appropriate commentary by providing commentary according to the user's level of expertise. Some or all of the above-described processing in the commentary unit may be performed using, or without, a generation AI. For example, the commentary unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the commentary according to that level.

[0047] During interpretation, the interpretation unit can determine the priority of interpretation based on the frequency of use of technical terms. For example, the interpretation unit measures the frequency of use of technical terms and determines the priority of interpretation based on that frequency. For example, the interpretation unit prioritizes interpretation of frequently used technical terms. The interpretation unit can also postpone interpretation of less frequently used technical terms. The interpretation unit can also translate frequently used technical terms first and present the translated terms to the user. This enables efficient interpretation by prioritizing interpretation of frequently used technical terms. Some or all of the above-described processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input data on the frequency of use of technical terms into the generation AI, and the generation AI can determine the priority of interpretation based on the frequency.

[0048] The interpretation unit can apply different interpretation algorithms to different fields of terminology during interpretation. For example, the interpretation unit applies a medical-specific interpretation algorithm to medical terminology. For example, the interpretation unit performs interpretation using an algorithm specialized for medical terminology. The interpretation unit can also apply a legal-specific interpretation algorithm to legal terminology. The interpretation unit can also apply a technical-specific interpretation algorithm to technical terminology. The interpretation unit can also apply a business-specific interpretation algorithm to business terminology. This improves the accuracy of interpretation by applying the optimal interpretation algorithm for each field. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interpretation unit can input medical terminology into a generation AI, which then applies a medical-specific interpretation algorithm to perform interpretation.

[0049] The interpretation unit can improve the accuracy of interpretation by referring to the user's past interpretation history during interpretation. For example, the interpretation unit refers to the user's past interpretation history and improves the accuracy of interpretation based on that history. For example, the interpretation unit refers to technical terms that the user has previously interpreted to improve the accuracy of interpretation of the same terms. The interpretation unit can also learn frequently used expressions from the user's past interpretation history and reflect them in the interpretation. The interpretation unit can also analyze the user's past interpretation history to maintain consistency in the interpretation. In this way, by referring to the past interpretation history, the consistency and accuracy of the interpretation are improved. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interpretation unit can input the user's past interpretation history data into the generation AI, and the generation AI can improve the accuracy of the interpretation based on that history.

[0050] During interpretation, the interpretation unit can determine the priority of interpretation based on the submission date of technical terms. For example, the interpretation unit measures the submission date of technical terms and determines the priority of interpretation based on that date. For example, the interpretation unit prioritizes interpretation of recently submitted technical terms. The interpretation unit can also postpone the interpretation of technical terms that were submitted earlier. The interpretation unit can also dynamically adjust the priority of interpretation based on the submission date. This enables efficient interpretation by determining the priority of interpretation based on the submission date. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input data on the submission date of technical terms into the generation AI, and the generation AI can determine the priority of interpretation based on that date.

[0051] The interpretation unit can adjust the order of interpretation based on the relevance of technical terms during interpretation. The interpretation unit, for example, measures the relevance of technical terms and adjusts the order of interpretation based on the relevance. For example, the interpretation unit prioritizes interpretation of highly relevant technical terms. The interpretation unit can also postpone interpretation of less relevant technical terms. The interpretation unit can also dynamically adjust the order of interpretation based on the relevance of technical terms. This enables efficient interpretation by adjusting the order of interpretation based on the relevance of technical terms. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input relevance data of technical terms into a generation AI, and the generation AI can adjust the order of interpretation based on the relevance.

[0052] During interpretation, the interpretation unit can adjust the use of technical terms in the interpretation according to the user's level of expertise. For example, the interpretation unit evaluates the user's level of expertise and adjusts the use of technical terms in the interpretation according to that level. For example, the interpretation unit uses detailed technical terms for users with a high level of expertise. The interpretation unit can also use simple technical terms for users with a low level of expertise. The interpretation unit can also dynamically adjust the use of technical terms in the interpretation according to the user's level of expertise. This enables more appropriate interpretation by providing an interpretation according to the user's level of expertise. Some or all of the above-described processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the interpretation according to that level.

[0053] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, refers to past learning data and optimizes the learning algorithm based on that data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also extract effective learning patterns from the past learning data and reflect them in the algorithm. The learning unit can also analyze past learning data and improve the accuracy of the learning algorithm. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into the generation AI, and the generation AI can optimize the learning algorithm based on that data.

[0054] The learning unit can update the learning data by reflecting user feedback during learning. The learning unit, for example, collects user feedback and updates the learning data based on that feedback. For example, the learning unit updates the learning data based on user feedback. The learning unit can also fill in deficiencies in the learning data from user feedback. The learning unit can also analyze user feedback and improve the quality of the learning data. In this way, the quality of the learning data is improved by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input user feedback data into the generation AI, and the generation AI can update the learning data based on that feedback.

[0055] During learning, the learning unit can weight the learning data based on the submission time of the technical term. For example, the learning unit measures the submission time of the technical term and weights the learning data based on that time. For example, the learning unit weights recently submitted technical terminology. The learning unit can also lightly weight older submitted technical terminology. The learning unit can also dynamically adjust the weighting of the learning data based on the submission time. This enables efficient learning by weighting the learning data based on the submission time. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input submission time data of technical terminology into the generation AI, and the generation AI can weight the learning data based on that time.

[0056] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit collects information from different data sources and integrates the information to enrich the training data. For example, the learning unit analyzes information from different data sources to improve the quality of the training data. The learning unit can also fill in deficiencies in the training data based on information from different data sources. In this way, the quality of the training data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input information from different data sources into the generation AI, which then integrates the information to enrich the training data.

[0057] When providing a guide, the guide unit can adjust the level of detail of the guide based on the importance of the technical term. For example, the guide unit measures the importance of the technical term and adjusts the level of detail of the guide based on the importance. For example, the guide unit provides a detailed guide for technical terminology with high importance. The guide unit can also provide a concise guide for technical terminology with low importance. The guide unit can also dynamically adjust the level of detail of the guide based on the importance of the technical term. This enables efficient guidance by adjusting the level of detail of the guide based on the importance of the technical term. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input importance data of technical terminology to the generation AI, and the generation AI can adjust the level of detail of the guide based on the importance.

[0058] When providing a guide, the guide unit can apply different guide algorithms depending on the category of the terminology. For example, the guide unit applies a medical-specialized guide algorithm to medical terminology. For example, the guide unit provides a guide using an algorithm specialized for medical terminology. The guide unit can also apply a legal-specialized guide algorithm to legal terminology. The guide unit can also apply a technical-specialized guide algorithm to technical terminology. The guide unit can also apply a business-specialized guide algorithm to business terminology. In this way, by applying a guide algorithm according to the category of terminology, the accuracy of the guide is improved. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guide unit can input medical terminology into a generation AI, which then applies a medical-specialized guide algorithm to provide a guide.

[0059] When providing guidance, the guide unit can improve the accuracy of the guidance by referring to the user's past guidance results. For example, the guide unit references the user's past guidance results and improves the accuracy of the guidance based on the results. For example, the guide unit references guidance the user has received in the past to improve the accuracy of the guidance for the same term. The guide unit can also learn frequently used expressions from the user's past guidance history and reflect them in the guidance. The guide unit can also analyze the user's past guidance history to maintain the consistency of the guidance. In this way, by referring to the past guidance results, the consistency and accuracy of the guidance are improved. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guide unit can input the user's past guidance result data into the generation AI, and the generation AI can improve the accuracy of the guidance based on the results.

[0060] When providing a guide, the guide unit can determine the priority of the guide based on the submission time of the technical term. The guide unit, for example, measures the submission time of the technical term and determines the priority of the guide based on that time. For example, the guide unit prioritizes the most recently submitted technical term. The guide unit can also postpone the submission time of technical term. The guide unit can also dynamically adjust the priority of the guide based on the submission time. This enables efficient guidance by determining the priority of the guide based on the submission time. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input data on the submission time of the technical term into the generation AI, and the generation AI can determine the priority of the guide based on that time.

[0061] When providing a guide, the guide unit can adjust the order of the guide based on the relevance of the technical terms. The guide unit, for example, measures the relevance of the technical terms and adjusts the order of the guide based on the relevance. For example, the guide unit prioritizes providing guidance on highly relevant technical terms. The guide unit can also postpone providing less relevant technical terms. The guide unit can also dynamically adjust the order of the guide based on the relevance of the technical terms. This enables efficient guidance by adjusting the order of the guide based on the relevance of the technical terms. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input relevance data of technical terms into the generation AI, and the generation AI can adjust the order of the guide based on the relevance.

[0062] When providing a guide, the guide unit can adjust the use of technical terms in the guide according to the user's level of expertise. The guide unit, for example, evaluates the user's level of expertise and adjusts the use of technical terms in the guide according to that level. For example, the guide unit uses detailed technical terms for users with a high level of expertise. The guide unit can also use simple technical terms for users with a low level of expertise. The guide unit can also dynamically adjust the use of technical terms in the guide according to the user's level of expertise. This enables more appropriate guidance by providing a guide according to the user's level of expertise. Some or all of the above-described processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the guide according to that level.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The translation unit can refer to the user's past translation history and learn the user's preferences and tendencies for specific technical terms. For example, the translation style and expressions selected by the user in the past can be analyzed and reused in similar situations. The translation unit can also prioritize translation of technical terms frequently used by the user. Furthermore, the translation unit can improve the translation accuracy of specific technical terms based on the user's past translation history. This allows the translation to be customized to meet the user's individual needs.

[0065] The explanation unit can estimate the user's level of expertise and adjust the level of detail of the explanation based on the estimated level of expertise. For example, a user with a high level of expertise can be provided with an explanation that includes detailed technical background and related theories. A user with a low level of expertise can be provided with basic concepts and concise explanations. Furthermore, the explanation unit can dynamically adjust the difficulty of the explanation according to the user's level of expertise. This allows the user to receive the most appropriate explanation based on their level of understanding.

[0066] The guide unit can refer to the user's past search history and automatically suggest related technical terms and topics. For example, it can list related terms and topics based on terms the user has searched for in the past and present them to the user. The guide unit can also analyze the user's search history and suggest new topics that the user may be interested in. Furthermore, the guide unit can create an individual learning plan based on the user's search history. This allows the user to receive a customized guide tailored to their interests and needs.

[0067] The translation unit can take into account the user's cultural background when translating technical terms. For example, it can prioritize expressions and phrases commonly used in a particular culture. It can also select appropriate expressions for culturally sensitive content. Furthermore, the translation unit can adjust the tone and style of the translation based on the user's cultural background. This allows for appropriate translations that take cultural background into consideration.

[0068] During interpretation, the interpretation unit can determine the priority of interpretation based on the frequency of use of technical terms. For example, frequently used technical terms can be interpreted first. Less frequently used technical terms can also be left for later interpretation. Furthermore, the interpretation unit can translate frequently used technical terms first and present them to the user. This allows for efficient interpretation by prioritizing the interpretation of frequently used technical terms.

[0069] The guide unit can estimate the user's level of expertise and customize the content of the guide based on the estimated level of expertise. For example, a user with a high level of expertise can be provided with a guide that includes detailed technical background and related theories. A user with a low level of expertise can be provided with basic concepts and concise explanations. Furthermore, the guide unit can dynamically adjust the difficulty level of the guide according to the user's level of expertise. This allows the user to receive the optimal guide according to their level of understanding.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The translation department translates technical terms into other languages. For example, it can translate technical terms in fields such as medicine, law, technology, and business. It can also use generative AI to translate technical terms. Step 2: The explanation unit provides context-sensitive explanations based on the technical terms translated by the translation unit. For example, it can analyze sentences or paragraphs containing technical terms and provide context-sensitive explanations. It can also use generative AI to provide context-sensitive explanations of technical terms. Step 3: The interpretation department provides real-time interpretation based on the explanations provided by the explanation department. For example, real-time interpretation can be performed in meetings that contain a lot of technical terms. Real-time interpretation can also be performed using generative AI. Step 4: The learning unit learns technical terms based on the interpretation results provided by the interpretation unit. For example, users can improve the interpretation of technical terms by teaching new technical terms and their definitions. Technical terminology can also be learned using generative AI. Step 5: The guide unit provides a terminology guide for users based on the terminology learned by the learning unit. For example, it can provide a glossary for each field, the meaning of terminology, and how to use it. It can also provide a terminology guide using generative AI.

[0072] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to provide comprehensive translation, explanation, interpretation, learning, and guidance for technical terms. This system translates technical terms into other languages, provides context-sensitive explanations, performs real-time interpretation, learns technical terms, and provides a technical terminology guide for users. This reduces the time required to understand content due to the increasing complexity of internal systems and improves work efficiency. For example, the system can translate technical terms in fields such as medicine, law, technology, and business into other languages. The system can also analyze sentences and paragraphs containing technical terms and provide context-sensitive explanations. Furthermore, the system can provide real-time interpretation for meetings and other events that contain a large amount of technical terminology. The system can improve its interpretation of technical terms by allowing users to learn new technical terms and their definitions. Finally, the system can provide a glossary for each field, as well as the meanings and usage of technical terms. This helps users understand specialized knowledge and improves work efficiency.

[0073] A technical terminology translation system according to an embodiment includes a translation unit, an explanation unit, an interpretation unit, a learning unit, and a guide unit. The translation unit translates technical terms into other languages. For example, the translation unit can translate technical terms in fields such as medicine, law, technology, and business into other languages. The translation unit can also translate technical terms using a generation AI. For example, the generation AI translates terms in a specific technical field into an appropriate language. The explanation unit provides context-appropriate explanations based on the technical terms translated by the translation unit. For example, the explanation unit can analyze sentences or paragraphs containing technical terms and provide context-appropriate explanations. The explanation unit can also use the generation AI to provide context-appropriate explanations of the technical terms. For example, the generation AI can analyze sentences or paragraphs containing technical terms, explain the terms, and provide related information and background information. The interpretation unit provides real-time interpretation based on the explanations provided by the explanation unit. For example, the interpretation unit can provide real-time interpretation for meetings containing a large amount of technical terms. The interpretation unit can also use the generation AI to provide real-time interpretation. For example, the generation AI can interpret technical terms in real time and provide appropriate translations in situations such as meetings containing a large amount of technical terminology. The learning unit learns technical terms based on the interpretation results provided by the interpretation unit. For example, the learning unit can improve the interpretation of technical terms by having a user teach the learning unit new technical terms and their definitions. The learning unit can also use the generation AI to learn technical terms. For example, the generation AI can improve the interpretation of technical terms by having a user teach the learning unit new technical terms and their definitions. The guide unit provides a technical terminology guide for users based on the technical terms learned by the learning unit. For example, the guide unit can provide a glossary for each field, as well as the meanings and usages of technical terms. The guide unit can also provide a technical terminology guide using the generation AI. For example, the generation AI can provide a guide on technical terms in a specific field, allowing users to easily search for glossaries for each field, as well as the meanings and usages of technical terms. This allows the technical terminology translation system according to the embodiment to help users understand specialized knowledge.

[0074] The translation unit can translate technical terms in the medical, legal, technical, and business fields into other languages. The medical, legal, technical, and business fields include, but are not limited to, medical specialties, laws and precedents, technical specifications, and business terms. The translation unit can, for example, translate technical terms in the medical field into other languages. The translation unit can also translate technical terms in the legal field into other languages. The translation unit can also translate technical terms in the technical field into other languages. The translation unit can also translate technical terms in the business field into other languages. This enables the translation of technical terms specialized in specific fields. Some or all of the above-described processing in the translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the translation unit can input technical terms in the medical field into a generation AI, which can then translate the terms into an appropriate language.

[0075] The explanation unit can analyze sentences and texts containing technical terms and provide explanations appropriate to the context. The explanation unit, for example, uses natural language processing technology to analyze sentences and texts containing technical terms. For example, the explanation unit performs grammatical analysis and semantic analysis to provide explanations appropriate to the context. The explanation unit can also use a generation AI to provide explanations appropriate to the context of technical terms. For example, the generation AI analyzes sentences and texts containing technical terms, explains the terms, and provides related information and background. This provides explanations appropriate to the context, thereby deepening understanding of the technical terms. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the explanation unit can input sentences and texts containing technical terms into a generation AI, which can explain the terms and provide related information and background.

[0076] The interpretation unit can provide real-time interpretation for conferences and other events that contain a lot of technical terminology. The interpretation unit provides real-time interpretation for conferences and other events that contain a lot of technical terminology. For example, the interpretation unit uses real-time interpretation technology to translate technical terminology into an appropriate language. The interpretation unit can also provide real-time interpretation using a generation AI. For example, the generation AI interprets technical terminology in real time for conferences and other events that contain a lot of technical terminology and provides an appropriate translation. This provides real-time interpretation to support understanding of technical terminology in conferences and other events. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, the generation AI. For example, the interpretation unit can input audio data of a conference that contains a lot of technical terminology into the generation AI, and the generation AI can provide interpretation in real time.

[0077] The learning unit can improve the interpretation of technical terms by teaching new technical terms and their definitions to the learning unit. For example, the user inputs new technical terms and their definitions to the learning unit through an interface. For example, the learning unit provides a dedicated interface for the user to input new technical terms and their definitions. The learning unit can also use a generation AI to learn technical terms. For example, the user teaches the generation AI new technical terms and their definitions, and the generation AI uses the information to improve the interpretation of technical terms. This allows the accuracy of the interpretation of technical terms to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI. For example, the learning unit can input new technical terms and their definitions input by the user to the generation AI, and the generation AI can learn the information to improve the interpretation of technical terms.

[0078] The guide unit can provide a glossary for each field, and the meanings and usages of technical terms. For example, the guide unit can provide glossaries for each field, such as medical, legal, technical, and business glossaries. The guide unit can also use a generation AI to provide a terminology guide. For example, the generation AI can provide a guide on technical terms for a specific field, allowing users to easily search for glossaries for each field, the meanings and usages of technical terms, etc. This allows users to easily search for and understand technical terms. Some or all of the above-described processing in the guide unit can be performed using, or without, the generation AI. For example, the guide unit can input glossaries for each field, the meanings and usages of technical terms, and the usages of technical terms into the generation AI, and the generation AI can provide a guide based on that information.

[0079] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. The translation unit, for example, estimates the user's emotions using emotion analysis technology. For example, the translation unit analyzes the user's facial expressions and voice data to estimate emotions. The translation unit can also adjust the translation expression based on the user's emotions using a generation AI. For example, if the user is stressed, the generation AI can provide a concise and easy-to-understand translation. If the user is relaxed, the generation AI can provide a detailed translation and include background information for technical terms. If the user is in a hurry, the generation AI can provide a short translation that focuses on the main points. This enables more appropriate translation by providing a translation that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the translation unit can input the user's emotional data into the generation AI, which can then adjust the way the translation is expressed based on that emotion.

[0080] During translation, the translation unit can determine translation priorities based on the frequency of use of technical terms. For example, the translation unit measures the frequency of use of technical terms and determines translation priorities based on that frequency. For example, the translation unit prioritizes translation of frequently used technical terms. The translation unit can also postpone translation of less frequently used technical terms. The translation unit can also translate frequently used technical terms first and present the translated version to the user. This enables efficient translation by prioritizing translation of frequently used technical terms. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input data on the frequency of use of technical terms into the generation AI, and the generation AI can determine translation priorities based on the frequency.

[0081] The translation unit can apply different translation algorithms to different fields of terminology during translation. For example, the translation unit applies a medical translation algorithm to medical terminology. For example, the translation unit performs translation using an algorithm specialized for medical terminology. The translation unit can also apply a legal translation algorithm to legal terminology. The translation unit can also apply a technical translation algorithm to technical terminology. The translation unit can also apply a business translation algorithm to business terminology. This improves translation accuracy by applying the optimal translation algorithm for each field. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input medical terminology into a generation AI, which then applies a medical translation algorithm to perform translation.

[0082] The translation unit can improve the accuracy of translation by referring to the user's past translation history during translation. The translation unit, for example, refers to the user's past translation history and improves the accuracy of the translation based on that history. For example, the translation unit can refer to technical terms that the user has translated in the past to improve the translation accuracy of the same terms. The translation unit can also learn frequently used expressions from the user's past translation history and reflect them in the translation. The translation unit can also analyze the user's past translation history to maintain consistency in the translation. In this way, by referring to the past translation history, the consistency and accuracy of the translation are improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input the user's past translation history data into the generation AI, and the generation AI can improve the accuracy of the translation based on that history.

[0083] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. The translation unit, for example, estimates the user's emotions using emotion analysis technology. For example, the translation unit analyzes the user's facial expressions and voice data to estimate the emotions. The translation unit can also adjust the length of the translation based on the user's emotions using a generation AI. For example, if the user is stressed, the generation AI can provide a short, concise translation. If the user is relaxed, the generation AI can provide a detailed translation. If the user is in a hurry, the generation AI can provide a concise translation. This enables more appropriate translation by providing a translation length that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input the user's emotional data into the generation AI, which can then adjust the length of the translation based on that emotion.

[0084] During translation, the translation unit can determine the priority of translation based on the submission date of technical terms. The translation unit, for example, measures the submission date of technical terms and determines the priority of translation based on that date. For example, the translation unit prioritizes translation of recently submitted technical terms. The translation unit can also postpone the translation of technical terms that were submitted earlier. The translation unit can also dynamically adjust the priority of translation based on the submission date. This enables efficient translation by determining the priority of translation based on the submission date. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input data on the submission date of technical terms into the generation AI, and the generation AI can determine the priority of translation based on that date.

[0085] The translation unit can adjust the order of translation based on the relevance of technical terms during translation. The translation unit, for example, measures the relevance of technical terms and adjusts the order of translation based on the relevance. For example, the translation unit prioritizes translating highly relevant technical terms. The translation unit can also postpone translating less relevant technical terms. The translation unit can also dynamically adjust the order of translation based on the relevance of technical terms. This enables efficient translation by adjusting the order of translation based on the relevance of technical terms. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI, for example. For example, the translation unit can input relevance data of technical terms into a generation AI, and the generation AI can adjust the order of translation based on the relevance.

[0086] During translation, the translation unit can adjust the use of technical terms in the translation according to the user's level of expertise. For example, the translation unit evaluates the user's level of expertise and adjusts the use of technical terms in the translation according to that level. For example, the translation unit uses detailed technical terms for users with a high level of expertise. The translation unit can also use simple technical terms for users with a low level of expertise. The translation unit can also dynamically adjust the use of technical terms in the translation according to the user's level of expertise. This enables more appropriate translation by providing a translation according to the user's level of expertise. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the translation according to that level.

[0087] The commentary unit can estimate the user's emotions and adjust the way the commentary is presented based on the estimated user's emotions. The commentary unit, for example, estimates the user's emotions using emotion analysis technology. For example, the commentary unit analyzes the user's facial expressions and voice data to estimate the emotions. The commentary unit can also adjust the way the commentary is presented based on the user's emotions using a generation AI. For example, if the user is stressed, the generation AI can provide a concise and easy-to-understand explanation. If the user is relaxed, the generation AI can provide a detailed explanation and include background information on technical terms. If the user is in a hurry, the generation AI can also provide a short explanation that focuses on the main points. This enables more appropriate explanations to be provided by providing explanations that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the commentary unit can be performed using, for example, a generation AI, or without a generation AI. For example, the commentary unit can input the user's emotional data into the generation AI, which can then adjust the way the commentary is expressed based on that emotion.

[0088] The explanation unit can adjust the level of detail of the explanation based on the importance of the technical term when providing the explanation. The explanation unit, for example, measures the importance of the technical term and adjusts the level of detail of the explanation based on the importance. For example, the explanation unit provides a detailed explanation for technical terminology with high importance. The explanation unit can also provide a concise explanation for technical terminology with low importance. The explanation unit can also dynamically adjust the level of detail of the explanation based on the importance of the technical term. This enables efficient explanation by adjusting the level of detail of the explanation based on the importance of the technical term. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, a generation AI. For example, the explanation unit can input importance data of technical terms into the generation AI, and the generation AI can adjust the level of detail of the explanation based on the importance.

[0089] The explanation unit can apply different explanation algorithms depending on the category of the technical term when providing explanations. For example, the explanation unit applies a medical-specialized explanation algorithm to technical terminology in the medical field. For example, the explanation unit provides explanations using an algorithm specialized for medical terminology. The explanation unit can also apply a legal-specialized explanation algorithm to technical terminology in the legal field. The explanation unit can also apply a technical-specialized explanation algorithm to technical terminology in the technical field. The explanation unit can also apply a business-specialized explanation algorithm to technical terminology in the business field. In this way, by applying an explanation algorithm according to the category of the technical terminology, the accuracy of the explanations is improved. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the explanation unit can input technical terminology in the medical field into a generation AI, which can then provide explanations by applying a medical-specialized explanation algorithm.

[0090] When providing an explanation, the explanation unit can improve the accuracy of the explanation by referring to the user's past explanation results. For example, the explanation unit refers to the user's past explanation results and improves the accuracy of the explanation based on the results. For example, the explanation unit improves the accuracy of the explanation of the same term by referring to explanations the user has received in the past. The explanation unit can also learn frequently used expressions from the user's past explanation history and reflect them in the explanation. The explanation unit can also analyze the user's past explanation history to maintain consistency in the explanation. In this way, by referring to the past explanation results, the consistency and accuracy of the explanation are improved. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the explanation unit can input the user's past explanation result data into the generation AI, and the generation AI can improve the accuracy of the explanation based on the results.

[0091] The commentary unit can estimate the user's emotions and adjust the length of the commentary based on the estimated user emotions. The commentary unit, for example, estimates the user's emotions using emotion analysis technology. For example, the commentary unit analyzes the user's facial expressions and voice data to estimate the emotions. The commentary unit can also adjust the length of the commentary based on the user's emotions using a generation AI. For example, if the user is stressed, the generation AI can provide a short, concise commentary. If the user is relaxed, the generation AI can provide a detailed commentary. If the user is in a hurry, the generation AI can provide a concise commentary. This allows for a more appropriate commentary by providing a length of commentary that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the commentary unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the commentary unit can input the user's emotional data into the generation AI, which can then adjust the length of the commentary based on that emotion.

[0092] The explanation unit can determine the priority of the explanation based on the submission date of the technical term when providing the explanation. The explanation unit, for example, measures the submission date of the technical term and determines the priority of the explanation based on that date. For example, the explanation unit prioritizes explanation of recently submitted technical term. The explanation unit can also postpone technical terminology submitted earlier. The explanation unit can also dynamically adjust the priority of the explanation based on the submission date. This enables efficient explanation by determining the priority of the explanation based on the submission date. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, a generation AI. For example, the explanation unit can input data on the submission date of the technical term into the generation AI, and the generation AI can determine the priority of the explanation based on that date.

[0093] The explanation unit can adjust the order of explanations based on the relevance of technical terms during explanation. The explanation unit, for example, measures the relevance of technical terms and adjusts the order of explanations based on the relevance. For example, the explanation unit prioritizes explanations of highly relevant technical terms. The explanation unit can also postpone explanations of less relevant technical terms. The explanation unit can also dynamically adjust the order of explanations based on the relevance of technical terms. This enables efficient explanations by adjusting the order of explanations based on the relevance of technical terms. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, a generation AI. For example, the explanation unit can input relevance data of technical terms into a generation AI, and the generation AI can adjust the order of explanations based on the relevance.

[0094] The commentary unit can adjust the use of technical terms in the commentary according to the user's level of expertise during commentary. For example, the commentary unit evaluates the user's level of expertise and adjusts the use of technical terms in the commentary according to that level. For example, the commentary unit uses detailed technical terms for users with a high level of expertise. The commentary unit can also use simple technical terms for users with a low level of expertise. The commentary unit can also dynamically adjust the use of technical terms in the commentary according to the user's level of expertise. This enables more appropriate commentary by providing commentary according to the user's level of expertise. Some or all of the above-described processing in the commentary unit may be performed using, or without, a generation AI. For example, the commentary unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the commentary according to that level.

[0095] The interpretation unit can estimate the user's emotions and adjust the interpretation's expression based on the estimated user's emotions. The interpretation unit, for example, estimates the user's emotions using emotion analysis technology. For example, the interpretation unit analyzes the user's facial expressions and voice data to estimate emotions. The interpretation unit can also adjust the interpretation's expression based on the user's emotions using a generation AI. For example, if the user is stressed, the generation AI can provide a concise and easy-to-understand interpretation. If the user is relaxed, the generation AI can provide a detailed interpretation that includes background information on technical terms. If the user is in a hurry, the generation AI can provide a short interpretation that focuses on the main points. This enables more appropriate interpretation by providing an interpretation that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the interpretation unit may input user emotion data into the generation AI, and the generation AI may adjust the interpretation's expression method based on the emotion.

[0096] During interpretation, the interpretation unit can determine the priority of interpretation based on the frequency of use of technical terms. For example, the interpretation unit measures the frequency of use of technical terms and determines the priority of interpretation based on that frequency. For example, the interpretation unit prioritizes interpretation of frequently used technical terms. The interpretation unit can also postpone interpretation of less frequently used technical terms. The interpretation unit can also translate frequently used technical terms first and present the translated terms to the user. This enables efficient interpretation by prioritizing interpretation of frequently used technical terms. Some or all of the above-described processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input data on the frequency of use of technical terms into the generation AI, and the generation AI can determine the priority of interpretation based on the frequency.

[0097] The interpretation unit can apply different interpretation algorithms to different fields of terminology during interpretation. For example, the interpretation unit applies a medical-specific interpretation algorithm to medical terminology. For example, the interpretation unit performs interpretation using an algorithm specialized for medical terminology. The interpretation unit can also apply a legal-specific interpretation algorithm to legal terminology. The interpretation unit can also apply a technical-specific interpretation algorithm to technical terminology. The interpretation unit can also apply a business-specific interpretation algorithm to business terminology. This improves the accuracy of interpretation by applying the optimal interpretation algorithm for each field. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interpretation unit can input medical terminology into a generation AI, which then applies a medical-specific interpretation algorithm to perform interpretation.

[0098] The interpretation unit can improve the accuracy of interpretation by referring to the user's past interpretation history during interpretation. For example, the interpretation unit refers to the user's past interpretation history and improves the accuracy of interpretation based on that history. For example, the interpretation unit refers to technical terms that the user has previously interpreted to improve the accuracy of interpretation of the same terms. The interpretation unit can also learn frequently used expressions from the user's past interpretation history and reflect them in the interpretation. The interpretation unit can also analyze the user's past interpretation history to maintain consistency in the interpretation. In this way, by referring to the past interpretation history, the consistency and accuracy of the interpretation are improved. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interpretation unit can input the user's past interpretation history data into the generation AI, and the generation AI can improve the accuracy of the interpretation based on that history.

[0099] The interpretation unit can estimate the user's emotions and adjust the length of the interpretation based on the estimated user emotions. The interpretation unit, for example, estimates the user's emotions using emotion analysis technology. For example, the interpretation unit analyzes the user's facial expressions and voice data to estimate the emotions. The interpretation unit can also adjust the length of the interpretation based on the user's emotions using a generation AI. For example, the generation AI can provide a short, concise interpretation when the user is stressed. The generation AI can provide a detailed interpretation when the user is relaxed. The generation AI can also provide a concise interpretation when the user is in a hurry. This enables more appropriate interpretation by providing the length of the interpretation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the interpretation unit can input the user's emotional data into the generation AI, which can then adjust the length of the interpretation based on that emotion.

[0100] During interpretation, the interpretation unit can determine the priority of interpretation based on the submission date of technical terms. For example, the interpretation unit measures the submission date of technical terms and determines the priority of interpretation based on that date. For example, the interpretation unit prioritizes interpretation of recently submitted technical terms. The interpretation unit can also postpone the interpretation of technical terms that were submitted earlier. The interpretation unit can also dynamically adjust the priority of interpretation based on the submission date. This enables efficient interpretation by determining the priority of interpretation based on the submission date. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input data on the submission date of technical terms into the generation AI, and the generation AI can determine the priority of interpretation based on that date.

[0101] The interpretation unit can adjust the order of interpretation based on the relevance of technical terms during interpretation. The interpretation unit, for example, measures the relevance of technical terms and adjusts the order of interpretation based on the relevance. For example, the interpretation unit prioritizes interpretation of highly relevant technical terms. The interpretation unit can also postpone interpretation of less relevant technical terms. The interpretation unit can also dynamically adjust the order of interpretation based on the relevance of technical terms. This enables efficient interpretation by adjusting the order of interpretation based on the relevance of technical terms. Some or all of the above-mentioned processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input relevance data of technical terms into a generation AI, and the generation AI can adjust the order of interpretation based on the relevance.

[0102] During interpretation, the interpretation unit can adjust the use of technical terms in the interpretation according to the user's level of expertise. For example, the interpretation unit evaluates the user's level of expertise and adjusts the use of technical terms in the interpretation according to that level. For example, the interpretation unit uses detailed technical terms for users with a high level of expertise. The interpretation unit can also use simple technical terms for users with a low level of expertise. The interpretation unit can also dynamically adjust the use of technical terms in the interpretation according to the user's level of expertise. This enables more appropriate interpretation by providing an interpretation according to the user's level of expertise. Some or all of the above-described processing in the interpretation unit may be performed using, or without, a generation AI. For example, the interpretation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the interpretation according to that level.

[0103] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions using emotion analysis technology. For example, the learning unit analyzes the user's facial expressions and voice data to estimate emotions. The learning unit can also select training data based on the user's emotions using a generation AI. For example, if the user is stressed, the generation AI selects simple and easy-to-understand training data. If the user is relaxed, the generation AI can select detailed and deep training data. If the user is in a hurry, the generation AI can select short training data that covers the main points. This enables more appropriate learning by providing training data that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, the generation AI, or without the generation AI. For example, the learning unit can input the user's emotional data into the generation AI, and the generation AI can select learning data based on that emotion.

[0104] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, refers to past learning data and optimizes the learning algorithm based on that data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also extract effective learning patterns from the past learning data and reflect them in the algorithm. The learning unit can also analyze past learning data and improve the accuracy of the learning algorithm. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into the generation AI, and the generation AI can optimize the learning algorithm based on that data.

[0105] The learning unit can update the learning data by reflecting user feedback during learning. The learning unit, for example, collects user feedback and updates the learning data based on that feedback. For example, the learning unit updates the learning data based on user feedback. The learning unit can also fill in deficiencies in the learning data from user feedback. The learning unit can also analyze user feedback and improve the quality of the learning data. In this way, the quality of the learning data is improved by reflecting user feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input user feedback data into the generation AI, and the generation AI can update the learning data based on that feedback.

[0106] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions using emotion analysis technology. For example, the learning unit analyzes the user's facial expressions and voice data to estimate the emotions. The learning unit can also adjust the frequency of learning based on the user's emotions using a generation AI. For example, the generation AI can reduce the frequency of learning when the user is stressed. The generation AI can also increase the frequency of learning when the user is relaxed. The generation AI can also adjust the frequency of learning when the user is in a hurry. This enables more appropriate learning by providing a learning frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI, or without the generation AI. For example, the learning unit can input the user's emotional data into the generation AI, and the generation AI can adjust the frequency of learning based on the emotion.

[0107] During learning, the learning unit can weight the learning data based on the submission time of the technical term. For example, the learning unit measures the submission time of the technical term and weights the learning data based on that time. For example, the learning unit weights recently submitted technical terminology. The learning unit can also lightly weight older submitted technical terminology. The learning unit can also dynamically adjust the weighting of the learning data based on the submission time. This enables efficient learning by weighting the learning data based on the submission time. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input submission time data of technical terminology into the generation AI, and the generation AI can weight the learning data based on that time.

[0108] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit collects information from different data sources and integrates the information to enrich the training data. For example, the learning unit analyzes information from different data sources to improve the quality of the training data. The learning unit can also fill in deficiencies in the training data based on information from different data sources. In this way, the quality of the training data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input information from different data sources into the generation AI, which then integrates the information to enrich the training data.

[0109] The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated user's emotions. The guide unit, for example, estimates the user's emotions using emotion analysis technology. For example, the guide unit analyzes the user's facial expressions and voice data to estimate the emotions. The guide unit can also adjust the way the guide is presented based on the user's emotions using a generation AI. For example, if the user is feeling stressed, the generation AI can provide a concise and easy-to-understand guide. If the user is relaxed, the generation AI can provide a detailed guide that also includes background information on technical terms. If the user is in a hurry, the generation AI can provide a short guide that focuses on the main points. This enables more appropriate guidance by providing guidance that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the guide unit can be performed using, for example, the generation AI, or without the generation AI. For example, the guide unit can input the user's emotional data into the generation AI, which can then adjust the way the guide is expressed based on that emotion.

[0110] When providing a guide, the guide unit can adjust the level of detail of the guide based on the importance of the technical term. For example, the guide unit measures the importance of the technical term and adjusts the level of detail of the guide based on the importance. For example, the guide unit provides a detailed guide for technical terminology with high importance. The guide unit can also provide a concise guide for technical terminology with low importance. The guide unit can also dynamically adjust the level of detail of the guide based on the importance of the technical term. This enables efficient guidance by adjusting the level of detail of the guide based on the importance of the technical term. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input importance data of technical terminology to the generation AI, and the generation AI can adjust the level of detail of the guide based on the importance.

[0111] When providing a guide, the guide unit can apply different guide algorithms depending on the category of the terminology. For example, the guide unit applies a medical-specialized guide algorithm to medical terminology. For example, the guide unit provides a guide using an algorithm specialized for medical terminology. The guide unit can also apply a legal-specialized guide algorithm to legal terminology. The guide unit can also apply a technical-specialized guide algorithm to technical terminology. The guide unit can also apply a business-specialized guide algorithm to business terminology. In this way, by applying a guide algorithm according to the category of terminology, the accuracy of the guide is improved. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guide unit can input medical terminology into a generation AI, which then applies a medical-specialized guide algorithm to provide a guide.

[0112] When providing guidance, the guide unit can improve the accuracy of the guidance by referring to the user's past guidance results. For example, the guide unit references the user's past guidance results and improves the accuracy of the guidance based on the results. For example, the guide unit references guidance the user has received in the past to improve the accuracy of the guidance for the same term. The guide unit can also learn frequently used expressions from the user's past guidance history and reflect them in the guidance. The guide unit can also analyze the user's past guidance history to maintain the consistency of the guidance. In this way, by referring to the past guidance results, the consistency and accuracy of the guidance are improved. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guide unit can input the user's past guidance result data into the generation AI, and the generation AI can improve the accuracy of the guidance based on the results.

[0113] The guide unit can estimate the user's emotions and adjust the length of the guidance based on the estimated user emotions. The guide unit, for example, estimates the user's emotions using emotion analysis technology. For example, the guide unit analyzes the user's facial expressions and voice data to estimate the emotions. The guide unit can also adjust the length of the guidance based on the user's emotions using a generation AI. For example, if the user is stressed, the generation AI can provide a short, concise guide. If the user is relaxed, the generation AI can provide a detailed guide. If the user is in a hurry, the generation AI can provide a concise guide. This enables more appropriate guidance by providing a length of guidance according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the guide unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the user's emotion data into the generation AI, and the generation AI can adjust the length of the guide based on the emotion.

[0114] When providing a guide, the guide unit can determine the priority of the guide based on the submission time of the technical term. The guide unit, for example, measures the submission time of the technical term and determines the priority of the guide based on that time. For example, the guide unit prioritizes the most recently submitted technical term. The guide unit can also postpone the submission time of technical term. The guide unit can also dynamically adjust the priority of the guide based on the submission time. This enables efficient guidance by determining the priority of the guide based on the submission time. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input data on the submission time of the technical term into the generation AI, and the generation AI can determine the priority of the guide based on that time.

[0115] When providing a guide, the guide unit can adjust the order of the guide based on the relevance of the technical terms. The guide unit, for example, measures the relevance of the technical terms and adjusts the order of the guide based on the relevance. For example, the guide unit prioritizes providing guidance on highly relevant technical terms. The guide unit can also postpone providing less relevant technical terms. The guide unit can also dynamically adjust the order of the guide based on the relevance of the technical terms. This enables efficient guidance by adjusting the order of the guide based on the relevance of the technical terms. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input relevance data of technical terms into the generation AI, and the generation AI can adjust the order of the guide based on the relevance.

[0116] When providing a guide, the guide unit can adjust the use of technical terms in the guide according to the user's level of expertise. The guide unit, for example, evaluates the user's level of expertise and adjusts the use of technical terms in the guide according to that level. For example, the guide unit uses detailed technical terms for users with a high level of expertise. The guide unit can also use simple technical terms for users with a low level of expertise. The guide unit can also dynamically adjust the use of technical terms in the guide according to the user's level of expertise. This enables more appropriate guidance by providing a guide according to the user's level of expertise. Some or all of the above-described processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the guide according to that level. === Hard Collateral 1-1 === Each of the multiple elements, including the translation unit, commentary unit, interpretation unit, learning unit, and guide unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the translation unit is realized by the control unit 46A of the smart device 14 and translates technical terms into other languages. The commentary unit is realized by the specific processing unit 290 of the data processing device 12 and provides context-appropriate explanations based on the translated technical terms. The interpretation unit is realized by the control unit 46A of the smart device 14 and performs real-time interpretation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns technical terms. The guide unit is realized by the control unit 46A of the smart device 14 and provides a technical terminology guide for the user. === Hard Collateral 1-2 === Each of the multiple elements, including the translation unit, explanation unit, interpretation unit, learning unit, and guide unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the translation unit is realized by the control unit 46A of the smart glasses 214 and translates technical terms into other languages. The explanation unit is realized by the specific processing unit 290 of the data processing device 12 and provides context-appropriate explanations based on the translated technical terms. The interpretation unit is realized by the control unit 46A of the smart glasses 214 and performs real-time interpretation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns technical terms. The guide unit is realized by the control unit 46A of the smart glasses 214 and provides a technical terminology guide for the user. === Hard Collateral 1-3 === Each of the multiple elements including the translation unit, explanation unit, interpretation unit, learning unit, and guide unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the translation unit is realized by the control unit 46A of the headset type terminal 314 and translates technical terms into other languages. The explanation unit is realized by the specific processing unit 290 of the data processing device 12 and provides context-appropriate explanations based on the translated technical terms. The interpretation unit is realized by the control unit 46A of the headset type terminal 314 and performs real-time interpretation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns technical terms. The guide unit is realized by the control unit 46A of the headset type terminal 314 and provides a technical terminology guide for the user. === Hard Collateral 1-4 === Each of the multiple elements including the translation unit, explanation unit, interpretation unit, learning unit, and guide unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the translation unit is realized by the control unit 46A of the robot 414 and translates technical terms into other languages. The explanation unit is realized by the specific processing unit 290 of the data processing device 12 and provides context-appropriate explanations based on the translated technical terms. The interpretation unit is realized by the control unit 46A of the robot 414 and performs real-time interpretation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns technical terms. The guide unit is realized by the control unit 46A of the robot 414 and provides a technical terminology guide for the user.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The translation unit can refer to the user's past translation history and learn the user's preferences and tendencies for specific technical terms. For example, the translation style and expressions selected by the user in the past can be analyzed and reused in similar situations. The translation unit can also prioritize translation of technical terms frequently used by the user. Furthermore, the translation unit can improve the translation accuracy of specific technical terms based on the user's past translation history. This allows the translation to be customized to meet the user's individual needs.

[0119] The explanation unit can estimate the user's level of expertise and adjust the level of detail of the explanation based on the estimated level of expertise. For example, a user with a high level of expertise can be provided with an explanation that includes detailed technical background and related theories. A user with a low level of expertise can be provided with basic concepts and concise explanations. Furthermore, the explanation unit can dynamically adjust the difficulty of the explanation according to the user's level of expertise. This allows the user to receive the most appropriate explanation based on their level of understanding.

[0120] The interpretation unit can estimate the user's emotions and adjust the tone and expressions of the interpretation based on the estimated emotions. For example, if the user is nervous, the interpretation unit can use a calm tone. If the user is relaxed, the interpretation unit can use more casual expressions. Furthermore, if the user is in a hurry, the interpretation unit can provide a concise interpretation that focuses on the main points. In this way, an appropriate interpretation can be provided according to the user's emotions.

[0121] The learning unit can estimate a user's learning style and customize learning content based on the estimated learning style. For example, a user who prefers visual learning can be provided with content that makes extensive use of diagrams and graphs. A user who prefers auditory learning can be provided with content in the form of audio commentary or podcasts. Furthermore, a user who prefers hands-on learning can be provided with interactive quizzes and simulations. This allows for an optimal learning experience tailored to the user's learning style.

[0122] The guide unit can refer to the user's past search history and automatically suggest related technical terms and topics. For example, it can list related terms and topics based on terms the user has searched for in the past and present them to the user. The guide unit can also analyze the user's search history and suggest new topics that the user may be interested in. Furthermore, the guide unit can create an individual learning plan based on the user's search history. This allows the user to receive a customized guide tailored to their interests and needs.

[0123] The translation unit can take into account the user's cultural background when translating technical terms. For example, it can prioritize expressions and phrases commonly used in a particular culture. It can also select appropriate expressions for culturally sensitive content. Furthermore, the translation unit can adjust the tone and style of the translation based on the user's cultural background. This allows for appropriate translations that take cultural background into consideration.

[0124] The commentary unit can estimate the user's emotions and adjust the pace of the commentary based on the estimated emotions. For example, if the user is impatient, the commentary unit can speed up the pace of the commentary. If the user is relaxed, the commentary unit can slow down the pace of the commentary. Furthermore, if the user is confused, the commentary unit can adjust the pace of the commentary and provide a more detailed explanation. In this way, an optimal commentary pace according to the user's emotions is provided.

[0125] During interpretation, the interpretation unit can determine the priority of interpretation based on the frequency of use of technical terms. For example, frequently used technical terms can be interpreted first. Less frequently used technical terms can also be left for later interpretation. Furthermore, the interpretation unit can translate frequently used technical terms first and present them to the user. This allows for efficient interpretation by prioritizing the interpretation of frequently used technical terms.

[0126] The learning unit can estimate the user's emotions and adjust the timing of learning based on the estimated emotions. For example, if the user is feeling stressed, the timing of learning can be delayed. Also, if the user is relaxed, the timing of learning can be advanced. Furthermore, if the user is in a hurry, the timing of learning can be adjusted to provide effective learning in a short amount of time. In this way, the optimal learning timing according to the user's emotions can be provided.

[0127] The guide unit can estimate the user's level of expertise and customize the content of the guide based on the estimated level of expertise. For example, a user with a high level of expertise can be provided with a guide that includes detailed technical background and related theories. A user with a low level of expertise can be provided with basic concepts and concise explanations. Furthermore, the guide unit can dynamically adjust the difficulty level of the guide according to the user's level of expertise. This allows the user to receive the optimal guide according to their level of understanding.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The translation department translates technical terms into other languages. For example, it can translate technical terms in fields such as medicine, law, technology, and business. It can also use generative AI to translate technical terms. Step 2: The explanation unit provides context-sensitive explanations based on the technical terms translated by the translation unit. For example, it can analyze sentences or paragraphs containing technical terms and provide context-sensitive explanations. It can also use generative AI to provide context-sensitive explanations of technical terms. Step 3: The interpretation department provides real-time interpretation based on the explanations provided by the explanation department. For example, real-time interpretation can be performed in meetings that contain a lot of technical terms. Real-time interpretation can also be performed using generative AI. Step 4: The learning unit learns technical terms based on the interpretation results provided by the interpretation unit. For example, users can improve the interpretation of technical terms by teaching new technical terms and their definitions. Technical terminology can also be learned using generative AI. Step 5: The guide unit provides a terminology guide for users based on the terminology learned by the learning unit. For example, it can provide a glossary for each field, the meaning of terminology, and how to use it. It can also provide a terminology guide using generative AI.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0170] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0173] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0174] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0183] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0186] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0191] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0192] 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.

[0193] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0194] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0195] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0196] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0201] [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A translation department that translates technical terms into other languages; an explanation unit that provides context-appropriate explanations based on the technical terms translated by the translation unit; an interpretation unit that performs real-time interpretation based on the commentary provided by the commentary unit; a learning unit that learns technical terms based on the interpretation result provided by the interpretation unit; a guide unit that provides a technical terminology guide for a user based on the technical terms learned by the learning unit; Equipped with A system characterized by:

2. The translation unit Translate medical, legal, technical, and business terminology into other languages 2. The system of claim 1.

3. The commentary section Analyzes sentences and texts containing technical terms and provides context-sensitive explanations 2. The system of claim 1.

4. The interpretation unit: Real-time interpretation for meetings containing a lot of technical terms 2. The system of claim 1.

5. The learning unit Improve terminology interpretation by allowing users to teach new terminology and its definitions 2. The system of claim 1.

6. The guide portion is Provides glossaries for each field, the meanings and usage of technical terms 2. The system of claim 1.

7. The translation unit Estimate the user's emotions and adjust the translation style based on the estimated user emotions.

2. The system of claim 1.

8. The translation unit During translation, prioritize translations based on term frequency 2. The system of claim 1.

Images