System

Abstract

An object of the system according to the embodiment is for the generative AI to understand the definitions of terms and generate an accurate answer.SOLUTION: A system according to an embodiment includes a definition providing unit and a database management unit. The definition providing unit provides a definition of a term that cannot be understood by the generation AI. The database management unit stores the definition of the term provided by the definition providing unit in the database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, generative AI may not understand the meaning or definition of certain terms, which could result in answers that lack context.

[0005] The system according to the embodiment aims to enable the generation AI to understand the definitions of terms and generate accurate answers. [Means for solving the problem]

[0006] The system according to the embodiment includes a definition providing unit and a database management unit. The definition providing unit provides definitions of terms that the generation AI cannot understand. The database management unit stores the definitions of terms provided by the definition providing unit in a database. [Effects of the Invention]

[0007] In the system according to the embodiment, the generation AI can understand the definition of terms and generate accurate answers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The "MonoWakari" system according to an embodiment of the present invention is a system that provides definitions of terms that a generation AI cannot understand, enabling the generation AI to generate accurate answers. This allows the "MonoWakari" system to accurately understand specific terms and generate answers with context.

[0029] The "MonoWakari" system according to the embodiment includes a definition providing unit and a database management unit. The definition providing unit provides definitions of terms that the generation AI cannot understand. For example, if the generation AI cannot understand the term "ABC Co., Ltd.", the definition providing unit provides that definition. The definition providing unit can also instantly provide the meaning or definition of terms that the generation AI cannot understand. The definition providing unit can also provide related information for terms that the generation AI cannot understand. The database management unit stores the term definitions provided by the definition providing unit in a database. For example, the database management unit can store the term definitions in a relational database. The database management unit can also store the term definitions in a NoSQL database. The database management unit can also periodically update the term definitions. As a result, the "MonoWakari" system according to the embodiment provides definitions of terms that the generation AI cannot understand and stores them in a database, allowing the generation AI to generate accurate answers. For example, even if the generation AI cannot understand the term "ABC Co., Ltd.", the definition can be instantly provided by utilizing "MonoWakari," enabling the generation AI to generate accurate answers.

[0030] When providing a definition of a term that the generation AI cannot understand, the definition providing unit can automatically search for related images or videos and provide visual information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can automatically search for related company logos and promotional videos and provide visual information to the user as well. This allows the user to deepen their understanding not only from text information but also from visual information. Furthermore, when the generation AI provides a definition of a term, the definition providing unit can automatically search for related images or videos and provide visual information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can automatically search for related company logos and promotional videos and provide visual information to the user as well. This allows the user to deepen their understanding by providing visual information as well.

[0031] When providing a definition of a term that the generation AI cannot understand, the definition providing unit can refer to past usage examples or context to present more specific examples. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can refer to past news articles and press releases to present specific examples. This allows the user to deepen their understanding through specific examples. Also, when the generation AI provides a definition of a term, the definition providing unit can refer to past usage examples and context to present more specific examples. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can refer to past news articles and press releases to present specific examples. This allows the user to deepen their understanding through referring to past usage examples and context.

[0032] When providing a definition of a term that the generation AI does not understand, the definition providing unit can simultaneously display definitions in different languages, thereby achieving multilingual support. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit simultaneously displays definitions in multiple languages, such as Japanese, English, and Chinese. This allows for support for users who speak different languages. Furthermore, when the generation AI provides a definition of a term, the definition providing unit can simultaneously display definitions in different languages, thereby achieving multilingual support. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition is simultaneously displayed in multiple languages, such as Japanese, English, and Chinese. This allows for multilingual support by simultaneously displaying definitions in different languages.

[0033] When providing a definition of a term that the generation AI cannot understand, the definition providing unit can automatically search for related news articles or academic papers to provide the latest information. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit automatically searches for the latest related news articles and press releases and provides them to the user. This allows the user to understand the latest corporate trends. In addition, when the generation AI provides a definition of a term, the definition providing unit can automatically search for related news articles and academic papers to provide the latest information. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit automatically searches for the latest related news articles and press releases and provides them to the user. This allows the user to automatically search for related news articles and academic papers to provide the latest information.

[0034] The "Monowakari" system is equipped with a database update unit that allows the generation AI to automatically add new terms and definitions, automating database updates. For example, the generation AI automatically detects new technical terms and industry terms and adds them to the terminology database. This allows the database to always have the latest information. The database update unit can also allow the generation AI to automatically add new terms and definitions, automating database updates. For example, the generation AI automatically detects new technical terms and industry terms and adds them to the terminology database. This automates database updates, allowing the system to always provide the latest information.

[0035] The "MonoWakari" system includes a graph display unit that displays the relationships between related terms in a terminology database in a graph structure, making it easier for users to understand visually. The graph display unit, for example, displays the relationships between terms in the terminology database in a graph structure, making it easier for users to understand visually. This allows users to intuitively grasp the relationships between the terms. The graph display unit can also display the relationships between related terms in the terminology database in a graph structure, making it easier for users to understand visually. For example, the relationships between terms in the terminology database can be displayed in a graph structure, making it easier for users to understand visually. This visual display of the relationships between terms can deepen the user's understanding.

[0036] The "Monowakari" system is equipped with a cloud management unit that manages a terminology database on the cloud and allows multiple users to access it simultaneously. The cloud management unit, for example, manages the terminology database on the cloud and builds a system that allows multiple users to access it simultaneously. This allows users to use the database in real time. The cloud management unit can also manage the terminology database on the cloud and allow multiple users to access it simultaneously. For example, a system is built that manages the terminology database on the cloud and allows multiple users to access it simultaneously. This allows multiple users to access it simultaneously by managing it on the cloud.

[0037] The "Monowakari" system is equipped with an expert editing department that provides a function for experts from different fields to jointly edit the terminology database, thereby improving the quality of the database. The expert editing department, for example, provides a function for experts from different fields to jointly edit the terminology database, thereby improving the quality of the database. The expert editing department also provides a function for experts from different fields to jointly edit the terminology database, thereby improving the quality of the database. For example, technical experts and industry experts work together to update term definitions. This allows experts from different fields to jointly edit, thereby improving the quality of the database.

[0038] The "MonoWakari" system includes a data source selection unit that, when the generation AI obtains a definition of a term, refers to multiple relevant data sources and selects the most reliable definition. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the data source selection unit refers to multiple reliable data sources (e.g., official website, industry reports, news articles) and selects the most reliable definition. This allows users to obtain reliable information. The data source selection unit can also refer to multiple relevant data sources and select the most reliable definition when the generation AI obtains a definition of a term. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the data source selection unit refers to multiple reliable data sources (e.g., official website, industry reports, news articles) and selects the most reliable definition. This allows the most reliable definition to be provided by referring to multiple data sources.

[0039] The "MonoWakari" system is equipped with a search history reference unit that references past user search history and provides the most appropriate definition when the generation AI obtains the definition of a term. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the search history reference unit references past user search history and provides the most appropriate definition. This allows the user to obtain a definition that is more suited to them. The search history reference unit can also reference past user search history and provide the most appropriate definition when the generation AI obtains the definition of a term. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", it references past user search history and provides the most appropriate definition. This allows the generation AI to provide the most appropriate definition by referencing past search history.

[0040] The "MonoWakari" system is equipped with a cooperative generation AI unit in which different generation AIs cooperate with each other to provide the most appropriate definition when a generation AI obtains a definition of a term. For example, when a generation AI obtains a definition of "ABC Co., Ltd.", the cooperative generation AI unit allows different generation AIs to cooperate with each other to provide the most appropriate definition. This allows users to obtain more reliable definitions. The cooperative generation AI unit can also allow different generation AIs to cooperate with each other to provide the most appropriate definition when a generation AI obtains a definition of a term. For example, when a generation AI obtains a definition of "ABC Co., Ltd.", different generation AIs cooperate with each other to provide the most appropriate definition. This allows different generation AIs to cooperate with each other to provide the most appropriate definition.

[0041] The "MonoWakari" system is equipped with a real-time update unit that updates related data in real time when the generation AI obtains a definition of a term, providing the latest definition. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the real-time update unit updates related data in real time and provides the latest definition. This allows users to always obtain the latest information. The real-time update unit can also update related data in real time when the generation AI obtains a definition of a term, providing the latest definition. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the real-time update unit updates related data in real time and provides the latest definition. This allows the latest definition to be provided by updating related data in real time.

[0042] The "MonoWakari" system adds a voice input function to the user interface and includes a voice input unit that can search for term definitions by voice. The voice input unit, for example, adds a voice input function to the user interface, allowing users to search for term definitions by voice. This allows users to search for term definitions without using their hands. The voice input unit can also add a voice input function to the user interface, allowing users to search for term definitions by voice. For example, when a user says, "Please tell me the definition of ABC Co., Ltd.", the generation AI provides the definition. By adding the voice input function, term definitions can be searched by voice.

[0043] The "MonoWakari" system includes a visual feedback unit that provides visual feedback to the user interface to make it easier for the user to understand the definition of a term. The visual feedback unit, for example, provides visual feedback to the user interface to make it easier for the user to understand the definition of a term. This allows the user to deepen their understanding through visual information. The visual feedback unit can also provide visual feedback to the user interface to make it easier for the user to understand the definition of a term. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it displays a related company logo or promotional video. This visual feedback makes it easier for the user to understand the definition of a term.

[0044] The "MonoWakari" system is equipped with a device support unit that enables the user interface to be used on different devices. The device support unit enables the user interface to be used on different devices, such as smartphones, tablets, and PCs. This allows users to use the interface comfortably on any device. The device support unit can also enable the user interface to be used on different devices. For example, the user interface can be used on different devices, such as smartphones, tablets, and PCs. This enables use on different devices, improving user convenience.

[0045] The "MonoWakari" system includes a customization unit that provides a customizable theme or layout for the user interface, allowing the user to change the interface to suit their preferences. For example, the customization unit provides a customizable theme for the user interface, allowing the user to change the interface's appearance to suit their preferences. This allows the user to use the interface more comfortably. The customization unit can also provide a customizable theme or layout for the user interface, allowing the user to change the interface to suit their preferences. For example, the user can select a dark mode or a light mode. This allows the user to change the interface to suit their preferences by providing a customizable theme or layout.

[0046] The "Monowakari" system includes a related information completion unit that automatically searches for and completes related information about a term when a user adds it. For example, when a user adds a new definition for "ABC Co., Ltd.", the related information completion unit automatically searches for related news articles and press releases to complete the definition. This makes the definition added by the user more specific and reliable. The related information completion unit can also automatically search for and complete related information about a term when a user adds it. For example, when a user adds a new definition for "ABC Co., Ltd.", the generation AI automatically searches for related news articles and press releases to complete the definition. This allows the generation AI to automatically search for and complete related information, improving the quality of the definition added by the user.

[0047] The "MonoWakari" system includes a feedback collection unit that collects feedback from other users on terms or definitions added by a user and improves the quality of the definition. For example, when a user adds a new definition for "ABC Co., Ltd.", the feedback collection unit collects feedback from other users and improves the quality of the definition. This allows the user to improve the definition by taking into consideration the opinions of other users. The feedback collection unit can also collect feedback from other users on terms or definitions added by a user and improve the quality of the definition. For example, when a user adds a new definition for "ABC Co., Ltd.", the feedback collection unit collects feedback from other users and improves the quality of the definition. This allows the quality of the definition to be improved by collecting feedback from other users.

[0048] The "MonoWakari" system provides a function that allows users to share terms or definitions they have added with other users, and includes a sharing function unit that builds a community-based database. For example, when a user adds a new definition for "ABC Co., Ltd.", the sharing function unit provides a function that allows the user to share it with other users, and builds a community-based database. This allows users to share information with other users and build a database collaboratively. The sharing function unit also provides a function that allows users to share terms or definitions they have added with other users, and builds a community-based database. For example, when a user adds a new definition for "ABC Co., Ltd.", the function allows the user to share it with other users, and builds a community-based database. This allows sharing with other users to build a community-based database.

[0049] The "Monowakari" system includes an expert review unit that allows experts from different fields to review terms or definitions added by users, thereby improving the reliability of the definitions. For example, when a user adds a new definition for "ABC Co., Ltd.", the expert review unit has experts from different fields review the definition, improving the reliability of the definition. This allows users to obtain highly reliable information. The expert review unit can also have experts from different fields review terms or definitions added by users, improving the reliability of the definition. For example, when a user adds a new definition for "ABC Co., Ltd.", experts from different fields review the definition, improving the reliability of the definition. This allows the reliability of the definition to be improved by having experts from different fields review it.

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

[0051] When the "Monowakari" system provides a definition of a term that the generation AI cannot understand, it can automatically search for related audio information and provide auditory information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it automatically searches for audio interviews and presentations of related companies and provides the user with auditory information as well. This allows the user to deepen their understanding not only from text information but also from auditory information. In addition, the definition providing unit can automatically search for related audio information and provide auditory information as well when the generation AI provides a definition of a term. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it automatically searches for audio interviews and presentations of related companies and provides the user with auditory information as well. This allows the user to deepen their understanding by providing auditory information as well.

[0052] When the "MonoWakari" system provides a definition of a term that the generation AI cannot understand, it can automatically generate related 3D models and animations to make it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate 3D models and animations of related companies, providing the user with visual information as well. This allows the user to deepen their understanding not only from text information but also from visual information. In addition, the definition provider can automatically generate related 3D models and animations when the generation AI provides a definition of a term to make it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate 3D models and animations of related companies, providing the user with visual information as well. This allows the user to deepen their understanding by providing visual information as well.

[0053] When the "Monowakari" system provides a definition of a term that the generation AI cannot understand, it can automatically search for related music and sound effects, providing auditory information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically search for theme songs and sound effects of related companies, providing auditory information to the user as well. This allows the user to deepen their understanding not only from text information but also from auditory information. In addition, the definition providing unit can automatically search for related music and sound effects, providing auditory information as well, when the generation AI provides a definition of a term. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically search for theme songs and sound effects of related companies, providing auditory information to the user as well. This allows the user to deepen their understanding by providing auditory information as well.

[0054] When the "MonoWakari" system provides a definition of a term that the generation AI cannot understand, it can automatically generate related infographics and charts to make it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate infographics and charts of related companies, providing the user with visual information as well. This allows the user to deepen their understanding not only from text information but also from visual information. In addition, the definition provider can automatically generate related infographics and charts when the generation AI provides a definition of a term, making it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate infographics and charts of related companies, providing the user with visual information as well. This allows the user to deepen their understanding by providing visual information.

[0055] When the "Monowakari" system provides a definition of a term that the generation AI cannot understand, it can automatically generate related virtual reality (VR) content, allowing the user to understand the term with an immersive experience. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate a VR tour or virtual office of a related company, providing the user with visual information as well. This allows the user to deepen their understanding not only through text information but also through a virtual reality experience. In addition, the definition provider can automatically generate related VR content when the generation AI provides a definition of a term, allowing the user to understand the term with an immersive experience. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate a VR tour or virtual office of a related company, providing the user with visual information as well. This allows the user to deepen their understanding by providing a virtual reality experience.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The definition provider provides definitions for terms that the generation AI does not understand. For example, if the generation AI does not understand the term "ABC Co., Ltd.", the definition provider provides that definition. The definition provider can also immediately provide the meaning or definition of terms that the generation AI does not understand. Furthermore, the definition provider can also provide related information for terms that the generation AI does not understand. Step 2: The database management unit stores the term definitions provided by the definition providing unit in a database. For example, the database management unit stores the term definitions in a relational database. The database management unit can also store the term definitions in a NoSQL database. Furthermore, the database management unit can also periodically update the term definitions.

[0058] (Example 2) The "MonoWakari" system according to an embodiment of the present invention is a system that provides definitions of terms that a generation AI cannot understand, enabling the generation AI to generate accurate answers. This allows the "MonoWakari" system to accurately understand specific terms and generate answers with context.

[0059] The "MonoWakari" system according to the embodiment includes a definition providing unit and a database management unit. The definition providing unit provides definitions of terms that the generation AI cannot understand. For example, if the generation AI cannot understand the term "ABC Co., Ltd.", the definition providing unit provides that definition. The definition providing unit can also instantly provide the meaning or definition of terms that the generation AI cannot understand. The definition providing unit can also provide related information for terms that the generation AI cannot understand. The database management unit stores the term definitions provided by the definition providing unit in a database. For example, the database management unit can store the term definitions in a relational database. The database management unit can also store the term definitions in a NoSQL database. The database management unit can also periodically update the term definitions. As a result, the "MonoWakari" system according to the embodiment provides definitions of terms that the generation AI cannot understand and stores them in a database, allowing the generation AI to generate accurate answers. For example, even if the generation AI cannot understand the term "ABC Co., Ltd.", the definition can be instantly provided by utilizing "MonoWakari," enabling the generation AI to generate accurate answers.

[0060] When providing a definition of a term that the generation AI cannot understand, the definition providing unit can automatically search for related images or videos and provide visual information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can automatically search for related company logos and promotional videos and provide visual information to the user as well. This allows the user to deepen their understanding not only from text information but also from visual information. Furthermore, when the generation AI provides a definition of a term, the definition providing unit can automatically search for related images or videos and provide visual information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can automatically search for related company logos and promotional videos and provide visual information to the user as well. This allows the user to deepen their understanding by providing visual information as well.

[0061] When providing a definition of a term that the generation AI cannot understand, the definition providing unit can refer to past usage examples or context to present more specific examples. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can refer to past news articles and press releases to present specific examples. This allows the user to deepen their understanding through specific examples. Also, when the generation AI provides a definition of a term, the definition providing unit can refer to past usage examples and context to present more specific examples. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit can refer to past news articles and press releases to present specific examples. This allows the user to deepen their understanding through referring to past usage examples and context.

[0062] The definition providing unit can use the emotion estimation function to analyze the emotions expressed when a user understands a term definition and provide additional information to improve the user's understanding. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit analyzes the user's facial expressions and voice, and if it determines that the user's level of understanding is low, it provides additional explanations and related information. This allows the user to gain a deeper understanding. The definition providing unit can also use the emotion estimation function to analyze the emotions expressed when a user understands a term definition and provide additional information to improve the user's understanding. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit analyzes the user's facial expressions and voice, and if it determines that the user's level of understanding is low, it provides additional explanations and related information. This allows the user to gain a deeper understanding.

[0063] When providing a definition of a term that the generation AI does not understand, the definition providing unit can simultaneously display definitions in different languages, thereby achieving multilingual support. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit simultaneously displays definitions in multiple languages, such as Japanese, English, and Chinese. This allows for support for users who speak different languages. Furthermore, when the generation AI provides a definition of a term, the definition providing unit can simultaneously display definitions in different languages, thereby achieving multilingual support. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition is simultaneously displayed in multiple languages, such as Japanese, English, and Chinese. This allows for multilingual support by simultaneously displaying definitions in different languages.

[0064] When providing a definition of a term that the generation AI cannot understand, the definition providing unit can automatically search for related news articles or academic papers to provide the latest information. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit automatically searches for the latest related news articles and press releases and provides them to the user. This allows the user to understand the latest corporate trends. In addition, when the generation AI provides a definition of a term, the definition providing unit can automatically search for related news articles and academic papers to provide the latest information. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit automatically searches for the latest related news articles and press releases and provides them to the user. This allows the user to automatically search for related news articles and academic papers to provide the latest information.

[0065] The definition providing unit can use the emotion estimation function to analyze the emotions felt by users when searching for definitions of terms and personalize search results. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit analyzes the user's emotional response in real time and provides search results that elicit positive emotions. This allows users to obtain search results that are more satisfying. The definition providing unit can also use the emotion estimation function to analyze the emotions felt by users when searching for definitions of terms and personalize search results. For example, when the generation AI provides a definition of "ABC Co., Ltd.", the definition providing unit analyzes the user's emotional response in real time and provides search results that elicit positive emotions. This allows search results to be personalized by using the emotion estimation function.

[0066] The "Monowakari" system is equipped with a database update unit that allows the generation AI to automatically add new terms and definitions, automating database updates. For example, the generation AI automatically detects new technical terms and industry terms and adds them to the terminology database. This allows the database to always have the latest information. The database update unit can also allow the generation AI to automatically add new terms and definitions, automating database updates. For example, the generation AI automatically detects new technical terms and industry terms and adds them to the terminology database. This automates database updates, allowing the system to always provide the latest information.

[0067] The "MonoWakari" system includes a graph display unit that displays the relationships between related terms in a terminology database in a graph structure, making it easier for users to understand visually. The graph display unit, for example, displays the relationships between terms in the terminology database in a graph structure, making it easier for users to understand visually. This allows users to intuitively grasp the relationships between the terms. The graph display unit can also display the relationships between related terms in the terminology database in a graph structure, making it easier for users to understand visually. For example, the relationships between terms in the terminology database can be displayed in a graph structure, making it easier for users to understand visually. This visual display of the relationships between terms can deepen the user's understanding.

[0068] The term database may include an emotion analysis unit that uses an emotion estimation function to analyze the emotion a user feels when using the term database, thereby improving the usability of the database. The emotion analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when using the term database in real time, thereby improving usability. This allows the user to use the database more comfortably. The emotion analysis unit may also use the emotion estimation function to analyze the emotion a user feels when using the term database, thereby improving the usability of the database. For example, the emotion estimation function may be used to analyze the emotion a user feels when using the term database in real time, thereby improving usability. This allows the usability of the database to be improved by using the emotion estimation function.

[0069] The "Monowakari" system is equipped with a cloud management unit that manages a terminology database on the cloud and allows multiple users to access it simultaneously. The cloud management unit, for example, manages the terminology database on the cloud and builds a system that allows multiple users to access it simultaneously. This allows users to use the database in real time. The cloud management unit can also manage the terminology database on the cloud and allow multiple users to access it simultaneously. For example, a system is built that manages the terminology database on the cloud and allows multiple users to access it simultaneously. This allows multiple users to access it simultaneously by managing it on the cloud.

[0070] The "Monowakari" system is equipped with an expert editing department that provides a function for experts from different fields to jointly edit the terminology database, thereby improving the quality of the database. The expert editing department, for example, provides a function for experts from different fields to jointly edit the terminology database, thereby improving the quality of the database. The expert editing department also provides a function for experts from different fields to jointly edit the terminology database, thereby improving the quality of the database. For example, technical experts and industry experts work together to update term definitions. This allows experts from different fields to jointly edit, thereby improving the quality of the database.

[0071] The term database may include an emotion analysis unit that uses an emotion estimation function to analyze the emotion a user feels when using the term database and personalize the contents of the database. The emotion analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when using the term database in real time and personalize the contents of the database. This allows the user to obtain content that is more suited to them. The emotion analysis unit may also use the emotion estimation function to analyze the emotion a user feels when using the term database and personalize the contents of the database. For example, the emotion estimation function may be used to analyze the emotion a user feels when using the term database in real time and personalize the contents of the database. This allows the content of the database to be personalized by using the emotion estimation function.

[0072] The "MonoWakari" system includes a data source selection unit that, when the generation AI obtains a definition of a term, refers to multiple relevant data sources and selects the most reliable definition. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the data source selection unit refers to multiple reliable data sources (e.g., official website, industry reports, news articles) and selects the most reliable definition. This allows users to obtain reliable information. The data source selection unit can also refer to multiple relevant data sources and select the most reliable definition when the generation AI obtains a definition of a term. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the data source selection unit refers to multiple reliable data sources (e.g., official website, industry reports, news articles) and selects the most reliable definition. This allows the most reliable definition to be provided by referring to multiple data sources.

[0073] The "MonoWakari" system is equipped with a search history reference unit that references past user search history and provides the most appropriate definition when the generation AI obtains the definition of a term. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the search history reference unit references past user search history and provides the most appropriate definition. This allows the user to obtain a definition that is more suited to them. The search history reference unit can also reference past user search history and provide the most appropriate definition when the generation AI obtains the definition of a term. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", it references past user search history and provides the most appropriate definition. This allows the generation AI to provide the most appropriate definition by referencing past search history.

[0074] The generation AI can be equipped with an emotion analysis unit that uses the emotion estimation function to analyze the user's emotions when obtaining a term definition and improve the accuracy of the definition. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the emotion analysis unit analyzes the user's emotional response in real time and improves the accuracy of the definition. This allows the user to obtain a more accurate definition. The emotion analysis unit can also use the emotion estimation function to analyze the user's emotions when the generation AI obtains a term definition and improve the accuracy of the definition. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the emotion analysis unit analyzes the user's emotional response in real time and improves the accuracy of the definition. This allows the accuracy of the definition to be improved by using the emotion estimation function.

[0075] The "MonoWakari" system is equipped with a cooperative generation AI unit in which different generation AIs cooperate with each other to provide the most appropriate definition when a generation AI obtains a definition of a term. For example, when a generation AI obtains a definition of "ABC Co., Ltd.", the cooperative generation AI unit allows different generation AIs to cooperate with each other to provide the most appropriate definition. This allows users to obtain more reliable definitions. The cooperative generation AI unit can also allow different generation AIs to cooperate with each other to provide the most appropriate definition when a generation AI obtains a definition of a term. For example, when a generation AI obtains a definition of "ABC Co., Ltd.", different generation AIs cooperate with each other to provide the most appropriate definition. This allows different generation AIs to cooperate with each other to provide the most appropriate definition.

[0076] The "MonoWakari" system is equipped with a real-time update unit that updates related data in real time when the generation AI obtains a definition of a term, providing the latest definition. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the real-time update unit updates related data in real time and provides the latest definition. This allows users to always obtain the latest information. The real-time update unit can also update related data in real time when the generation AI obtains a definition of a term, providing the latest definition. For example, when the generation AI obtains a definition of "ABC Co., Ltd.", the real-time update unit updates related data in real time and provides the latest definition. This allows the latest definition to be provided by updating related data in real time.

[0077] The generation AI can be equipped with an emotion analysis unit that uses the emotion estimation function to analyze the user's emotions when obtaining a term definition and personalize the method of providing the definition. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the emotion analysis unit analyzes the user's emotional response in real time and personalizes the method of providing the definition. This allows the user to obtain a definition that is more suited to them. The emotion analysis unit can also use the emotion estimation function to analyze the user's emotions when the generation AI obtains a term definition and personalize the method of providing the definition. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the emotion analysis unit analyzes the user's emotional response in real time and personalizes the method of providing the definition. This allows the method of providing the definition to be personalized by using the emotion estimation function.

[0078] The "MonoWakari" system adds a voice input function to the user interface and includes a voice input unit that can search for term definitions by voice. The voice input unit, for example, adds a voice input function to the user interface, allowing users to search for term definitions by voice. This allows users to search for term definitions without using their hands. The voice input unit can also add a voice input function to the user interface, allowing users to search for term definitions by voice. For example, when a user says, "Please tell me the definition of ABC Co., Ltd.", the generation AI provides the definition. By adding the voice input function, term definitions can be searched by voice.

[0079] The "MonoWakari" system includes a visual feedback unit that provides visual feedback to the user interface to make it easier for the user to understand the definition of a term. The visual feedback unit, for example, provides visual feedback to the user interface to make it easier for the user to understand the definition of a term. This allows the user to deepen their understanding through visual information. The visual feedback unit can also provide visual feedback to the user interface to make it easier for the user to understand the definition of a term. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it displays a related company logo or promotional video. This visual feedback makes it easier for the user to understand the definition of a term.

[0080] The user interface may include an emotion analysis unit that uses an emotion estimation function to analyze the emotion a user feels when using the interface, thereby improving the usability of the interface. The emotion analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when using the interface in real time, thereby improving usability. This allows the user to use the interface more comfortably. The emotion analysis unit may also use the emotion estimation function to analyze the emotion a user feels when using the interface, thereby improving the usability of the interface. For example, the emotion estimation function may be used to analyze the emotion a user feels when using the interface in real time, thereby improving usability. This allows the use of the emotion estimation function to improve the usability of the interface.

[0081] The "MonoWakari" system is equipped with a device support unit that enables the user interface to be used on different devices. The device support unit enables the user interface to be used on different devices, such as smartphones, tablets, and PCs. This allows users to use the interface comfortably on any device. The device support unit can also enable the user interface to be used on different devices. For example, the user interface can be used on different devices, such as smartphones, tablets, and PCs. This enables use on different devices, improving user convenience.

[0082] The "MonoWakari" system includes a customization unit that provides a customizable theme or layout for the user interface, allowing the user to change the interface to suit their preferences. For example, the customization unit provides a customizable theme for the user interface, allowing the user to change the interface's appearance to suit their preferences. This allows the user to use the interface more comfortably. The customization unit can also provide a customizable theme or layout for the user interface, allowing the user to change the interface to suit their preferences. For example, the user can select a dark mode or a light mode. This allows the user to change the interface to suit their preferences by providing a customizable theme or layout.

[0083] The user interface may include an emotion analysis unit that uses an emotion estimation function to analyze the emotion a user feels when using the interface and personalize the design of the interface. The emotion analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when using the interface in real time and personalize the design. This allows the user to use the interface more comfortably. The emotion analysis unit may also use the emotion estimation function to analyze the emotion a user feels when using the interface and personalize the design of the interface. For example, if the user is feeling stressed, colors or designs that help the user relax may be provided. In this way, the emotion estimation function can be used to personalize the design of the interface.

[0084] The "Monowakari" system includes a related information completion unit that automatically searches for and completes related information about a term when a user adds it. For example, when a user adds a new definition for "ABC Co., Ltd.", the related information completion unit automatically searches for related news articles and press releases to complete the definition. This makes the definition added by the user more specific and reliable. The related information completion unit can also automatically search for and complete related information about a term when a user adds it. For example, when a user adds a new definition for "ABC Co., Ltd.", the generation AI automatically searches for related news articles and press releases to complete the definition. This allows the generation AI to automatically search for and complete related information, improving the quality of the definition added by the user.

[0085] The "MonoWakari" system includes a feedback collection unit that collects feedback from other users on terms or definitions added by a user and improves the quality of the definition. For example, when a user adds a new definition for "ABC Co., Ltd.", the feedback collection unit collects feedback from other users and improves the quality of the definition. This allows the user to improve the definition by taking into consideration the opinions of other users. The feedback collection unit can also collect feedback from other users on terms or definitions added by a user and improve the quality of the definition. For example, when a user adds a new definition for "ABC Co., Ltd.", the feedback collection unit collects feedback from other users and improves the quality of the definition. This allows the quality of the definition to be improved by collecting feedback from other users.

[0086] The generative AI can be equipped with an emotion analysis unit that uses an emotion estimation function to analyze the emotions of users when customizing terms and optimize the customization process. For example, the emotion analysis unit uses the emotion estimation function to analyze the emotions of users when adding a new definition for "ABC Co., Ltd." in real time and optimizes the customization process. This allows users to customize more comfortably. The emotion analysis unit can also use the emotion estimation function to analyze the emotions of users when customizing terms and optimize the customization process. For example, the emotion estimation function can analyze the emotions of users when adding a new definition for "ABC Co., Ltd." in real time and optimize the customization process. This allows the customization process to be optimized by using the emotion estimation function.

[0087] The "MonoWakari" system provides a function that allows users to share terms or definitions they have added with other users, and includes a sharing function unit that builds a community-based database. For example, when a user adds a new definition for "ABC Co., Ltd.", the sharing function unit provides a function that allows the user to share it with other users, and builds a community-based database. This allows users to share information with other users and build a database collaboratively. The sharing function unit also provides a function that allows users to share terms or definitions they have added with other users, and builds a community-based database. For example, when a user adds a new definition for "ABC Co., Ltd.", the function allows the user to share it with other users, and builds a community-based database. This allows sharing with other users to build a community-based database.

[0088] The "Monowakari" system includes an expert review unit that allows experts from different fields to review terms or definitions added by users, thereby improving the reliability of the definitions. For example, when a user adds a new definition for "ABC Co., Ltd.", the expert review unit has experts from different fields review the definition, improving the reliability of the definition. This allows users to obtain highly reliable information. The expert review unit can also have experts from different fields review terms or definitions added by users, improving the reliability of the definition. For example, when a user adds a new definition for "ABC Co., Ltd.", experts from different fields review the definition, improving the reliability of the definition. This allows the reliability of the definition to be improved by having experts from different fields review it.

[0089] The generative AI may include an emotion analysis unit that uses an emotion estimation function to analyze the emotions of a user when customizing terms and personalize the customization process. For example, the emotion analysis unit uses the emotion estimation function to analyze the emotions of a user when adding a new definition for "ABC Co., Ltd." in real time and personalize the customization process. This allows the user to customize more comfortably. The emotion analysis unit may also use the emotion estimation function to analyze the emotions of a user when customizing terms and personalize the customization process. For example, the emotion estimation function may analyze the emotions of a user when adding a new definition for "ABC Co., Ltd." in real time and personalize the customization process. This allows the customization process to be personalized by using the emotion estimation function.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When the "Monowakari" system provides a definition of a term that the generation AI cannot understand, it can automatically search for related audio information and provide auditory information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it automatically searches for audio interviews and presentations of related companies and provides the user with auditory information as well. This allows the user to deepen their understanding not only from text information but also from auditory information. In addition, the definition providing unit can automatically search for related audio information and provide auditory information as well when the generation AI provides a definition of a term. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it automatically searches for audio interviews and presentations of related companies and provides the user with auditory information as well. This allows the user to deepen their understanding by providing auditory information as well.

[0092] When the "MonoWakari" system provides a definition of a term that the generation AI cannot understand, it can automatically generate related 3D models and animations to make it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate 3D models and animations of related companies, providing the user with visual information as well. This allows the user to deepen their understanding not only from text information but also from visual information. In addition, the definition provider can automatically generate related 3D models and animations when the generation AI provides a definition of a term to make it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate 3D models and animations of related companies, providing the user with visual information as well. This allows the user to deepen their understanding by providing visual information as well.

[0093] When the "Monowakari" system provides a definition of a term that the generation AI cannot understand, it can automatically search for related music and sound effects, providing auditory information as well. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically search for theme songs and sound effects of related companies, providing auditory information to the user as well. This allows the user to deepen their understanding not only from text information but also from auditory information. In addition, the definition providing unit can automatically search for related music and sound effects, providing auditory information as well, when the generation AI provides a definition of a term. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically search for theme songs and sound effects of related companies, providing auditory information to the user as well. This allows the user to deepen their understanding by providing auditory information as well.

[0094] When the "MonoWakari" system provides a definition of a term that the generation AI cannot understand, it can automatically generate related infographics and charts to make it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate infographics and charts of related companies, providing the user with visual information as well. This allows the user to deepen their understanding not only from text information but also from visual information. In addition, the definition provider can automatically generate related infographics and charts when the generation AI provides a definition of a term, making it easier to understand visually. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate infographics and charts of related companies, providing the user with visual information as well. This allows the user to deepen their understanding by providing visual information.

[0095] When the "Monowakari" system provides a definition of a term that the generation AI cannot understand, it can automatically generate related virtual reality (VR) content, allowing the user to understand the term with an immersive experience. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate a VR tour or virtual office of a related company, providing the user with visual information as well. This allows the user to deepen their understanding not only through text information but also through a virtual reality experience. In addition, the definition provider can automatically generate related VR content when the generation AI provides a definition of a term, allowing the user to understand the term with an immersive experience. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it can automatically generate a VR tour or virtual office of a related company, providing the user with visual information as well. This allows the user to deepen their understanding by providing a virtual reality experience.

[0096] The definition providing unit can use the emotion estimation function to analyze the emotions felt when a user understands a term definition and provide additional information to improve their understanding. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it analyzes the user's facial expressions and voice, and if it determines that their level of understanding is low, it provides additional explanations and related information. This allows the user to gain a deeper understanding. The definition providing unit can also use the emotion estimation function to analyze the emotions felt when a user understands a term definition and provide additional information to improve their understanding. For example, when the generation AI provides a definition of "ABC Co., Ltd.", it analyzes the user's facial expressions and voice, and if it determines that their level of understanding is low, it provides additional explanations and related information. This allows the emotion estimation function to provide additional information to improve the user's understanding.

[0097] The definition providing unit can use the emotion estimation function to analyze the emotions felt by users when searching for definitions of terms and personalize search results. For example, when the generation AI provides a definition for "ABC Co., Ltd.", it analyzes the user's emotional response in real time and provides search results that elicit positive emotions. This allows users to obtain search results that are more satisfying. The definition providing unit can also use the emotion estimation function to analyze the emotions felt by users when searching for definitions of terms and personalize search results. For example, when the generation AI provides a definition for "ABC Co., Ltd.", it analyzes the user's emotional response in real time and provides search results that elicit positive emotions. This allows search results to be personalized by using the emotion estimation function.

[0098] The term database may include an emotion analysis unit that uses an emotion estimation function to analyze the emotion a user feels when using the term database, thereby improving the usability of the database. The emotion analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when using the term database in real time, thereby improving usability. This allows the user to use the database more comfortably. The emotion analysis unit may also use the emotion estimation function to analyze the emotion a user feels when using the term database, thereby improving the usability of the database. For example, the emotion estimation function may be used to analyze the emotion a user feels when using the term database in real time, thereby improving usability. This allows the usability of the database to be improved by using the emotion estimation function.

[0099] The generation AI can be equipped with an emotion analysis unit that uses the emotion estimation function to analyze the user's emotions when obtaining a term definition and improve the accuracy of the definition. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the emotion analysis unit analyzes the user's emotional response in real time and improves the accuracy of the definition. This allows the user to obtain a more accurate definition. The emotion analysis unit can also use the emotion estimation function to analyze the user's emotions when the generation AI obtains a term definition and improve the accuracy of the definition. For example, when the generation AI obtains the definition of "ABC Co., Ltd.", the emotion analysis unit analyzes the user's emotional response in real time and improves the accuracy of the definition. This allows the accuracy of the definition to be improved by using the emotion estimation function.

[0100] The generative AI can be equipped with an emotion analysis unit that uses an emotion estimation function to analyze the emotions of users when customizing terms and optimize the customization process. For example, the emotion analysis unit uses the emotion estimation function to analyze the emotions of users when adding a new definition for "ABC Co., Ltd." in real time and optimizes the customization process. This allows users to customize more comfortably. The emotion analysis unit can also use the emotion estimation function to analyze the emotions of users when customizing terms and optimize the customization process. For example, the emotion estimation function can analyze the emotions of users when adding a new definition for "ABC Co., Ltd." in real time and optimize the customization process. This allows the customization process to be optimized by using the emotion estimation function.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The definition provider provides definitions for terms that the generation AI does not understand. For example, if the generation AI does not understand the term "ABC Co., Ltd.", the definition provider provides that definition. The definition provider can also immediately provide the meaning or definition of terms that the generation AI does not understand. Furthermore, the definition provider can also provide related information for terms that the generation AI does not understand. Step 2: The database management unit stores the term definitions provided by the definition providing unit in a database. For example, the database management unit stores the term definitions in a relational database. The database management unit can also store the term definitions in a NoSQL database. Furthermore, the database management unit can also periodically update the term definitions.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a definition providing unit that provides definitions of terms that the generating AI cannot understand; a database management unit that stores the definitions of the terms provided by the definition providing unit in a database; A system characterized by:

2. The definition providing unit When providing a definition of a term that the AI ​​does not understand, it automatically searches for related images or videos to provide visual information.

2. The system of claim 1.

3. The definition providing unit When providing definitions of terms that the AI ​​generation cannot understand, it also displays definitions in different languages ​​simultaneously, achieving multilingual support.

2. The system of claim 1.

4. The generative AI automatically adds new terms or definitions and has a database update section that automatically updates the database.

2. The system of claim 1.

5. When the generation AI obtains term definitions, it references multiple relevant data sources and has a data source selection section that selects the most reliable definition.

2. The system of claim 1.

6. A voice input function has been added to the user interface, providing a voice input section that allows users to search for definitions of terms by voice.

2. The system of claim 1.

7. When users add their own terms or definitions, the generation AI automatically searches for and completes related information for those terms.

2. The system of claim 1.

8. The definition providing unit When providing a definition of a term that the generation AI cannot understand, the emotion estimation function is used to analyze the emotion the user felt when understanding the definition of the term, and to provide additional information to improve comprehension.

2. The system of claim 1.

Images