System

Abstract

To provide a system for confirm the meaning of a word used in a seminar or a meeting on the spot.SOLUTION: A system includes an analysis part, a display part, and an explanation part. The analysis part performs morphological analysis on transcribed data. The display part makes a word analyzed by the analysis part clickable. The explanation part provides an explanation or analysis of the word clicked on the display part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to immediately check the meanings of technical terms and important words used in seminars and meetings, which can lead to incomplete understanding.

[0005] The system according to the embodiment aims to enable immediate confirmation of the meaning of words used in seminars and meetings. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a display unit, and an explanation unit. The analysis unit performs morphological analysis on the transcription data. The display unit makes the words analyzed by the analysis unit clickable. The explanation unit explains or analyzes the word clicked by the display unit. Effect of the Invention

[0007] The system according to the embodiment can enable the immediate confirmation of the meaning of words used in seminars and meetings. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that performs morphological analysis on the transcription data generated in an online seminar or online meeting (for example, a seminar or meeting using Zoom (registered trademark) or Teams (registered trademark)) and makes each word clickable. When a user clicks on a word, the AI ​​explains and analyzes the word, and displays not only a simple explanation but also content that matches the sentence (for example, if the word is stock price, the current stock price, etc.). This allows the meaning of terms that are used in the atmosphere while listening to a talk to be confirmed on the spot, and allows for a deeper understanding of the content that was spoken. For example, the system performs morphological analysis on the transcription data generated in an online seminar or online meeting. Through morphological analysis, each word is divided and displayed as a clickable link. Next, when a user clicks on a word that interests them, the AI ​​explains and analyzes the word. For example, when the user clicks on the word "stock price", the AI ​​displays the current stock price information. In addition, the AI ​​also provides explanations according to the context, so not only the meaning of the word but also information based on the context in which the word was used is provided. With this system, even if a term that the user does not understand appears during a seminar or meeting, the user can easily check the meaning on the spot and deepen their understanding. For example, this is particularly useful in technical seminars with many technical terms and finance-related meetings. In addition, the information provided by the AI ​​is updated in real time, so users can always obtain the latest information. This allows the system to check terms that users do not understand on the spot during online seminars and online meetings, deepening their understanding.

[0029] The system according to the embodiment includes an analysis unit, a display unit, and an explanation unit. The analysis unit performs morphological analysis on the transcription data generated in an online seminar or online meeting. The morphological analysis is performed using specific tools or techniques such as MeCab or Juman. The analysis unit performs morphological analysis on the transcription data, for example, and divides each word. The display unit makes the words analyzed by the analysis unit clickable. Specific methods for making the words clickable include a link setting method and a click area definition. For example, the display unit displays the analyzed words as hyperlinks so that the user can click them. The explanation unit explains and analyzes the words clicked by the display unit. Specific contents of the explanation and analysis include an explanation using a dictionary database and an analysis using statistical analysis. For example, the explanation unit obtains the meaning of the clicked word from the dictionary database and displays it to the user. The explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. This allows the system to check terms that the user does not understand during an online seminar or online meeting on the spot and deepen their understanding. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit may perform explanation or analysis using an AI model that takes the clicked word as input and outputs explanation or analysis.

[0030] The analysis unit performs morphological analysis on the transcription data generated in online seminars and online meetings. The morphological analysis is performed using specific tools and techniques such as MeCab and Juman. The analysis unit performs morphological analysis on the transcription data, for example, and divides each word. Specifically, when the transcription data is input, the analysis unit first performs preprocessing of the data. Preprocessing includes removing unnecessary symbols and spaces, unifying line breaks, and the like. Next, the transcription data is divided into words using a morphological analysis tool. For example, when MeCab is used, the transcription data is converted into the input format of MeCab and the analysis is performed. As a result of the analysis, information such as the part of speech, pronunciation, and base form of each word is obtained. This information is stored in a database to be used in subsequent processing. In addition, the analysis unit can calculate the frequency and co-occurrence of words and identify words and phrases with high importance. This allows the analysis unit to analyze the transcription data in detail and extract information that is useful to the user.

[0031] The display unit makes the words analyzed by the analysis unit clickable. Specific methods for making the words clickable include setting links and defining clickable areas. For example, the display unit displays the analyzed words as hyperlinks so that the user can click them. Specifically, the analysis results are displayed in HTML format, and for each word, A hyperlink is set using tags. This allows a user to click on a word and transition to the corresponding link destination. In addition, the definition of the clickable area can be styled using CSS to visually indicate that it is clickable. For example, the clickable area can be clearly indicated to the user by underlining the word or changing its color. Furthermore, the display unit can dynamically update the display content in response to the user's operation. For example, when a user clicks on a specific word, information related to the word is displayed as a pop-up window or a tooltip. In this way, the display unit intuitively and interactively displays the analyzed words, allowing the user to easily access the information.

[0032] The explanation unit performs explanation and analysis of the word clicked by the display unit. Specific contents of the explanation and analysis include explanation using a dictionary database and analysis using statistical analysis. For example, the explanation unit obtains the meaning of the clicked word from the dictionary database and displays it to the user. Specifically, when the user clicks on a word, the word is sent to the explanation unit. The explanation unit first accesses the dictionary database and searches for the meaning and usage examples of the corresponding word. The search results are displayed in a format that is easy for the user to understand. Furthermore, the explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. In this case, the explanation unit accesses an external stock price information API and obtains the latest stock price data. The obtained data is provided to the user in real time. The explanation unit can also perform statistical analysis of the word and display related topics and trends. For example, the context in which a specific word frequently appears and related news articles are displayed. This allows the user to deepen their understanding not only of the meaning of the word but also of how the word is used. Furthermore, some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide explanations and analysis using an AI model that takes the clicked word as input and outputs explanations and analysis. Specifically, an AI model using natural language processing technology is trained to provide the meaning of the word and related information with high accuracy. In this way, the explanation unit can provide quick and accurate information on the word clicked by the user, deepening the user's understanding.

[0033] The explanation unit can explain and analyze the clicked word. For example, the explanation unit obtains the meaning of the clicked word from a dictionary database and displays it to the user. For example, the explanation unit obtains the meaning of the clicked word from a dictionary database and displays it to the user. The explanation unit also provides information based on the context of the clicked word. For example, when a word "stock price" is clicked, current stock price information is displayed. This makes it easier for the user to understand the meaning of the word by explaining and analyzing the clicked word. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can perform explanation and analysis using an AI model that inputs the clicked word and outputs explanation and analysis.

[0034] The explanation unit can provide information according to the context. The explanation unit, for example, provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. The explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. In this way, by providing information according to the context, not only the meaning of the word but also information based on the context in which the word was used is provided. A part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that inputs the clicked word and outputs information according to the context.

[0035] The explanation unit may include an information providing unit that provides at least one of stock price information, explanations of technical terms, and related news articles. The explanation unit provides, for example, stock price information. For example, when a word of stock price is clicked, current stock price information is displayed. The explanation unit also provides an explanation of technical terms. For example, when a word of technical terms is clicked, an explanation of the technical term is displayed. The explanation unit also provides related news articles. For example, when a word of a news article is clicked, related news articles are displayed. In this way, by providing stock price information, explanations of technical terms, related news articles, and the like, the user can obtain more information. Part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that takes the clicked word as input and outputs information.

[0036] The analysis unit can use a general morphological analysis technique. The analysis unit performs morphological analysis using specific tools and techniques such as MeCab and Juman. For example, the analysis unit performs morphological analysis on the transcription data and divides each word. This improves the accuracy of the analysis by using a general morphological analysis technique. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that inputs transcription data and outputs morphological analysis.

[0037] The analysis unit can optimize the analysis algorithm by referring to the user's past click history during analysis. For example, the analysis unit preferentially analyzes words that the user has frequently clicked in the past. The analysis unit can also preferentially analyze words related to a specific field from the user's past click history. The analysis unit can also analyze the user's past click history and preferentially analyze words related to topics of interest. In this way, the analysis algorithm is optimized by referring to the user's past click history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that uses the user's past click history as input and optimizes the analysis algorithm.

[0038] The analysis unit can apply different analysis methods depending on the theme of the seminar or meeting during the analysis. For example, in a technical seminar, the analysis unit applies an analysis method specialized in technical terms. In addition, the analysis unit can also apply an analysis method specialized in financial terms in a financial meeting. In addition, the analysis unit can apply an analysis method specialized in education-related terms in an educational seminar. In this way, the accuracy of the analysis is improved by applying an analysis method depending on the theme of the seminar or meeting. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that inputs the theme of the seminar or meeting and applies an analysis method.

[0039] The analysis unit can prioritize analysis of region-specific terms in consideration of the geographical location information of the user during analysis. For example, when the user is in Japan, the analysis unit prioritizes analysis of Japan-specific terms. In addition, when the user is in the United States, the analysis unit can also prioritize analysis of America-specific terms. In addition, when the user is in Europe, the analysis unit can also prioritize analysis of Europe-specific terms. In this way, analysis of region-specific terms is prioritized by considering the geographical location information of the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that takes the geographical location information of the user as input and prioritizes analysis of region-specific terms.

[0040] During the analysis, the analysis unit can analyze the social media activity of the user and prioritize analysis of related terms. For example, the analysis unit prioritizes analysis of terms frequently used by the user on X (formerly Twitter (registered trademark)). The analysis unit can also prioritize analysis of terms related to topics in which the user is interested on Facebook (registered trademark). The analysis unit can also prioritize analysis of industry terms in which the user is interested on LinkedIn (registered trademark). In this way, analysis of related terms is prioritized by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that takes the social media activity of the user as input and prioritizes analysis of related terms.

[0041] The display unit can select the optimal display method by referring to the user's past click history at the time of display. For example, the display unit highlights words that the user has frequently clicked in the past. The display unit can also preferentially display information related to topics of interest from the user's past click history. The display unit can also analyze the user's past click history and provide an optimal display layout. As a result, the optimal display method is selected by referring to the user's past click history. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that inputs the user's past click history and selects the optimal display method.

[0042] The display unit can apply different display styles depending on the theme of the seminar or meeting when displaying. For example, in a technical seminar, the display unit applies a style that highlights technical terms. In addition, the display unit can also apply a style that highlights stock prices and economic indicators in a financial meeting. In addition, the display unit can also apply a style that highlights education-related terms in an educational seminar. In this way, the visibility of information is improved by applying a display style depending on the theme of the seminar or meeting. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display information using an AI model that uses the theme of the seminar or meeting as an input and applies a display style.

[0043] The display unit can select the optimal display method in consideration of the user's device information at the time of display. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. In addition, when the user is using a desktop, the display unit can also provide a display method including detailed information. In this way, the optimal display method is selected by considering the user's device information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display information using an AI model that inputs the user's device information and selects the optimal display method.

[0044] The display unit can analyze the user's social media activity and display related information preferentially when displaying. For example, the display unit highlights terms that the user frequently uses on X (formerly Twitter). The display unit can also preferentially display information related to topics in which the user is interested on Facebook. The display unit can also preferentially display industry information in which the user is interested on LinkedIn. This allows related information to be displayed preferentially by analyzing the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that uses the user's social media activity as an input and preferentially displays related information.

[0045] The explanation unit can adjust the level of detail of the explanation based on the importance of the clicked word during the explanation. For example, the explanation unit provides a detailed explanation in the case of an important word. In addition, the explanation unit can also provide a concise explanation in the case of a general word. In addition, the explanation unit can provide a detailed explanation including related background information in the case of technical terms. This makes it easier for the user to obtain the necessary information by adjusting the level of detail of the explanation based on the importance of the clicked word. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that uses the importance of the clicked word as an input and adjusts the level of detail of the explanation.

[0046] The explanation unit can apply different explanation algorithms depending on the category of the clicked word when explaining. For example, in the case of technical terms, the explanation unit provides an explanation including technical background information. In addition, in the case of financial terms, the explanation unit can also provide an explanation including the current market situation. In addition, in the case of educational terms, the explanation unit can also provide an explanation based on educational theory and practice. This makes it easier for the user to understand the information by applying the explanation algorithm depending on the category of the clicked word. Part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that uses the category of the clicked word as input and applies the explanation algorithm.

[0047] The explanation unit can determine the priority of the explanation based on the frequency of use of the clicked word during the explanation. For example, the explanation unit provides a detailed explanation for a frequently used word. Also, the explanation unit can provide a concise explanation for a commonly used word. Also, the explanation unit can provide a detailed explanation including related background information for technical terms. In this way, the priority of the explanation can be determined based on the frequency of use of the clicked word, so that the user can preferentially obtain important information. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that inputs the frequency of use of the clicked word and determines the priority of the explanation.

[0048] The explanation unit can adjust the order of explanations based on the relevance of the clicked word during explanation. For example, in the case of an important word, the explanation unit provides the explanation first. In addition, in the case of a general word, the explanation unit can also provide the explanation later. In addition, in the case of a technical term, the explanation unit can preferentially provide an explanation including related background information. In this way, by adjusting the order of explanations based on the relevance of the clicked word, the user can efficiently obtain related information. Part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that uses the relevance of the clicked word as an input and adjusts the order of explanations.

[0049] When providing information, the information providing unit can select optimal information by referring to the past usage history of the clicked word. For example, the information providing unit preferentially provides information related to words that the user has frequently clicked in the past. The information providing unit can also provide information related to topics of interest from the user's past usage history. The information providing unit can also analyze the user's past usage history and provide optimal information. As a result, optimal information is selected by referring to the past usage history of the clicked word. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs the past usage history of the clicked word and selects optimal information.

[0050] The information providing unit can apply different information providing methods according to the category of the clicked word when providing information. For example, in the case of technical terms, the information providing unit can provide technical background information and related research papers. In addition, in the case of financial terms, the information providing unit can also provide the current market situation and related news articles. In addition, in the case of educational terms, the information providing unit can also provide information based on educational theory and practice. In this way, by applying the information providing method according to the category of the clicked word, the user can easily understand the information. Part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that uses the category of the clicked word as input and applies the information providing method.

[0051] The information providing unit can provide optimal information in consideration of the geographical distribution of the clicked word when providing information. For example, when the user is in Japan, the information providing unit can provide information specific to Japan preferentially. Also, when the user is in America, the information providing unit can also provide information specific to America preferentially. Also, when the user is in Europe, the information providing unit can also provide information specific to Europe preferentially. In this way, optimal information is provided by considering the geographical distribution of the clicked word. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs the geographical distribution of the clicked word and provides optimal information.

[0052] The information providing unit can improve the accuracy of the information by referring to related literature of the clicked word when providing the information. The information providing unit can provide the information by referring to, for example, the latest research paper related to the clicked word. The information providing unit can also provide the information by referring to a news article related to the clicked word. The information providing unit can also provide the information by referring to a specialized book related to the clicked word. As a result, the accuracy of the information is improved by referring to related literature of the clicked word. A part or all of the above-mentioned processing in the information providing unit can be performed, for example, using AI or can be performed without using AI. For example, the information providing unit can provide the information by using an AI model that uses related literature of the clicked word as an input and improves the accuracy of the information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can optimize the analysis algorithm by referring to the user's past search history. For example, the analysis unit preferentially analyzes words that the user frequently searched in the past. The analysis unit can also preferentially analyze words related to a specific field from the user's past search history. Furthermore, the analysis unit can analyze the user's past search history and preferentially analyze words related to topics of interest. In this way, the analysis algorithm is optimized by referring to the user's past search history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that uses the user's past search history as input and optimizes the analysis algorithm.

[0055] The display unit can select the optimal display method by taking into account the device information of the user. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method that includes detailed information. In this way, the optimal display method is selected by taking into account the device information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display information using an AI model that inputs the device information of the user and selects the optimal display method.

[0056] The explanation unit can apply different explanation algorithms depending on the category of the clicked word. For example, in the case of technical terms, an explanation including technical background information is provided. In addition, in the case of financial terms, the explanation unit can also provide an explanation including the current market situation. Furthermore, in the case of educational terms, the explanation unit can also provide an explanation based on educational theory and practice. This makes it easier for the user to understand the information by applying the explanation algorithm depending on the category of the clicked word. Some or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI or may be performed without using AI. For example, the explanation unit can provide information using an AI model that takes the category of the clicked word as input and applies the explanation algorithm.

[0057] During the analysis, the analysis unit can prioritize analysis of region-specific terms in consideration of the geographical location information of the user. For example, when the user is in Japan, the analysis unit prioritizes analysis of Japan-specific terms. In addition, when the user is in the United States, the analysis unit can also prioritize analysis of United States-specific terms. Furthermore, when the user is in Europe, the analysis unit can also prioritize analysis of Europe-specific terms. In this way, analysis of region-specific terms is prioritized by considering the geographical location information of the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that takes the geographical location information of the user as input and prioritizes analysis of region-specific terms.

[0058] The explanation unit can determine the priority of the explanation based on the frequency of use of the clicked word during the explanation. For example, in the case of a frequently used word, a detailed explanation is provided. In addition, in the case of a commonly used word, the explanation unit can also provide a concise explanation. Furthermore, in the case of technical terms, the explanation unit can also provide a detailed explanation including related background information. In this way, by determining the priority of the explanation based on the frequency of use of the clicked word, the user can preferentially obtain important information. Part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI or may be performed without using AI. For example, the explanation unit can provide information using an AI model that inputs the frequency of use of the clicked word and determines the priority of the explanation.

[0059] The information providing unit can improve the accuracy of the information by referring to related literature of the clicked word when providing the information. For example, the information providing unit can provide the information by referring to the latest research paper related to the clicked word. The information providing unit can also provide the information by referring to a news article related to the clicked word. Furthermore, the information providing unit can also provide the information by referring to a specialized book related to the clicked word. As a result, the accuracy of the information is improved by referring to the related literature of the clicked word. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI or may be performed without using AI. For example, the information providing unit can provide the information using an AI model that uses the related literature of the clicked word as an input and improves the accuracy of the information.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The analysis unit performs morphological analysis on the transcription data generated during online seminars and online meetings. Morphological analysis is performed using specific tools and technologies, such as MeCab and Juman. The analysis unit performs morphological analysis on the transcription data and divides each word. Step 2: The display unit makes the words analyzed by the analysis unit clickable. Specific methods for making the words clickable include setting a link or defining a clickable area. For example, the display unit displays the analyzed words as hyperlinks so that the user can click them. Step 3: The explanation unit explains and analyzes the word clicked by the display unit. Specific contents of the explanation and analysis include explanation using a dictionary database and analysis using statistical analysis. For example, the explanation unit obtains the meaning of the clicked word from the dictionary database and displays it to the user. The explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can perform explanation and analysis using an AI model that takes the clicked word as input and outputs explanation and analysis.

[0062] (Example 2) The system according to the embodiment of the present invention is a system that performs morphological analysis on the transcription data generated in an online seminar or online meeting (for example, a seminar or meeting using Zoom (registered trademark) or Teams (registered trademark)) and makes each word clickable. When a user clicks on a word, the AI ​​explains and analyzes the word, and displays not only a simple explanation but also content that matches the sentence (for example, if the word is stock price, the current stock price, etc.). This allows the meaning of terms that are used in the atmosphere while listening to a talk to be confirmed on the spot, and allows for a deeper understanding of the content that was spoken. For example, the system performs morphological analysis on the transcription data generated in an online seminar or online meeting. Through morphological analysis, each word is divided and displayed as a clickable link. Next, when a user clicks on a word that interests them, the AI ​​explains and analyzes the word. For example, when the user clicks on the word "stock price", the AI ​​displays the current stock price information. In addition, the AI ​​also provides explanations according to the context, so not only the meaning of the word but also information based on the context in which the word was used is provided. With this system, even if a term that the user does not understand appears during a seminar or meeting, the user can easily check the meaning on the spot and deepen their understanding. For example, this is particularly useful in technical seminars with many technical terms and finance-related meetings. In addition, the information provided by the AI ​​is updated in real time, so users can always obtain the latest information. This allows the system to check terms that users do not understand on the spot during online seminars and online meetings, deepening their understanding.

[0063] The system according to the embodiment includes an analysis unit, a display unit, and an explanation unit. The analysis unit performs morphological analysis on the transcription data generated in an online seminar or online meeting. The morphological analysis is performed using specific tools or techniques such as MeCab or Juman. The analysis unit performs morphological analysis on the transcription data, for example, and divides each word. The display unit makes the words analyzed by the analysis unit clickable. Specific methods for making the words clickable include a link setting method and a click area definition. For example, the display unit displays the analyzed words as hyperlinks so that the user can click them. The explanation unit explains and analyzes the words clicked by the display unit. Specific contents of the explanation and analysis include an explanation using a dictionary database and an analysis using statistical analysis. For example, the explanation unit obtains the meaning of the clicked word from the dictionary database and displays it to the user. The explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. This allows the system to check terms that the user does not understand during an online seminar or online meeting on the spot and deepen their understanding. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit may perform explanation or analysis using an AI model that takes the clicked word as input and outputs explanation or analysis.

[0064] The analysis unit performs morphological analysis on the transcription data generated in online seminars and online meetings. The morphological analysis is performed using specific tools and techniques such as MeCab and Juman. The analysis unit performs morphological analysis on the transcription data, for example, and divides each word. Specifically, when the transcription data is input, the analysis unit first performs preprocessing of the data. Preprocessing includes removing unnecessary symbols and spaces, unifying line breaks, and the like. Next, the transcription data is divided into words using a morphological analysis tool. For example, when MeCab is used, the transcription data is converted into the input format of MeCab and the analysis is performed. As a result of the analysis, information such as the part of speech, pronunciation, and base form of each word is obtained. This information is stored in a database to be used in subsequent processing. In addition, the analysis unit can calculate the frequency and co-occurrence of words and identify words and phrases with high importance. This allows the analysis unit to analyze the transcription data in detail and extract information that is useful to the user.

[0065] The display unit makes the words analyzed by the analysis unit clickable. Specific methods for making the words clickable include setting links and defining clickable areas. For example, the display unit displays the analyzed words as hyperlinks so that the user can click them. Specifically, the analysis results are displayed in HTML format, and for each word, A hyperlink is set using tags. This allows a user to click on a word and transition to the corresponding link destination. In addition, the definition of the clickable area can be styled using CSS to visually indicate that it is clickable. For example, the clickable area can be clearly indicated to the user by underlining the word or changing its color. Furthermore, the display unit can dynamically update the display content in response to the user's operation. For example, when a user clicks on a specific word, information related to the word is displayed as a pop-up window or a tooltip. In this way, the display unit intuitively and interactively displays the analyzed words, allowing the user to easily access the information.

[0066] The explanation unit performs explanation and analysis of the word clicked by the display unit. Specific contents of the explanation and analysis include explanation using a dictionary database and analysis using statistical analysis. For example, the explanation unit obtains the meaning of the clicked word from the dictionary database and displays it to the user. Specifically, when the user clicks on a word, the word is sent to the explanation unit. The explanation unit first accesses the dictionary database and searches for the meaning and usage examples of the corresponding word. The search results are displayed in a format that is easy for the user to understand. Furthermore, the explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. In this case, the explanation unit accesses an external stock price information API and obtains the latest stock price data. The obtained data is provided to the user in real time. The explanation unit can also perform statistical analysis of the word and display related topics and trends. For example, the context in which a specific word frequently appears and related news articles are displayed. This allows the user to deepen their understanding not only of the meaning of the word but also of how the word is used. Furthermore, some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide explanations and analysis using an AI model that takes the clicked word as input and outputs explanations and analysis. Specifically, an AI model using natural language processing technology is trained to provide the meaning of the word and related information with high accuracy. In this way, the explanation unit can provide quick and accurate information on the word clicked by the user, deepening the user's understanding.

[0067] The explanation unit can explain and analyze the clicked word. For example, the explanation unit obtains the meaning of the clicked word from a dictionary database and displays it to the user. For example, the explanation unit obtains the meaning of the clicked word from a dictionary database and displays it to the user. The explanation unit also provides information based on the context of the clicked word. For example, when a word "stock price" is clicked, current stock price information is displayed. This makes it easier for the user to understand the meaning of the word by explaining and analyzing the clicked word. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can perform explanation and analysis using an AI model that inputs the clicked word and outputs explanation and analysis.

[0068] The explanation unit can provide information according to the context. The explanation unit, for example, provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. The explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. In this way, by providing information according to the context, not only the meaning of the word but also information based on the context in which the word was used is provided. A part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that inputs the clicked word and outputs information according to the context.

[0069] The explanation unit may include an information providing unit that provides at least one of stock price information, explanations of technical terms, and related news articles. The explanation unit provides, for example, stock price information. For example, when a word of stock price is clicked, current stock price information is displayed. The explanation unit also provides an explanation of technical terms. For example, when a word of technical terms is clicked, an explanation of the technical term is displayed. The explanation unit also provides related news articles. For example, when a word of a news article is clicked, related news articles are displayed. In this way, by providing stock price information, explanations of technical terms, related news articles, and the like, the user can obtain more information. Part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that takes the clicked word as input and outputs information.

[0070] The analysis unit can use a general morphological analysis technique. The analysis unit performs morphological analysis using specific tools and techniques such as MeCab and Juman. For example, the analysis unit performs morphological analysis on the transcription data and divides each word. This improves the accuracy of the analysis by using a general morphological analysis technique. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that inputs transcription data and outputs morphological analysis.

[0071] The analysis unit can estimate the user's emotion and adjust the accuracy of the morphological analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit increases the accuracy of the morphological analysis and reduces misrecognition. In addition, when the user is relaxed, the analysis unit can prioritize the analysis speed and tolerate some misrecognition. In addition, when the user is in a hurry, the analysis unit can maximize the analysis speed and provide a result quickly. Thereby, the accuracy of the analysis is improved by adjusting the accuracy of the morphological analysis based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can perform morphological analysis using an AI model that inputs the user's emotion data and adjusts the accuracy of the morphological analysis.

[0072] The analysis unit can optimize the analysis algorithm by referring to the user's past click history during analysis. For example, the analysis unit preferentially analyzes words that the user has frequently clicked in the past. The analysis unit can also preferentially analyze words related to a specific field from the user's past click history. The analysis unit can also analyze the user's past click history and preferentially analyze words related to topics of interest. In this way, the analysis algorithm is optimized by referring to the user's past click history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that uses the user's past click history as input and optimizes the analysis algorithm.

[0073] The analysis unit can apply different analysis methods depending on the theme of the seminar or meeting during the analysis. For example, in a technical seminar, the analysis unit applies an analysis method specialized in technical terms. In addition, the analysis unit can also apply an analysis method specialized in financial terms in a financial meeting. In addition, the analysis unit can apply an analysis method specialized in education-related terms in an educational seminar. In this way, the accuracy of the analysis is improved by applying an analysis method depending on the theme of the seminar or meeting. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that inputs the theme of the seminar or meeting and applies an analysis method.

[0074] The analysis unit can estimate the user's emotion and adjust the display order of the analysis results based on the estimated user's emotion. For example, when the user is nervous, the analysis unit displays important words first. Also, when the user is relaxed, the analysis unit can display detailed analysis results sequentially. Also, when the user is in a hurry, the analysis unit can preferentially display analysis results that focus on the main points. In this way, by adjusting the display order of the analysis results based on the user's emotion, the user can preferentially check important information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can perform morphological analysis using an AI model that receives the user's emotion data as input and adjusts the display order of the analysis results.

[0075] The analysis unit can prioritize analysis of region-specific terms in consideration of the geographical location information of the user during analysis. For example, when the user is in Japan, the analysis unit prioritizes analysis of Japan-specific terms. In addition, when the user is in the United States, the analysis unit can also prioritize analysis of America-specific terms. In addition, when the user is in Europe, the analysis unit can also prioritize analysis of Europe-specific terms. In this way, analysis of region-specific terms is prioritized by considering the geographical location information of the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that takes the geographical location information of the user as input and prioritizes analysis of region-specific terms.

[0076] During the analysis, the analysis unit can analyze the user's social media activity and prioritize analysis of related terms. For example, the analysis unit prioritizes analysis of terms frequently used by the user on X (formerly Twitter). The analysis unit can also prioritize analysis of terms related to topics in which the user is interested on Facebook. The analysis unit can also prioritize analysis of industry terms in which the user is interested on LinkedIn. In this way, analysis of related terms is prioritized by analyzing the user's social media activity. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that takes the user's social media activity as input and prioritizes analysis of related terms.

[0077] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is nervous, the display unit provides a simple and highly visible display method. In addition, when the user is relaxed, the display unit can also provide a display method including detailed information. In addition, when the user is in a hurry, the display unit can also provide a display method that focuses on the main points. This makes it easier for the user to understand information by adjusting the display method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can display information using an AI model that uses the user's emotion data as an input and adjusts the display method.

[0078] The display unit can select the optimal display method by referring to the user's past click history at the time of display. For example, the display unit highlights words that the user has frequently clicked in the past. The display unit can also preferentially display information related to topics of interest from the user's past click history. The display unit can also analyze the user's past click history and provide an optimal display layout. As a result, the optimal display method is selected by referring to the user's past click history. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that inputs the user's past click history and selects the optimal display method.

[0079] The display unit can apply different display styles depending on the theme of the seminar or meeting when displaying. For example, in a technical seminar, the display unit applies a style that highlights technical terms. In addition, the display unit can also apply a style that highlights stock prices and economic indicators in a financial meeting. In addition, the display unit can also apply a style that highlights education-related terms in an educational seminar. In this way, the visibility of information is improved by applying a display style depending on the theme of the seminar or meeting. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display information using an AI model that uses the theme of the seminar or meeting as an input and applies a display style.

[0080] The display unit can estimate the user's emotion and determine the priority of the display contents based on the estimated user's emotion. For example, when the user is nervous, the display unit displays important information first. Also, when the user is relaxed, the display unit can sequentially display detailed information. Also, when the user is in a hurry, the display unit can preferentially display information that covers the main points. As a result, important information is preferentially displayed by determining the priority of the display contents based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can display information using an AI model that inputs the user's emotion data and determines the priority of the display contents.

[0081] The display unit can select the optimal display method in consideration of the user's device information at the time of display. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. In addition, when the user is using a desktop, the display unit can also provide a display method including detailed information. In this way, the optimal display method is selected by considering the user's device information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display information using an AI model that inputs the user's device information and selects the optimal display method.

[0082] The display unit can analyze the user's social media activity and display related information preferentially when displaying. For example, the display unit highlights terms that the user frequently uses on X (formerly Twitter). The display unit can also preferentially display information related to topics in which the user is interested on Facebook. The display unit can also preferentially display industry information in which the user is interested on LinkedIn. This allows related information to be displayed preferentially by analyzing the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that uses the user's social media activity as an input and preferentially displays related information.

[0083] The explanation unit can estimate the user's emotion and adjust the way of expressing the explanation based on the estimated user's emotion. For example, when the user is nervous, the explanation unit provides a simple and highly visible explanation. In addition, when the user is relaxed, the explanation unit can also provide an explanation including detailed information. In addition, when the user is in a hurry, the explanation unit can also provide an explanation that covers the main points. This makes it easier for the user to understand information by adjusting the way of expressing the explanation based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the explanation unit can provide information using an AI model that inputs the user's emotion data and adjusts the way of expressing the explanation.

[0084] The explanation unit can adjust the level of detail of the explanation based on the importance of the clicked word during the explanation. For example, the explanation unit provides a detailed explanation in the case of an important word. In addition, the explanation unit can also provide a concise explanation in the case of a general word. In addition, the explanation unit can provide a detailed explanation including related background information in the case of technical terms. This makes it easier for the user to obtain the necessary information by adjusting the level of detail of the explanation based on the importance of the clicked word. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that uses the importance of the clicked word as an input and adjusts the level of detail of the explanation.

[0085] The explanation unit can apply different explanation algorithms depending on the category of the clicked word when explaining. For example, in the case of technical terms, the explanation unit provides an explanation including technical background information. In addition, in the case of financial terms, the explanation unit can also provide an explanation including the current market situation. In addition, in the case of educational terms, the explanation unit can also provide an explanation based on educational theory and practice. This makes it easier for the user to understand the information by applying the explanation algorithm depending on the category of the clicked word. Part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that uses the category of the clicked word as input and applies the explanation algorithm.

[0086] The explanation unit can estimate the user's emotion and adjust the length of the explanation based on the estimated user's emotion. For example, when the user is in a hurry, the explanation unit provides a short and to the point explanation. In addition, when the user is relaxed, the explanation unit can also provide a detailed explanation. In addition, when the user is excited, the explanation unit can also provide an explanation with a visually stimulating effect. This makes it easier for the user to understand information by adjusting the length of the explanation based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the explanation unit can provide information using an AI model that inputs the user's emotion data and adjusts the length of the explanation.

[0087] The explanation unit can determine the priority of the explanation based on the frequency of use of the clicked word during the explanation. For example, the explanation unit provides a detailed explanation for a frequently used word. Also, the explanation unit can provide a concise explanation for a commonly used word. Also, the explanation unit can provide a detailed explanation including related background information for technical terms. In this way, the priority of the explanation can be determined based on the frequency of use of the clicked word, so that the user can preferentially obtain important information. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that inputs the frequency of use of the clicked word and determines the priority of the explanation.

[0088] The explanation unit can adjust the order of explanations based on the relevance of the clicked word during explanation. For example, in the case of an important word, the explanation unit provides the explanation first. In addition, in the case of a general word, the explanation unit can also provide the explanation later. In addition, in the case of a technical term, the explanation unit can preferentially provide an explanation including related background information. In this way, by adjusting the order of explanations based on the relevance of the clicked word, the user can efficiently obtain related information. Part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI, or may be performed without using AI. For example, the explanation unit can provide information using an AI model that uses the relevance of the clicked word as an input and adjusts the order of explanations.

[0089] The information providing unit can estimate the user's emotion and adjust the type of information to be provided based on the estimated user's emotion. For example, when the user is nervous, the information providing unit provides simple and highly visible information. In addition, when the user is relaxed, the information providing unit can also provide detailed information. In addition, when the user is in a hurry, the information providing unit can also provide information that covers the main points. This makes it easier for the user to obtain the information he or she needs by adjusting the type of information to be provided based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit can provide information using an AI model that inputs the user's emotion data and adjusts the type of information to be provided.

[0090] When providing information, the information providing unit can select optimal information by referring to the past usage history of the clicked word. For example, the information providing unit preferentially provides information related to words that the user has frequently clicked in the past. The information providing unit can also provide information related to topics of interest from the user's past usage history. The information providing unit can also analyze the user's past usage history and provide optimal information. As a result, optimal information is selected by referring to the past usage history of the clicked word. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs the past usage history of the clicked word and selects optimal information.

[0091] The information providing unit can apply different information providing methods according to the category of the clicked word when providing information. For example, in the case of technical terms, the information providing unit can provide technical background information and related research papers. In addition, in the case of financial terms, the information providing unit can also provide the current market situation and related news articles. In addition, in the case of educational terms, the information providing unit can also provide information based on educational theory and practice. In this way, by applying the information providing method according to the category of the clicked word, the user can easily understand the information. Part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that uses the category of the clicked word as input and applies the information providing method.

[0092] The information providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. For example, when the user is nervous, the information providing unit provides important information first. Also, when the user is relaxed, the information providing unit can provide detailed information sequentially. Also, when the user is in a hurry, the information providing unit can provide information with a focus on the main points preferentially. As a result, important information is provided preferentially by determining the priority of information to be provided based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit can provide information using an AI model that inputs the user's emotion data and determines the priority of information to be provided.

[0093] The information providing unit can provide optimal information in consideration of the geographical distribution of the clicked word when providing information. For example, when the user is in Japan, the information providing unit can provide information specific to Japan preferentially. Also, when the user is in America, the information providing unit can also provide information specific to America preferentially. Also, when the user is in Europe, the information providing unit can also provide information specific to Europe preferentially. In this way, optimal information is provided by considering the geographical distribution of the clicked word. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs the geographical distribution of the clicked word and provides optimal information.

[0094] The information providing unit can improve the accuracy of the information by referring to related literature of the clicked word when providing the information. The information providing unit can provide the information by referring to, for example, the latest research paper related to the clicked word. The information providing unit can also provide the information by referring to a news article related to the clicked word. The information providing unit can also provide the information by referring to a specialized book related to the clicked word. As a result, the accuracy of the information is improved by referring to related literature of the clicked word. A part or all of the above-mentioned processing in the information providing unit can be performed, for example, using AI or can be performed without using AI. For example, the information providing unit can provide the information by using an AI model that uses related literature of the clicked word as an input and improves the accuracy of the information.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The analysis unit can optimize the analysis algorithm by referring to the user's past search history. For example, the analysis unit preferentially analyzes words that the user frequently searched in the past. The analysis unit can also preferentially analyze words related to a specific field from the user's past search history. Furthermore, the analysis unit can analyze the user's past search history and preferentially analyze words related to topics of interest. In this way, the analysis algorithm is optimized by referring to the user's past search history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that uses the user's past search history as input and optimizes the analysis algorithm.

[0097] The analysis unit can estimate the user's emotion and adjust the display order of the analysis results based on the estimated user's emotion. For example, if the user is nervous, important words are displayed first. In addition, the analysis unit can also display detailed analysis results sequentially when the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can preferentially display analysis results that focus on the main points. In this way, by adjusting the display order of the analysis results based on the user's emotion, the user can preferentially check important information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can perform morphological analysis using an AI model that inputs the user's emotion data and adjusts the display order of the analysis results.

[0098] The display unit can select the optimal display method by taking into account the device information of the user. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method that includes detailed information. In this way, the optimal display method is selected by taking into account the device information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can display information using an AI model that inputs the device information of the user and selects the optimal display method.

[0099] The explanation unit can apply different explanation algorithms depending on the category of the clicked word. For example, in the case of technical terms, an explanation including technical background information is provided. In addition, in the case of financial terms, the explanation unit can also provide an explanation including the current market situation. Furthermore, in the case of educational terms, the explanation unit can also provide an explanation based on educational theory and practice. This makes it easier for the user to understand the information by applying the explanation algorithm depending on the category of the clicked word. Some or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI or may be performed without using AI. For example, the explanation unit can provide information using an AI model that takes the category of the clicked word as input and applies the explanation algorithm.

[0100] The information providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. For example, if the user is nervous, important information is provided first. Also, if the user is relaxed, the information providing unit can provide detailed information sequentially. Furthermore, if the user is in a hurry, the information providing unit can preferentially provide information that covers the main points. As a result, important information is provided preferentially by determining the priority of information to be provided based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit can provide information using an AI model that inputs the user's emotion data and determines the priority of information to be provided.

[0101] During the analysis, the analysis unit can prioritize analysis of region-specific terms in consideration of the geographical location information of the user. For example, when the user is in Japan, the analysis unit prioritizes analysis of Japan-specific terms. In addition, when the user is in the United States, the analysis unit can also prioritize analysis of United States-specific terms. Furthermore, when the user is in Europe, the analysis unit can also prioritize analysis of Europe-specific terms. In this way, analysis of region-specific terms is prioritized by considering the geographical location information of the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform morphological analysis using an AI model that takes the geographical location information of the user as input and prioritizes analysis of region-specific terms.

[0102] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is nervous, the display unit provides a simple and highly visible display method. In addition, when the user is relaxed, the display unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can also provide a display method that focuses on the main points. This makes it easier for the user to understand information by adjusting the display method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can display information using an AI model that uses the user's emotion data as input and adjusts the display method.

[0103] The explanation unit can determine the priority of the explanation based on the frequency of use of the clicked word during the explanation. For example, in the case of a frequently used word, a detailed explanation is provided. In addition, in the case of a commonly used word, the explanation unit can also provide a concise explanation. Furthermore, in the case of technical terms, the explanation unit can also provide a detailed explanation including related background information. In this way, by determining the priority of the explanation based on the frequency of use of the clicked word, the user can preferentially obtain important information. Part or all of the above-mentioned processing in the explanation unit may be performed, for example, using AI or may be performed without using AI. For example, the explanation unit can provide information using an AI model that inputs the frequency of use of the clicked word and determines the priority of the explanation.

[0104] The information providing unit can improve the accuracy of the information by referring to related literature of the clicked word when providing the information. For example, the information providing unit can provide the information by referring to the latest research paper related to the clicked word. The information providing unit can also provide the information by referring to a news article related to the clicked word. Furthermore, the information providing unit can also provide the information by referring to a specialized book related to the clicked word. As a result, the accuracy of the information is improved by referring to the related literature of the clicked word. A part or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI or may be performed without using AI. For example, the information providing unit can provide the information using an AI model that uses the related literature of the clicked word as an input and improves the accuracy of the information.

[0105] The explanation unit can estimate the user's emotion and adjust the way of expressing the explanation based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible explanation is provided. In addition, if the user is relaxed, the explanation unit can also provide an explanation including detailed information. Furthermore, if the user is in a hurry, the explanation unit can also provide an explanation that covers the main points. This makes it easier for the user to understand information by adjusting the way of expressing the explanation based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the explanation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the explanation unit can provide information using an AI model that inputs the user's emotion data and adjusts the way of expressing the explanation.

[0106] The process flow of the second embodiment will be briefly described below.

[0107] Step 1: The analysis unit performs morphological analysis on the transcription data generated during online seminars and online meetings. Morphological analysis is performed using specific tools and technologies, such as MeCab and Juman. The analysis unit performs morphological analysis on the transcription data and divides each word. Step 2: The display unit makes the words analyzed by the analysis unit clickable. Specific methods for making the words clickable include setting a link or defining a clickable area. For example, the display unit displays the analyzed words as hyperlinks so that the user can click them. Step 3: The explanation unit explains and analyzes the word clicked by the display unit. Specific contents of the explanation and analysis include explanation using a dictionary database and analysis using statistical analysis. For example, the explanation unit obtains the meaning of the clicked word from the dictionary database and displays it to the user. The explanation unit also provides information based on the context of the clicked word. For example, when the word stock price is clicked, current stock price information is displayed. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can perform explanation and analysis using an AI model that takes the clicked word as input and outputs explanation and analysis.

[0108] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0110] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0111] For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process of morphological analysis of the transcription data generated in an online seminar or an online meeting is performed by the specific processing unit 290 of the data processing device 12. The display unit is realized by, for example, the control unit 46A of the smart device 14, and displays the analyzed words as clickable links. The explanation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and explains and analyzes the clicked word. The explanation unit is also realized by, for example, the control unit 46A of the smart device 14, and provides information based on the meaning and context of the clicked word. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0115] 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.

[0116] 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 the voice according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0118] 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 performed in a secure state.

[0119] Fig. 4 shows an example of 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.

[0120] 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.

[0121] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0125] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0126] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process of morphological analysis of the transcription data generated in an online seminar or an online meeting is performed by the specific processing unit 290 of the data processing device 12. The display unit is realized by, for example, the control unit 46A of the smart glasses 214, and displays the analyzed words as clickable links. The explanation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and explains and analyzes the clicked word. The explanation unit is also realized by, for example, the control unit 46A of the smart glasses 214, and provides information based on the meaning and context of the clicked word. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] 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 performed in a secure state.

[0135] 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.

[0136] 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.

[0137] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 a voice indicating a user input for 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.

[0141] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process of morphological analysis of the transcription data generated in an online seminar or online meeting is performed by the specific processing unit 290 of the data processing device 12. The display unit is realized by, for example, the control unit 46A of the headset type terminal 314, and displays the analyzed words as clickable links. The explanation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and explains and analyzes the clicked word. The explanation unit is also realized by, for example, the control unit 46A of the headset type terminal 314, and provides information based on the meaning and context of the clicked word. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0147] 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.

[0148] 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 the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0150] 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 performed in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0152] 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.

[0153] 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.

[0154] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0156] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] 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 a voice indicating a user input for 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.

[0158] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0160] For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the process of morphologically analyzing the transcription data generated in an online seminar or online meeting is performed by the specific processing unit 290 of the data processing device 12. The display unit is realized by, for example, the control unit 46A of the robot 414, and displays the analyzed words as clickable links. The explanation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and explains and analyzes the clicked word. The explanation unit is also realized by, for example, the control unit 46A of the robot 414, and provides information based on the meaning and context of the clicked word. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0162] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0164] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0166] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0168] 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.

[0169] 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 Universal Serial Bus (USB) 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.

[0170] In addition, 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 upon request from the data processing device 12.

[0171] 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.

[0172] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0173] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0174] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0175] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0177] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0179] (Appendix 1) an analysis unit that performs morphological analysis on the transcription data; a display unit that makes the words analyzed by the analysis unit clickable; an explanation unit that explains or analyzes a word clicked on by the display unit; Equipped A system characterized by: (Appendix 2) The explanation section includes: Explains and analyzes clicked words 2. The system of claim 1, (Appendix 3) The explanation section includes: Providing contextual information 2. The system of claim 1, (Appendix 4) The explanation section includes: An information providing unit that provides at least one of stock price information, explanations of technical terms, and related news articles 2. The system of claim 1, (Appendix 5) The analysis unit is Uses general morphological analysis techniques 2. The system of claim 1, (Appendix 6) The analysis unit is Estimate user's emotions and adjust the accuracy of morphological analysis based on the estimated user's emotions. 2. The system of claim 1, (Appendix 7) The analysis unit is When analyzing, optimize the analysis algorithm by referring to the user's past click history. 2. The system of claim 1, (Appendix 8) The analysis unit is During analysis, apply different analysis methods depending on the theme of the seminar or meeting. 2. The system of claim 1, (Appendix 9) The analysis unit is Infer user emotions and adjust the display order of analysis results based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The analysis unit is During parsing, prioritize region-specific terms based on the user's geographic location 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, the system analyzes the user's social media activity and prioritizes relevant terms. 2. The system of claim 1, (Appendix 12) The display unit is Estimate user's emotions and adjust the display method based on the estimated user's emotions 2. The system of claim 1, (Appendix 13) The display unit is When displaying, the optimal display method is selected by referring to the user's past click history. 2. The system of claim 1, (Appendix 14) The display unit is When viewing, apply different display styles depending on the theme of the seminar or meeting 2. The system of claim 1, (Appendix 15) The display unit is Estimate the user's emotions and prioritize the content to be displayed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 16) The display unit is When displaying, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 17) The display unit is When displayed, the app analyzes the user's social media activity and prioritizes the display of relevant information. 2. The system of claim 1, (Appendix 18) The explanation section includes: Infer the user's emotions and adjust the way the explanation is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 19) The explanation section includes: When explaining, adjust the detail of the explanation based on the importance of the clicked word 2. The system of claim 1, (Appendix 20) The explanation section includes: During explanation, apply different explanation algorithms depending on the category of the clicked word 2. The system of claim 1, (Appendix 21) The explanation section includes: Infer user sentiment and adjust the length of explanations based on the estimated user sentiment 2. The system of claim 1, (Appendix 22) The explanation section includes: During explanation, prioritize explanations based on frequency of clicked words 2. The system of claim 1, (Appendix 23) The explanation section includes: When describing something, adjust the order of the description based on the relevance of the clicked word 2. The system of claim 1, (Appendix 24) The information providing unit is Inferring a user's emotions and adjusting the type of information provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 25) The information providing unit is When providing information, the most appropriate information is selected by referring to the past usage history of the clicked word. 2. The system of claim 1, (Appendix 26) The information providing unit is When providing information, apply different information presentation methods depending on the category of the clicked word. 2. The system of claim 1, (Appendix 27) The information providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The information providing unit is When providing information, provide the most appropriate information by taking into account the geographical distribution of the clicked words. 2. The system of claim 1, (Appendix 29) The information providing unit is When providing information, the accuracy of the information is improved by referring to related literature of the clicked word. 2. The system of claim 1, [Explanation of symbols]

[0180] 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. an analysis unit that performs morphological analysis on the transcription data; a display unit that makes the words analyzed by the analysis unit clickable; An explanation unit that explains or analyzes the word clicked by the display unit. A system characterized by:

2. The explanation section includes: Providing contextual information 2. The system of claim 1 .

3. The explanation section includes: An information providing unit that provides at least one of stock price information, explanations of technical terms, and related news articles 2. The system of claim 1 .

4. The analysis unit is Estimate user's emotions and adjust the accuracy of morphological analysis based on the estimated user's emotions.

2. The system of claim 1 .

5. The analysis unit is When analyzing, optimize the analysis algorithm by referring to the user's past click history.

2. The system of claim 1 .

6. The analysis unit is During analysis, apply different analysis methods depending on the theme of the seminar or meeting.

2. The system of claim 1 .

7. The analysis unit is Infer user emotions and adjust the display order of analysis results based on the estimated user emotions 2. The system of claim 1 .

8. The analysis unit is During parsing, prioritize region-specific terms based on the user's geographic location 2. The system of claim 1 .

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