system

Abstract

The system according to this embodiment aims to translate technical terms into easily understandable language and support comprehension. [Solution] The system according to the embodiment comprises a translation unit, a generation unit, an integration unit, and an optimization unit. The translation unit stores technical terms in a database, understands the context, and translates them in an easy-to-understand manner. The generation unit automatically creates diagrams and concrete examples to supplement difficult concepts. The integration unit integrates with document management tools and educational platforms. The optimization unit optimizes translation accuracy and content by utilizing user feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that there are many technical terms and the information is too technical to be easily understood.

[0005] The system according to the embodiment aims to translate technical terms in an easy-to-understand manner and assist in understanding.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a translation unit, a generation unit, an integration unit, and an optimization unit. The translation unit stores technical terms in a database and translates them in an accessible manner while understanding the context. The generation unit automatically creates diagrams and concrete examples to supplement difficult concepts. The integration unit integrates with document management tools and educational platforms. The optimization unit optimizes translation accuracy and content by utilizing user feedback. [Effects of the Invention]

[0007] The system according to this embodiment can translate technical terms into easily understandable language, thereby supporting comprehension. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​tool according to an embodiment of the present invention is a system for making information containing a lot of technical jargon and difficult to understand easily understandable to anyone. This system is equipped with a technical jargon database and a translation function, which stores technical jargon from a wide range of fields in the database and has the function of translating it in an easy-to-understand manner while understanding the context. For example, it can translate technical jargon in the medical field into language that is easy for the general public to understand. It is also equipped with an automatic generation function for diagrams and examples, which automatically creates diagrams and concrete examples that supplement difficult concepts and support understanding. For example, complex technical explanations can be made visually easy to understand using diagrams and concrete examples. Furthermore, it is equipped with a function to link with other tools, which simplifies operation by integrating with document management tools and educational platforms. For example, by linking with an educational platform, it can enable students to use the translation of technical jargon and the automatic generation of diagrams when learning. Finally, it is equipped with a personalized learning support function, which optimizes translation accuracy and content by utilizing user feedback. For example, in order to provide translations and diagrams that are easy for users to understand, user feedback can be collected and the AI ​​can learn and optimize it. With this AI tool, information containing a lot of technical jargon and difficult to understand can be made easily understandable to anyone. This saves time and costs, enhances understanding through visual support, lowers knowledge barriers, and allows for efficient acquisition of specialized knowledge. As a result, AI tools can present information that is often jargon-heavy and difficult to understand in a way that is easily accessible to everyone.

[0029] The AI ​​tool according to this embodiment comprises a translation unit, a generation unit, an integration unit, and an optimization unit. The translation unit stores technical terms in a database, understands the context, and translates them in an accessible manner. Technical terms include, but are not limited to, fields such as medicine, technology, and law. For example, the translation unit translates technical terms in the medical field into language that is easy for the general public to understand. The translation unit can understand the context of technical terms and translate them into accessible language using a contextual analysis algorithm. The generation unit automatically creates diagrams and concrete examples that supplement difficult concepts. For example, the generation unit makes complex technical explanations visually easy to understand using diagrams and concrete examples. The generation unit can automatically generate diagrams and concrete examples using a data visualization algorithm. The integration unit integrates with document management tools and educational platforms. For example, the integration unit can integrate with educational platforms to allow students to use translation of technical terms and automatic generation of diagrams when learning. The integration unit can seamlessly integrate with other tools using API integration. The optimization unit optimizes translation accuracy and content by utilizing user feedback. For example, the optimization unit collects user feedback, and the AI ​​learns and optimizes based on that feedback. The optimization unit can optimize translation accuracy and content based on feedback using machine learning algorithms. As a result, the AI ​​tool according to this embodiment can translate specialized terminology, generate charts and graphs, integrate with other tools, and optimize based on feedback.

[0030] The translation department stores specialized terminology in a database and translates it in an accessible way by understanding the context. Specialized terminology includes, but is not limited to, fields such as medicine, technology, and law. For example, the translation department translates medical terminology into language easily understood by the general public. Specifically, in the medical field, for instance, the term "myocardial infarction" might be translated as "a disease in which the blood vessels of the heart become blocked." In the technical field, "algorithm" could be translated as "a procedure for solving a problem." In the legal field, "contract" could be translated as "a document outlining the agreed-upon terms." The translation department uses contextual analysis algorithms to understand the context of specialized terminology and translate it into accessible language. These contextual analysis algorithms analyze the overall flow of a text and the surrounding context to provide an appropriate translation. For example, if the term "myocardial infarction" appears in a medical text, the context is analyzed to provide an appropriate translation. This allows the translation department to provide texts containing specialized terminology in a way that is easily understood by the general public. Furthermore, the translation department can regularly update its database of technical terms to reflect new terminology and the latest information. This ensures that translations are always based on the most up-to-date information.

[0031] The generation unit automatically creates diagrams and concrete examples to supplement complex concepts. For example, it makes complex technical explanations visually easier to understand using diagrams and concrete examples. Specifically, in a technical explanation, for instance, when explaining the "structure of a database," it can automatically generate diagrams showing database tables and relationships. In the medical field, for example, to explain the "mechanism of myocardial infarction," it could generate diagrams showing cross-sections of the heart and diagrams showing blood vessel blockages. The generation unit can automatically generate diagrams and concrete examples using data visualization algorithms. Data visualization algorithms are used to generate appropriate diagrams and concrete examples based on the input data and information. For example, it can take technical data as input and generate graphs and charts to visually represent that data. Furthermore, the generation unit can customize the format and content of diagrams and concrete examples according to the user's needs and requirements. This allows the generation unit to provide information in a way that is easy for users to understand. In addition, the generation unit can automatically insert the generated diagrams and concrete examples into documents to complement the overall explanation. This allows users to easily create documents that include visual information.

[0032] The Integration Unit integrates with document management tools and educational platforms. For example, it can integrate with educational platforms to enable students to use translation of technical terms and automatic generation of diagrams and charts when learning. Specifically, if the learning materials used by students on the educational platform contain technical terms, the Integration Unit will automatically translate those terms and present them in a way that is easy for students to understand. Furthermore, for difficult concepts in the learning materials, the Generation Unit will insert automatically generated diagrams and charts and concrete examples to make them visually easier to understand. The Integration Unit can seamlessly integrate with other tools using API integration. Through API integration, the Integration Unit can exchange data with document management tools and educational platforms in real time and provide necessary information. For example, if a document created by a user in a document management tool contains technical terms, the Integration Unit can automatically translate those terms and reflect them in the document. This allows users to efficiently create documents using translation of technical terms and automatic generation of diagrams and charts. In addition, the Integration Unit can also integrate with other educational tools and learning management systems to support student learning. This will allow the collaborative department to utilize the translation of specialized terminology and the automatic generation of diagrams and charts in educational settings to deepen students' understanding.

[0033] The optimization unit leverages user feedback to optimize translation accuracy and content. For example, the optimization unit collects user feedback, and the AI ​​learns and optimizes based on that feedback. Specifically, when users provide ratings and comments on translation results, the optimization unit collects this feedback, and the AI ​​learns from that information. Using machine learning algorithms, the system can optimize translation accuracy and content based on feedback. For instance, if a user rates a translation as "difficult to understand," the optimization unit uses that rating to improve the translation algorithm, making future translations clearer. Similarly, if a user rates a translation as "accurate," the optimization unit uses that result as a reference to improve translation accuracy in similar contexts. By continuously collecting user feedback and allowing the AI ​​to learn from that information, the optimization unit can constantly optimize translation accuracy and content. Furthermore, the optimization unit can analyze user feedback to identify common problems and areas for improvement. This allows the optimization unit to take concrete measures to improve the overall performance of the translation algorithm. As a result, the optimization unit can provide highly accurate translations that meet user needs, improving the user experience.

[0034] The translation unit can translate medical terminology into language that is easily understood by the general public. For example, the translation unit can translate medical terminology into everyday expressions. The translation unit can convert medical terminology such as disease names, treatments, and drug names into simple explanations. The translation unit uses contextual analysis algorithms to understand the context of medical terminology and translate it into more accessible language. This makes medical terminology easier to understand. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input medical terminology into a generating AI, which can understand the context and translate it into more accessible language.

[0035] The generation unit can make complex technical explanations visually easy to understand using diagrams and concrete examples. For example, the generation unit can make complex technical explanations visually easy to understand using flowcharts and graphs. The generation unit can explain specialized technical specifications and algorithm details using diagrams and concrete examples. The generation unit automatically generates diagrams and concrete examples using data visualization algorithms. This makes technical explanations visually easy to understand. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a technical explanation into a generation AI, and the generation AI can automatically generate diagrams and concrete examples.

[0036] The integration unit can work with the educational platform to enable students to use translations of technical terms and automatic generation of charts and graphs when learning. For example, the integration unit can work with the educational platform to provide translations of technical terms when students are learning. The integration unit can also work with the educational platform to provide automatic generation of charts and graphs when students are learning. The integration unit seamlessly integrates with the educational platform using API integration. This allows it to work with the educational platform to support learning. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input data obtained from the educational platform into a generating AI, which can then perform translations of technical terms and automatic generation of charts and graphs.

[0037] The optimization unit can collect user feedback, and the AI ​​can learn and optimize it. The optimization unit collects user feedback, for example, through surveys or analysis of usage logs. Based on the collected feedback, the AI ​​learns and optimizes the translation accuracy and content. The optimization unit uses machine learning algorithms to perform optimization based on the feedback. This allows for optimization that leverages user feedback. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user feedback data into a generating AI, which can then learn and perform optimization.

[0038] The translation unit can adjust the level of detail in the translation based on the frequency of use of technical terms. For example, the translation unit may add a concise explanation to frequently used technical terms. It may also add a detailed explanation to less frequently used technical terms. It may translate technical terms of moderate frequency with an appropriate level of detail. This allows for providing a level of translation detail that corresponds to the frequency of use of technical terms. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input technical term frequency data into a generating AI, which can then adjust the level of detail in the translation based on the frequency of use.

[0039] The translation unit can apply different translation algorithms depending on the category of the technical term during translation. For example, the translation unit can apply a translation algorithm specialized for medical terminology to medical terminology. The translation unit can also apply a translation algorithm specialized for technical terminology to technical terminology. The translation unit can apply a translation algorithm specialized for legal terminology to legal terminology. This makes it possible to provide translation algorithms that are appropriate for the category of technical terminology. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input technical terminology category data into a generating AI, and the generating AI can apply different translation algorithms depending on the category.

[0040] The translation department can prioritize translations based on the submission date of technical terms. For example, the department may prioritize translating recently submitted technical terms. It may also postpone the translation of older technical terms. The department dynamically adjusts the translation priority according to the submission date. This allows for the provision of translation priorities based on the submission date of technical terms. Some or all of the above processes in the translation department may be performed using AI, for example, or not using AI. For example, the translation department can input technical term submission date data into a generating AI, which can then determine the translation priority based on the submission date.

[0041] The translation unit can adjust the order of translations based on the relevance of technical terms during the translation process. For example, the translation unit may prioritize the translation of highly relevant technical terms. It may also postpone the translation of less relevant technical terms. The translation unit dynamically adjusts the order of translations according to the relevance of technical terms. This allows for the provision of a translation order that is appropriate to the relevance of technical terms. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input technical term relevance data into a generating AI, which can then adjust the order of translations based on relevance.

[0042] The generation unit can adjust the level of detail in the generated figures, tables, and examples based on the importance of the technical terms. For example, the generation unit can generate detailed figures for highly important technical terms. It can also generate concise figures for less important technical terms. For technical terms of moderate importance, it can generate figures with an appropriate level of detail. This allows for the provision of figures and examples with a level of detail corresponding to the importance of the technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term importance data into a generation AI, which can then adjust the level of detail in the figures and examples based on their importance.

[0043] The generation unit can apply different generation algorithms depending on the category of technical terms when generating diagrams and examples. For example, the generation unit can apply a medical diagram generation algorithm to technical terms in the medical field. The generation unit can also apply a technical diagram generation algorithm to technical terms in the technical field. The generation unit can apply a legal diagram generation algorithm to technical terms in the legal field. This makes it possible to provide generation algorithms that correspond to the category of technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term category data into a generation AI, and the generation AI can apply different generation algorithms depending on the category.

[0044] The generation unit can determine the generation priority based on the submission date of technical terms when generating figures, tables, and specific examples. For example, the generation unit may prioritize generating figures and tables based on recently submitted technical terms. The generation unit may also postpone the generation of figures and tables based on older technical terms. The generation unit dynamically adjusts the generation priority of figures and tables according to the submission date. This allows for the provision of a generation priority that corresponds to the submission date of technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term submission date data into a generation AI, which can then determine the generation priority based on the submission date.

[0045] The generation unit can adjust the generation order of figures, tables, and specific examples based on the relevance of technical terms. For example, the generation unit can prioritize the generation of figures and tables based on highly relevant technical terms. The generation unit can also postpone the generation of figures and tables based on less relevant technical terms. The generation unit dynamically adjusts the generation order of figures and tables according to the relevance of technical terms. This allows for a generation order that corresponds to the relevance of technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term relevance data into a generation AI, which can then adjust the generation order based on the relevance.

[0046] The integration unit can adjust the level of detail of the integration based on the frequency of use of other tools during integration. For example, the integration unit can provide a detailed integration method for frequently used tools. It can also provide a simpler integration method for infrequently used tools. For tools with moderate usage, it can provide an integration method with an appropriate level of detail. This allows for providing an integration level that corresponds to the frequency of use of other tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input usage frequency data of other tools into a generating AI, which can then adjust the level of detail of the integration based on the usage frequency.

[0047] The integration unit can apply different integration algorithms depending on the category of the other tool during integration. For example, the integration unit can apply an educational integration algorithm to an educational platform. It can also apply a document management integration algorithm to a document management tool. It can apply a communication integration algorithm to a communication tool. This allows the integration unit to provide integration algorithms tailored to the category of the other tool. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input category data of the other tool into a generating AI, which can then apply different integration algorithms depending on the category.

[0048] The integration unit can adjust the order of integration based on the submission timing of other tools during integration. For example, the integration unit may prioritize integration with recently submitted tools. The integration unit may also postpone integration with older tools. The integration unit dynamically adjusts the order of integration according to the submission timing. This allows for the provision of an integration order that corresponds to the submission timing of other tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input submission timing data of other tools into a generating AI, which can then adjust the order of integration based on the submission timing.

[0049] The integration unit can adjust the integration method based on the relationships between other tools during integration. For example, the integration unit may prioritize integrating highly relevant tools. It may also postpone integrating less relevant tools. The integration unit dynamically adjusts the integration method according to the relationships between tools. This allows it to provide an integration method that is appropriate to the relationships between other tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input relationship data of other tools into a generating AI, which can then adjust the integration method based on those relationships.

[0050] The optimization unit can adjust the level of detail of the optimization based on the importance of the user's feedback during the optimization process. For example, the optimization unit can perform detailed optimization for highly important feedback. The optimization unit can also perform concise optimization for less important feedback. The optimization unit can perform optimization with an appropriate level of detail for feedback of moderate importance. This allows the optimization unit to provide an level of detail that corresponds to the importance of the user's feedback. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the importance data of the user's feedback into a generating AI, which can then adjust the level of detail of the optimization based on the importance.

[0051] The optimization unit can apply different optimization algorithms depending on the category of user feedback during optimization. For example, the optimization unit can apply an educational optimization algorithm to feedback in the education field. The optimization unit can also apply a technical optimization algorithm to feedback in the technology field. The optimization unit can apply a medical optimization algorithm to feedback in the medical field. This makes it possible to provide an optimization algorithm that corresponds to the category of user feedback. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input user feedback category data into a generating AI, and the generating AI can apply different optimization algorithms depending on the category.

[0052] The optimization unit can adjust the optimization order based on when user feedback is submitted. For example, the optimization unit may prioritize optimizing recently submitted feedback. The optimization unit may also postpone the optimization of older feedback. The optimization unit dynamically adjusts the optimization order according to the submission timing. This allows the optimization order to be provided according to when user feedback is submitted. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user feedback submission timing data into a generating AI, which can then adjust the optimization order based on the submission timing.

[0053] The optimization unit can adjust its optimization method based on the relevance of user feedback during optimization. For example, the optimization unit may prioritize optimizing highly relevant feedback. The optimization unit may also postpone optimizing less relevant feedback. The optimization unit dynamically adjusts its optimization method according to the relevance of the feedback. This allows it to provide an optimization method that is appropriate to the relevance of user feedback. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input relevance data of user feedback into a generating AI, which can then adjust its optimization method based on the relevance.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The translation unit can adjust the level of detail in translations based on the frequency of use of technical terms. For example, frequently used technical terms may be translated with a concise explanation. Less frequently used technical terms may be translated with a detailed explanation. Technical terms of moderate frequency may be translated with an appropriate level of detail. This allows for translation detail levels to be provided according to the frequency of use of technical terms. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input technical term frequency data into a generating AI, which can then adjust the level of detail in translations based on the frequency of use.

[0056] The generation unit can adjust the level of detail in the generated figures, tables, and examples based on the importance of the technical terms. For example, it can generate detailed figures and tables for highly important technical terms, concise figures and tables for less important technical terms, and figures and tables with an appropriate level of detail for moderately important technical terms. This allows for the provision of figures and examples with a level of detail appropriate to the importance of the technical terms. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input technical term importance data into a generation AI, which can then adjust the level of detail in the figures and examples based on their importance.

[0057] The integration unit can adjust the level of detail of the integration based on the frequency of use of other tools during integration. For example, it can provide a detailed integration method for frequently used tools, a simpler integration method for infrequently used tools, and an appropriately detailed integration method for moderately used tools. This allows for providing an integration level that corresponds to the frequency of use of other tools. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input usage frequency data of other tools into a generating AI, which can then adjust the level of detail of the integration based on the usage frequency.

[0058] The optimization unit can adjust the level of detail of the optimization based on the importance of the user's feedback. For example, it can perform detailed optimization for highly important feedback, simple optimization for less important feedback, and optimization with a moderate level of detail for moderately important feedback. This allows for providing an optimization level that corresponds to the importance of the user's feedback. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the importance data of the user's feedback into a generating AI, which can then adjust the level of detail of the optimization based on the importance.

[0059] The translation unit can apply different translation algorithms depending on the category of the technical term during translation. For example, a translation algorithm specialized for medical terminology can be applied to medical terminology. A translation algorithm specialized for technical terminology can also be applied to technical terminology. A translation algorithm specialized for legal terminology can be applied to legal terminology. This allows for the provision of translation algorithms tailored to the category of technical terminology. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input technical term category data into a generating AI, which can then apply different translation algorithms depending on the category.

[0060] The generation unit can apply different generation algorithms depending on the category of technical terms when generating diagrams and examples. For example, a medical diagram generation algorithm can be applied to technical terms in the medical field. A technical diagram generation algorithm can also be applied to technical terms in the technical field. A legal diagram generation algorithm can be applied to technical terms in the legal field. This makes it possible to provide generation algorithms that correspond to the category of technical terms. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input technical term category data into a generation AI, and the generation AI can apply different generation algorithms depending on the category.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The translation department stores technical terms in a database and translates them in an accessible way, understanding the context. Technical terms include, but are not limited to, fields such as medicine, technology, and law. The translation department uses contextual analysis algorithms to understand the context of technical terms and translate them into accessible language. Step 2: The generation unit automatically creates diagrams and concrete examples to supplement complex concepts. The generation unit uses data visualization algorithms to make complex technical explanations visually easy to understand using diagrams and concrete examples. Step 3: The integration unit integrates with document management tools and educational platforms. The integration unit can seamlessly integrate with other tools using API integration. For example, it can integrate with educational platforms to allow students to use translation of specialized terminology and automatic generation of charts and graphs when learning. Step 4: The optimization unit optimizes translation accuracy and content based on user feedback. The optimization unit collects user feedback and uses machine learning algorithms to optimize translation accuracy and content based on that feedback.

[0063] (Example of form 2) The AI ​​tool according to an embodiment of the present invention is a system for making information containing a lot of technical jargon and difficult to understand easily understandable to anyone. This system is equipped with a technical jargon database and a translation function, which stores technical jargon from a wide range of fields in the database and has the function of translating it in an easy-to-understand manner while understanding the context. For example, it can translate technical jargon in the medical field into language that is easy for the general public to understand. It is also equipped with an automatic generation function for diagrams and examples, which automatically creates diagrams and concrete examples that supplement difficult concepts and support understanding. For example, complex technical explanations can be made visually easy to understand using diagrams and concrete examples. Furthermore, it is equipped with a function to link with other tools, which simplifies operation by integrating with document management tools and educational platforms. For example, by linking with an educational platform, it can enable students to use the translation of technical jargon and the automatic generation of diagrams when learning. Finally, it is equipped with a personalized learning support function, which optimizes translation accuracy and content by utilizing user feedback. For example, in order to provide translations and diagrams that are easy for users to understand, user feedback can be collected and the AI ​​can learn and optimize it. With this AI tool, information containing a lot of technical jargon and difficult to understand can be made easily understandable to anyone. This saves time and costs, enhances understanding through visual support, lowers knowledge barriers, and allows for efficient acquisition of specialized knowledge. As a result, AI tools can present information that is often jargon-heavy and difficult to understand in a way that is easily accessible to everyone.

[0064] The AI ​​tool according to this embodiment comprises a translation unit, a generation unit, an integration unit, and an optimization unit. The translation unit stores technical terms in a database, understands the context, and translates them in an accessible manner. Technical terms include, but are not limited to, fields such as medicine, technology, and law. For example, the translation unit translates technical terms in the medical field into language that is easy for the general public to understand. The translation unit can understand the context of technical terms and translate them into accessible language using a contextual analysis algorithm. The generation unit automatically creates diagrams and concrete examples that supplement difficult concepts. For example, the generation unit makes complex technical explanations visually easy to understand using diagrams and concrete examples. The generation unit can automatically generate diagrams and concrete examples using a data visualization algorithm. The integration unit integrates with document management tools and educational platforms. For example, the integration unit can integrate with educational platforms to allow students to use translation of technical terms and automatic generation of diagrams when learning. The integration unit can seamlessly integrate with other tools using API integration. The optimization unit optimizes translation accuracy and content by utilizing user feedback. For example, the optimization unit collects user feedback, and the AI ​​learns and optimizes based on that feedback. The optimization unit can optimize translation accuracy and content based on feedback using machine learning algorithms. As a result, the AI ​​tool according to this embodiment can translate specialized terminology, generate charts and graphs, integrate with other tools, and optimize based on feedback.

[0065] The translation department stores specialized terminology in a database and translates it in an accessible way by understanding the context. Specialized terminology includes, but is not limited to, fields such as medicine, technology, and law. For example, the translation department translates medical terminology into language easily understood by the general public. Specifically, in the medical field, for instance, the term "myocardial infarction" might be translated as "a disease in which the blood vessels of the heart become blocked." In the technical field, "algorithm" could be translated as "a procedure for solving a problem." In the legal field, "contract" could be translated as "a document outlining the agreed-upon terms." The translation department uses contextual analysis algorithms to understand the context of specialized terminology and translate it into accessible language. These contextual analysis algorithms analyze the overall flow of a text and the surrounding context to provide an appropriate translation. For example, if the term "myocardial infarction" appears in a medical text, the context is analyzed to provide an appropriate translation. This allows the translation department to provide texts containing specialized terminology in a way that is easily understood by the general public. Furthermore, the translation department can regularly update its database of technical terms to reflect new terminology and the latest information. This ensures that translations are always based on the most up-to-date information.

[0066] The generation unit automatically creates diagrams and concrete examples to supplement complex concepts. For example, it makes complex technical explanations visually easier to understand using diagrams and concrete examples. Specifically, in a technical explanation, for instance, when explaining the "structure of a database," it can automatically generate diagrams showing database tables and relationships. In the medical field, for example, to explain the "mechanism of myocardial infarction," it could generate diagrams showing cross-sections of the heart and diagrams showing blood vessel blockages. The generation unit can automatically generate diagrams and concrete examples using data visualization algorithms. Data visualization algorithms are used to generate appropriate diagrams and concrete examples based on the input data and information. For example, it can take technical data as input and generate graphs and charts to visually represent that data. Furthermore, the generation unit can customize the format and content of diagrams and concrete examples according to the user's needs and requirements. This allows the generation unit to provide information in a way that is easy for users to understand. In addition, the generation unit can automatically insert the generated diagrams and concrete examples into documents to complement the overall explanation. This allows users to easily create documents that include visual information.

[0067] The Integration Unit integrates with document management tools and educational platforms. For example, it can integrate with educational platforms to enable students to use translation of technical terms and automatic generation of diagrams and charts when learning. Specifically, if the learning materials used by students on the educational platform contain technical terms, the Integration Unit will automatically translate those terms and present them in a way that is easy for students to understand. Furthermore, for difficult concepts in the learning materials, the Generation Unit will insert automatically generated diagrams and charts and concrete examples to make them visually easier to understand. The Integration Unit can seamlessly integrate with other tools using API integration. Through API integration, the Integration Unit can exchange data with document management tools and educational platforms in real time and provide necessary information. For example, if a document created by a user in a document management tool contains technical terms, the Integration Unit can automatically translate those terms and reflect them in the document. This allows users to efficiently create documents using translation of technical terms and automatic generation of diagrams and charts. In addition, the Integration Unit can also integrate with other educational tools and learning management systems to support student learning. This will allow the collaborative department to utilize the translation of specialized terminology and the automatic generation of diagrams and charts in educational settings to deepen students' understanding.

[0068] The optimization unit leverages user feedback to optimize translation accuracy and content. For example, the optimization unit collects user feedback, and the AI ​​learns and optimizes based on that feedback. Specifically, when users provide ratings and comments on translation results, the optimization unit collects this feedback, and the AI ​​learns from that information. Using machine learning algorithms, the system can optimize translation accuracy and content based on feedback. For instance, if a user rates a translation as "difficult to understand," the optimization unit uses that rating to improve the translation algorithm, making future translations clearer. Similarly, if a user rates a translation as "accurate," the optimization unit uses that result as a reference to improve translation accuracy in similar contexts. By continuously collecting user feedback and allowing the AI ​​to learn from that information, the optimization unit can constantly optimize translation accuracy and content. Furthermore, the optimization unit can analyze user feedback to identify common problems and areas for improvement. This allows the optimization unit to take concrete measures to improve the overall performance of the translation algorithm. As a result, the optimization unit can provide highly accurate translations that meet user needs, improving the user experience.

[0069] The translation unit can translate medical terminology into language that is easily understood by the general public. For example, the translation unit can translate medical terminology into everyday expressions. The translation unit can convert medical terminology such as disease names, treatments, and drug names into simple explanations. The translation unit uses contextual analysis algorithms to understand the context of medical terminology and translate it into more accessible language. This makes medical terminology easier to understand. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input medical terminology into a generating AI, which can understand the context and translate it into more accessible language.

[0070] The generation unit can make complex technical explanations visually easy to understand using diagrams and concrete examples. For example, the generation unit can make complex technical explanations visually easy to understand using flowcharts and graphs. The generation unit can explain specialized technical specifications and algorithm details using diagrams and concrete examples. The generation unit automatically generates diagrams and concrete examples using data visualization algorithms. This makes technical explanations visually easy to understand. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a technical explanation into a generation AI, and the generation AI can automatically generate diagrams and concrete examples.

[0071] The integration unit can work with the educational platform to enable students to use translations of technical terms and automatic generation of charts and graphs when learning. For example, the integration unit can work with the educational platform to provide translations of technical terms when students are learning. The integration unit can also work with the educational platform to provide automatic generation of charts and graphs when students are learning. The integration unit seamlessly integrates with the educational platform using API integration. This allows it to work with the educational platform to support learning. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input data obtained from the educational platform into a generating AI, which can then perform translations of technical terms and automatic generation of charts and graphs.

[0072] The optimization unit can collect user feedback, and the AI ​​can learn and optimize it. The optimization unit collects user feedback, for example, through surveys or analysis of usage logs. Based on the collected feedback, the AI ​​learns and optimizes the translation accuracy and content. The optimization unit uses machine learning algorithms to perform optimization based on the feedback. This allows for optimization that leverages user feedback. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user feedback data into a generating AI, which can then learn and perform optimization.

[0073] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is stressed, the translation unit will use simple and intuitive language. If the user is relaxed, the translation unit may use detailed and polite language. If the user is in a hurry, the translation unit will use concise and to-the-point language. This allows for the provision of translations that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI, which can then adjust the translation's expression based on the emotions.

[0074] The translation unit can adjust the level of detail in the translation based on the frequency of use of technical terms. For example, the translation unit may add a concise explanation to frequently used technical terms. It may also add a detailed explanation to less frequently used technical terms. It may translate technical terms of moderate frequency with an appropriate level of detail. This allows for providing a level of translation detail that corresponds to the frequency of use of technical terms. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input technical term frequency data into a generating AI, which can then adjust the level of detail in the translation based on the frequency of use.

[0075] The translation unit can apply different translation algorithms depending on the category of the technical term during translation. For example, the translation unit can apply a translation algorithm specialized for medical terminology to medical terminology. The translation unit can also apply a translation algorithm specialized for technical terminology to technical terminology. The translation unit can apply a translation algorithm specialized for legal terminology to legal terminology. This makes it possible to provide translation algorithms that are appropriate for the category of technical terminology. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input technical terminology category data into a generating AI, and the generating AI can apply different translation algorithms depending on the category.

[0076] The translation unit can estimate the user's emotions and adjust the translation length based on the estimated emotions. For example, if the user is stressed, the translation unit can provide a short, to-the-point translation. If the user is relaxed, the translation unit can also provide a detailed and polite translation. If the user is in a hurry, the translation unit can provide a concise and quick translation. This allows for translation lengths to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI, which can then adjust the translation length based on the emotion.

[0077] The translation department can prioritize translations based on the submission date of technical terms. For example, the department may prioritize translating recently submitted technical terms. It may also postpone the translation of older technical terms. The department dynamically adjusts the translation priority according to the submission date. This allows for the provision of translation priorities based on the submission date of technical terms. Some or all of the above processes in the translation department may be performed using AI, for example, or not using AI. For example, the translation department can input technical term submission date data into a generating AI, which can then determine the translation priority based on the submission date.

[0078] The translation unit can adjust the order of translations based on the relevance of technical terms during the translation process. For example, the translation unit may prioritize the translation of highly relevant technical terms. It may also postpone the translation of less relevant technical terms. The translation unit dynamically adjusts the order of translations according to the relevance of technical terms. This allows for the provision of a translation order that is appropriate to the relevance of technical terms. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input technical term relevance data into a generating AI, which can then adjust the order of translations based on relevance.

[0079] The generation unit can estimate the user's emotions and adjust the presentation of charts and examples based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate visually rich charts. If the user is in a hurry, the generation unit can also generate concise and to-the-point charts. If the user is excited, the generation unit can generate charts with visually stimulating effects. This allows for the provision of charts and examples 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 is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the presentation of charts and examples based on the emotions.

[0080] The generation unit can adjust the level of detail in the generated figures, tables, and examples based on the importance of the technical terms. For example, the generation unit can generate detailed figures for highly important technical terms. It can also generate concise figures for less important technical terms. For technical terms of moderate importance, it can generate figures with an appropriate level of detail. This allows for the provision of figures and examples with a level of detail corresponding to the importance of the technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term importance data into a generation AI, which can then adjust the level of detail in the figures and examples based on their importance.

[0081] The generation unit can apply different generation algorithms depending on the category of technical terms when generating diagrams and examples. For example, the generation unit can apply a medical diagram generation algorithm to technical terms in the medical field. The generation unit can also apply a technical diagram generation algorithm to technical terms in the technical field. The generation unit can apply a legal diagram generation algorithm to technical terms in the legal field. This makes it possible to provide generation algorithms that correspond to the category of technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term category data into a generation AI, and the generation AI can apply different generation algorithms depending on the category.

[0082] The generation unit can estimate the user's emotions and adjust the length of charts and examples based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise charts. If the user is relaxed, the generation unit can also generate longer charts with detailed explanations. If the user is excited, the generation unit can generate charts with visually stimulating effects. This allows for the provision of charts and examples with lengths appropriate 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 is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the length of charts and examples based on the emotions.

[0083] The generation unit can determine the generation priority based on the submission date of technical terms when generating figures, tables, and specific examples. For example, the generation unit may prioritize generating figures and tables based on recently submitted technical terms. The generation unit may also postpone the generation of figures and tables based on older technical terms. The generation unit dynamically adjusts the generation priority of figures and tables according to the submission date. This allows for the provision of a generation priority that corresponds to the submission date of technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term submission date data into a generation AI, which can then determine the generation priority based on the submission date.

[0084] The generation unit can adjust the generation order of figures, tables, and specific examples based on the relevance of technical terms. For example, the generation unit can prioritize the generation of figures and tables based on highly relevant technical terms. The generation unit can also postpone the generation of figures and tables based on less relevant technical terms. The generation unit dynamically adjusts the generation order of figures and tables according to the relevance of technical terms. This allows for a generation order that corresponds to the relevance of technical terms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input technical term relevance data into a generation AI, which can then adjust the generation order based on the relevance.

[0085] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated emotions. For example, if the user is stressed, the interaction unit can provide a simple and intuitive interaction method. If the user is relaxed, the interaction unit can also provide a detailed and careful interaction method. If the user is in a hurry, the interaction unit can provide a quick and concise interaction method. This allows for the provision of interaction methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI, for example, or not using AI. For example, the interaction unit can input user emotion data into the generative AI, which can then adjust the interaction method based on the emotions.

[0086] The integration unit can adjust the level of detail of the integration based on the frequency of use of other tools during integration. For example, the integration unit can provide a detailed integration method for frequently used tools. It can also provide a simpler integration method for infrequently used tools. For tools with moderate usage, it can provide an integration method with an appropriate level of detail. This allows for providing an integration level that corresponds to the frequency of use of other tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input usage frequency data of other tools into a generating AI, which can then adjust the level of detail of the integration based on the usage frequency.

[0087] The integration unit can apply different integration algorithms depending on the category of the other tool during integration. For example, the integration unit can apply an educational integration algorithm to an educational platform. It can also apply a document management integration algorithm to a document management tool. It can apply a communication integration algorithm to a communication tool. This allows the integration unit to provide integration algorithms tailored to the category of the other tool. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input category data of the other tool into a generating AI, which can then apply different integration algorithms depending on the category.

[0088] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the user is stressed, the collaboration unit will prioritize important collaborations. If the user is relaxed, the collaboration unit can also perform detailed collaborations. If the user is in a hurry, the collaboration unit will perform rapid collaborations. This allows for the provision of collaboration priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input user emotion data into a generative AI, which can then determine the priority of collaborations based on the emotions.

[0089] The integration unit can adjust the order of integration based on the submission timing of other tools during integration. For example, the integration unit may prioritize integration with recently submitted tools. The integration unit may also postpone integration with older tools. The integration unit dynamically adjusts the order of integration according to the submission timing. This allows for the provision of an integration order that corresponds to the submission timing of other tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input submission timing data of other tools into a generating AI, which can then adjust the order of integration based on the submission timing.

[0090] The integration unit can adjust the integration method based on the relationships between other tools during integration. For example, the integration unit may prioritize integrating highly relevant tools. It may also postpone integrating less relevant tools. The integration unit dynamically adjusts the integration method according to the relationships between tools. This allows it to provide an integration method that is appropriate to the relationships between other tools. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input relationship data of other tools into a generating AI, which can then adjust the integration method based on those relationships.

[0091] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is stressed, the optimization unit can provide a simple and intuitive optimization method. If the user is relaxed, the optimization unit can also provide a detailed and careful optimization method. If the user is in a hurry, the optimization unit can provide a quick and concise optimization method. This allows for the provision of optimization methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into a generative AI, which can then adjust the optimization method based on the emotions.

[0092] The optimization unit can adjust the level of detail of the optimization based on the importance of the user's feedback during the optimization process. For example, the optimization unit can perform detailed optimization for highly important feedback. The optimization unit can also perform concise optimization for less important feedback. The optimization unit can perform optimization with an appropriate level of detail for feedback of moderate importance. This allows the optimization unit to provide an level of detail that corresponds to the importance of the user's feedback. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the importance data of the user's feedback into a generating AI, which can then adjust the level of detail of the optimization based on the importance.

[0093] The optimization unit can apply different optimization algorithms depending on the category of user feedback during optimization. For example, the optimization unit can apply an educational optimization algorithm to feedback in the education field. The optimization unit can also apply a technical optimization algorithm to feedback in the technology field. The optimization unit can apply a medical optimization algorithm to feedback in the medical field. This makes it possible to provide an optimization algorithm that corresponds to the category of user feedback. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input user feedback category data into a generating AI, and the generating AI can apply different optimization algorithms depending on the category.

[0094] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit will prioritize important optimizations. If the user is relaxed, the optimization unit can also perform detailed optimizations. If the user is in a hurry, the optimization unit will perform rapid optimizations. This allows for optimization priorities to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input user emotion data into a generative AI, which can then determine optimization priorities based on the emotions.

[0095] The optimization unit can adjust the optimization order based on when user feedback is submitted. For example, the optimization unit may prioritize optimizing recently submitted feedback. The optimization unit may also postpone the optimization of older feedback. The optimization unit dynamically adjusts the optimization order according to the submission timing. This allows the optimization order to be provided according to when user feedback is submitted. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user feedback submission timing data into a generating AI, which can then adjust the optimization order based on the submission timing.

[0096] The optimization unit can adjust its optimization method based on the relevance of user feedback during optimization. For example, the optimization unit may prioritize optimizing highly relevant feedback. The optimization unit may also postpone optimizing less relevant feedback. The optimization unit dynamically adjusts its optimization method according to the relevance of the feedback. This allows it to provide an optimization method that is appropriate to the relevance of user feedback. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input relevance data of user feedback into a generating AI, which can then adjust its optimization method based on the relevance.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The translation unit can estimate the user's emotions and adjust the tone of the translation based on those emotions. For example, if the user is stressed, the translation unit can provide a gentle tone. If the user is relaxed, the translation unit can also provide a friendly tone. If the user is in a hurry, the translation unit can provide a concise and direct tone. This allows for a translation tone that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI, which can then adjust the tone of the translation based on the emotion.

[0099] The generation unit can estimate the user's emotions and adjust the colors of charts and examples based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate charts using calming colors. If the user is excited, the generation unit can also generate charts using vibrant colors. If the user is stressed, the generation unit can generate charts using calming colors. This allows for the provision of charts and examples with colors appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the colors of charts and examples based on the emotions.

[0100] The integration unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the integration unit will notify with a quiet sound. If the user is relaxed, the integration unit may also notify with a soft sound. If the user is in a hurry, the integration unit will notify with a quick and clear sound. This allows for the provision of notification methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into a generative AI, which can then adjust the notification method based on the emotion.

[0101] The optimization unit can estimate the user's emotions and adjust the optimization feedback method based on the estimated user emotions. For example, if the user is stressed, the optimization unit provides a simple and intuitive feedback method. If the user is relaxed, the optimization unit can also provide a detailed and thoughtful feedback method. If the user is in a hurry, the optimization unit provides a quick and concise feedback method. This allows for the provision of feedback methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI, which can then adjust the feedback method based on the emotions.

[0102] The translation unit can adjust the level of detail in translations based on the frequency of use of technical terms. For example, frequently used technical terms may be translated with a concise explanation. Less frequently used technical terms may be translated with a detailed explanation. Technical terms of moderate frequency may be translated with an appropriate level of detail. This allows for translation detail levels to be provided according to the frequency of use of technical terms. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input technical term frequency data into a generating AI, which can then adjust the level of detail in translations based on the frequency of use.

[0103] The generation unit can adjust the level of detail in the generated figures, tables, and examples based on the importance of the technical terms. For example, it can generate detailed figures and tables for highly important technical terms, concise figures and tables for less important technical terms, and figures and tables with an appropriate level of detail for moderately important technical terms. This allows for the provision of figures and examples with a level of detail appropriate to the importance of the technical terms. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input technical term importance data into a generation AI, which can then adjust the level of detail in the figures and examples based on their importance.

[0104] The integration unit can adjust the level of detail of the integration based on the frequency of use of other tools during integration. For example, it can provide a detailed integration method for frequently used tools, a simpler integration method for infrequently used tools, and an appropriately detailed integration method for moderately used tools. This allows for providing an integration level that corresponds to the frequency of use of other tools. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input usage frequency data of other tools into a generating AI, which can then adjust the level of detail of the integration based on the usage frequency.

[0105] The optimization unit can adjust the level of detail of the optimization based on the importance of the user's feedback. For example, it can perform detailed optimization for highly important feedback, simple optimization for less important feedback, and optimization with a moderate level of detail for moderately important feedback. This allows for providing an optimization level that corresponds to the importance of the user's feedback. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the importance data of the user's feedback into a generating AI, which can then adjust the level of detail of the optimization based on the importance.

[0106] The translation unit can apply different translation algorithms depending on the category of the technical term during translation. For example, a translation algorithm specialized for medical terminology can be applied to medical terminology. A translation algorithm specialized for technical terminology can also be applied to technical terminology. A translation algorithm specialized for legal terminology can be applied to legal terminology. This allows for the provision of translation algorithms tailored to the category of technical terminology. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input technical term category data into a generating AI, which can then apply different translation algorithms depending on the category.

[0107] The generation unit can apply different generation algorithms depending on the category of technical terms when generating diagrams and examples. For example, a medical diagram generation algorithm can be applied to technical terms in the medical field. A technical diagram generation algorithm can also be applied to technical terms in the technical field. A legal diagram generation algorithm can be applied to technical terms in the legal field. This makes it possible to provide generation algorithms that correspond to the category of technical terms. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input technical term category data into a generation AI, and the generation AI can apply different generation algorithms depending on the category.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The translation department stores technical terms in a database and translates them in an accessible way, understanding the context. Technical terms include, but are not limited to, fields such as medicine, technology, and law. The translation department uses contextual analysis algorithms to understand the context of technical terms and translate them into accessible language. Step 2: The generation unit automatically creates diagrams and concrete examples to supplement complex concepts. The generation unit uses data visualization algorithms to make complex technical explanations visually easy to understand using diagrams and concrete examples. Step 3: The integration unit integrates with document management tools and educational platforms. The integration unit can seamlessly integrate with other tools using API integration. For example, it can integrate with educational platforms to allow students to use translation of specialized terminology and automatic generation of charts and graphs when learning. Step 4: The optimization unit optimizes translation accuracy and content based on user feedback. The optimization unit collects user feedback and uses machine learning algorithms to optimize translation accuracy and content based on that feedback.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the translation unit, generation unit, collaboration unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the translation unit is implemented by the control unit 46A of the smart device 14, which stores technical terms in a database, understands the context, and translates them in an accessible way. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, which automatically creates diagrams and concrete examples to supplement difficult concepts. The collaboration unit is implemented by the control unit 46A of the smart device 14, which integrates with document management tools and educational platforms. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12, which optimizes translation accuracy and content by utilizing user feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the translation unit, generation unit, collaboration unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the translation unit is implemented by the control unit 46A of the smart glasses 214, which stores technical terms in a database, understands the context, and translates them in an accessible way. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically creates diagrams and concrete examples to supplement difficult concepts. The collaboration unit is implemented by the control unit 46A of the smart glasses 214, which integrates with document management tools and educational platforms. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes translation accuracy and content by utilizing user feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the translation unit, generation unit, collaboration unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the translation unit is implemented by the control unit 46A of the headset terminal 314, which stores technical terms in a database, understands the context, and translates them in an easily understandable way. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically creates diagrams and concrete examples to supplement difficult concepts. The collaboration unit is implemented by the control unit 46A of the headset terminal 314, which integrates with document management tools and educational platforms. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes translation accuracy and content by utilizing user feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the translation unit, generation unit, collaboration unit, and optimization unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the translation unit is implemented by the control unit 46A of the robot 414, which stores technical terms in a database, understands the context, and translates them in an easily understandable way. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically creates diagrams and concrete examples to supplement difficult concepts. The collaboration unit is implemented by the control unit 46A of the robot 414, which integrates with document management tools and educational platforms. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes translation accuracy and content by utilizing user feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A translation department that stores specialized terminology in a database and translates it in an accessible way while understanding the context, A generation unit that automatically creates diagrams and concrete examples to supplement difficult concepts, The integration department will integrate with document management tools and educational platforms, It includes an optimization unit that optimizes translation accuracy and content based on user feedback. A system characterized by the following features. (Note 2) The aforementioned translation department, Translating medical terminology into language that is easy for the general public to understand. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Making complex technical explanations visually easy to understand using diagrams, charts, and concrete examples. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, By integrating with educational platforms, we enable students to utilize features such as translation of specialized terminology and automatic generation of charts and graphs when learning. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, The AI ​​learns from user feedback and optimizes accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned translation department, During translation, adjust the level of detail based on the frequency of use of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned translation department, During translation, different translation algorithms are applied depending on the category of technical terminology. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned translation department, During translation, translation priorities are determined based on the timing of submission of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate the user's emotions and adjust the way charts, graphs, and specific examples are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating diagrams, charts, and specific examples, the level of detail is adjusted based on the importance of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating diagrams, charts, and concrete examples, different generation algorithms are applied depending on the category of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of charts and examples based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating diagrams, charts, and specific examples, the priority of generation is determined based on the timing of submission of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating diagrams, charts, and specific examples, the generation order is adjusted based on the relevance of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, When integrating, adjust the level of detail based on the frequency of use of other tools. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, When integrating, different integration algorithms are applied depending on the category of the other tool. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, During integration, the order of integration will be adjusted based on the submission timing of other tools. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, During integration, the method of integration is adjusted based on the relevance of other tools. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, the level of detail of the optimization is adjusted based on the importance of user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, During optimization, different optimization algorithms are applied depending on the category of user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, During optimization, the optimization order is adjusted based on when user feedback is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, During optimization, the optimization method is adjusted based on the relevance of user feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A translation department that stores specialized terminology in a database and translates it in an accessible way while understanding the context, A generation unit that automatically creates diagrams and concrete examples to supplement difficult concepts, The integration department will integrate with document management tools and educational platforms, It includes an optimization unit that optimizes translation accuracy and content based on user feedback. A system characterized by the following features.

2. The aforementioned translation department, Translating medical terminology into language that is easy for the general public to understand. The system according to feature 1.

3. The generating unit is Making complex technical explanations visually easy to understand using diagrams, charts, and concrete examples. The system according to feature 1.

4. The aforementioned linkage unit is, By integrating with educational platforms, we enable students to utilize features such as translation of specialized terminology and automatic generation of charts and graphs when learning. The system according to feature 1.

5. The optimization unit, The AI ​​learns from user feedback and optimizes the system. The system according to feature 1.

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

7. The aforementioned translation department, During translation, adjust the level of detail based on the frequency of use of technical terms. The system according to feature 1.

8. The aforementioned translation department, During translation, different translation algorithms are applied depending on the category of technical terminology. The system according to feature 1.

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