system

Abstract

The system according to the embodiment aims to automatically explain technical terms and abbreviations used in sports broadcasts, allowing viewers to enjoy the broadcasts more. [Solution] A system according to an embodiment includes a reception unit, an extraction unit, and an explanation unit. The reception unit inputs an identification code. The extraction unit extracts technical terms or abbreviations from the commentary based on the identification code input by the reception unit. The explanation unit explains the meaning of the technical terms or abbreviations extracted by the extraction unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the technical terms and abbreviations used in sports broadcasts are difficult to understand, making it difficult for viewers to enjoy the content.

[0005] The system according to the embodiment aims to automatically explain technical terms and abbreviations used in sports broadcasts, allowing viewers to enjoy the broadcasts more. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an extraction unit, and an explanation unit. The reception unit inputs an identification code. The extraction unit extracts technical terms or abbreviations from the commentary based on the identification code input by the reception unit. The explanation unit explains the meaning of the technical terms or abbreviations extracted by the extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically explain technical terms and abbreviations used in sports broadcasts, allowing viewers to enjoy the broadcasts more. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sports commentary system according to an embodiment of the present invention allows a user to input an identification code, and AI extracts technical terms and abbreviations from the commentary and provides explanations of their meanings. The sports commentary system allows a user to input an identification code into a smartphone app, and AI extracts technical terms and abbreviations from the commentary and provides explanations of their meanings, allowing more people to enjoy sports broadcasts. For example, in a sports commentary system, a user inputs an identification code. The user simply inputs the identification code announced during the broadcast. Next, the sports commentary system uses the input identification code to extract technical terms and abbreviations from the commentary. The AI ​​analyzes the audio and text data of the commentary to identify technical terms and abbreviations. For example, if the term "offside" appears in a soccer game, it provides an explanation of its meaning. The extracted terms and abbreviations are explained in sync with the commentary of the game or race being watched by the user. For example, when a user is watching a soccer game, the meaning of the term "offside" is explained the moment it appears in the commentary. This allows a wider audience to enjoy sports broadcasts. This allows even those unfamiliar with sports to enjoy the live broadcast while understanding the meaning of technical terms and abbreviations. Furthermore, since the commentary is provided through a smartphone app, users can easily access it. For example, by simply entering an identification code into the smartphone app, the commentary will be automatically displayed. In this way, more people can enjoy live sports broadcasts.

[0029] A sports broadcast commentary system according to an embodiment includes a reception unit, an extraction unit, and a commentary unit. The reception unit receives an identification code input by a user. The identification code may be, for example, numbers, letters, or a specific format, but is not limited to these examples. For example, the reception unit allows a user to input the identification code announced during the broadcast into a smartphone app. The extraction unit uses AI to extract technical terms and abbreviations from the commentary based on the identification code input by the reception unit. The extraction unit, for example, analyzes audio data and text data of the commentary to identify technical terms and abbreviations. For example, the extraction unit can convert the audio data of the commentary into text data using speech recognition technology and extract technical terms and abbreviations from the text data. The extraction unit can also identify technical terms and abbreviations from the text data using natural language processing technology. For example, the extraction unit receives audio data as input and uses an AI model that outputs text data to generate text data from the audio data and extract technical terms and abbreviations from the text data. The commentary unit uses AI to explain the meanings of the technical terms and abbreviations extracted by the extraction unit. For example, the commentary unit provides commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. For example, if the user is watching a soccer game, the commentary unit can provide an explanation of the meaning of the term "offside" the moment it appears in the live commentary. As a result, the sports broadcast commentary system according to the embodiment extracts technical terms and abbreviations based on the identification codes and provides explanations of their meanings, allowing more people to enjoy sports broadcasts.

[0030] The commentary unit includes a providing unit that provides the commentary to the user. The providing unit uses AI to provide the commentary to the user. The providing unit includes, for example, a display unit that displays the commentary content on the user's smartphone. The providing unit can provide the commentary to the user by, for example, audio, text, video, or other methods. For example, the providing unit can display the commentary content in text format. The providing unit can also provide the commentary content in audio format. Furthermore, the providing unit can also provide the commentary content in video format. This makes it easier for the user to understand the commentary provided to the user.

[0031] The providing unit includes a display unit that displays the commentary content on the user's smartphone. The display unit uses AI to display the commentary content on the user's smartphone. The display unit can display the commentary content in the form of, for example, a pop-up, a notification, a dedicated app, or the like. For example, the display unit can display the commentary content in a pop-up format. The display unit can also display the commentary content in a notification format. Furthermore, the display unit can display the commentary content in a dedicated app. This allows the user to easily check the commentary by displaying the commentary content on their smartphone.

[0032] The extraction unit analyzes the commentary audio data or text data to identify technical terms or abbreviations. The extraction unit uses AI to analyze the commentary audio data or text data to identify technical terms or abbreviations. Examples of audio data and text data include, but are not limited to, real-time audio, recorded data, and text logs. The extraction unit can, for example, convert the commentary audio data into text data using speech recognition technology and extract technical terms and abbreviations from the text data. The extraction unit can also identify technical terms and abbreviations from the text data using natural language processing technology. For example, the extraction unit uses an AI model that inputs audio data and outputs text data to generate text data from the audio data and extracts technical terms and abbreviations from the text data. This allows technical terms and abbreviations to be accurately identified by analyzing the audio data and text data.

[0033] The commentary unit provides commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. The commentary unit uses AI to provide commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. Specific methods and standards for synchronization include, but are not limited to, real-time synchronization and synchronization with a fixed time delay. For example, when a user is watching a soccer game, the commentary unit can provide an explanation of the meaning of the term "offside" the moment the term appears in the commentary. The commentary unit can also provide commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. For example, the commentary unit can provide commentary in real time in synchronization with the live commentary. The commentary unit can also provide synchronization with a fixed time delay. This allows commentary to be synchronized with the live commentary, making it easier for users to understand in real time.

[0034] The reception unit analyzes the user's past identification code input history and selects an input method. The reception unit uses AI to analyze the user's past identification code input history and selects the optimal input method. Input methods include, but are not limited to, voice input, text input, and touch input. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of identification codes entered by the user in the past and provide an auto-completion function to reduce the effort required for input. The reception unit can also predict and suggest identification codes to be used in specific time periods based on the user's past input history. In this way, the optimal input method can be provided for the user by analyzing the past input history.

[0035] The reception unit performs filtering based on the user's current viewing status and areas of interest when the identification code is input. The reception unit uses AI to perform filtering based on the user's current viewing status and areas of interest when the identification code is input. Methods for identifying the viewing status and areas of interest include, but are not limited to, viewing history, survey results, and user profiles. For example, if the user is watching a soccer game, the reception unit displays only identification codes related to soccer. Furthermore, if the user is interested in a particular player, the reception unit can also preferentially display identification codes related to that player. Furthermore, the reception unit can suggest related identification codes based on the history of games the user has watched in the past. In this way, by filtering based on the viewing status and areas of interest, it is possible to provide identification codes that are highly relevant to the user.

[0036] The reception unit selects the optimal input means depending on the user's input method when inputting an identification code. The reception unit uses AI to select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting an identification code. Examples of input means include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit may preferentially provide a voice recognition function. Furthermore, if the user prefers text input, the reception unit may preferentially provide keyboard input. Furthermore, if the user prefers image input, the reception unit may also provide a two-dimensional code (e.g., QR Code (registered trademark)) scanning function. This improves input convenience by selecting the optimal input means depending on the user's input method.

[0037] When inputting an identification code, the reception unit prioritizes inputting a highly relevant code in consideration of the user's geographical location information. When inputting an identification code, the reception unit uses AI to prioritize inputting a highly relevant code in consideration of the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, IP address, location information services, etc. For example, if the user is in a specific stadium, the reception unit may prioritize displaying an identification code related to the stadium. Furthermore, if the user is in a specific region, the reception unit may prioritize displaying an identification code related to the region. Furthermore, if the user is traveling, the reception unit may prioritize displaying an identification code related to the user's travel destination. In this way, by taking geographical location information into consideration, it is possible to provide an identification code that is highly relevant to the user.

[0038] The reception unit analyzes the user's social media activity when the identification code is input and inputs a related code. The reception unit uses AI to analyze the user's social media activity when the identification code is input and inputs a related code. Methods for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. The reception unit displays, for example, an identification code related to a location where the user checked in on social media. The reception unit can also analyze the content of the user's posts on social media and display a related identification code. The reception unit can also display a related identification code based on the activity of the user's friends on social media. In this way, an identification code that is highly relevant to the user can be provided by analyzing social media activity.

[0039] The reception unit customizes the input method by reflecting the user's past feedback when the identification code is input. The reception unit uses AI to customize the input method by reflecting the user's past feedback when the identification code is input. Methods for collecting past feedback include, but are not limited to, survey results, user comments, and evaluation data. For example, the reception unit preferentially provides input methods that the user has previously preferred. The reception unit can also avoid input methods that the user has previously been dissatisfied with. The reception unit can also suggest the optimal input method based on the user's past feedback. In this way, the optimal input method for the user can be provided by reflecting past feedback.

[0040] The extraction unit adjusts the level of detail of the extraction based on the importance of the commentary during extraction. The extraction unit uses AI to adjust the level of detail of the extraction based on the importance of the commentary during extraction. Methods for evaluating the importance of the commentary include, but are not limited to, the number of viewers, the scale of the event, and past data. For example, the extraction unit extracts detailed technical terms and abbreviations in the case of important matches or races. In addition, the extraction unit can extract only basic technical terms and abbreviations in the case of general matches or races. In addition, the extraction unit can prioritize extracting related technical terms and abbreviations in the case of specific events or highlight scenes. In this way, by adjusting the level of detail of the extraction based on the importance of the commentary, important information can be preferentially extracted.

[0041] The extraction unit applies different extraction algorithms depending on the category of the commentary during extraction. The extraction unit uses AI to apply different extraction algorithms depending on the category of the commentary during extraction. Types of extraction algorithms and application methods include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, in the case of a soccer game, the extraction unit may apply an extraction algorithm specialized for soccer. Furthermore, in the case of a baseball game, the extraction unit may apply an extraction algorithm specialized for baseball. Furthermore, in the case of a motorsport race, the extraction unit may apply an extraction algorithm specialized for motorsport. In this way, by applying an extraction algorithm according to the category, more appropriate technical terms and abbreviations can be extracted.

[0042] The extraction unit improves the accuracy of extraction by referring to the user's past extraction results during extraction. The extraction unit uses AI to improve the accuracy of extraction by referring to the user's past extraction results during extraction. Methods for improving the accuracy of extraction include, but are not limited to, using past data and introducing a feedback loop. For example, the extraction unit preferentially extracts technical terms and abbreviations that the user has previously preferred. The extraction unit can also avoid extraction results that the user has previously dissatisfied with. The extraction unit can also apply an optimal extraction algorithm based on the user's past extraction results. This improves the accuracy of extraction by referring to past extraction results.

[0043] The extraction unit determines the extraction priority based on the time of submission of the commentary at the time of extraction. The extraction unit uses AI to determine the extraction priority based on the time of submission of the commentary at the time of extraction. Evaluation criteria for submission time include, but are not limited to, for example, the submission date and time, the timing of the event, etc. For example, in the case of commentary of an important game or race, the extraction unit prioritizes extraction of terms and abbreviations that were submitted recently. In addition, in the case of commentary of a general game or race, the extraction unit can also extract terms and abbreviations that were submitted older. In addition, in the case of a specific event or highlight scene, the extraction unit can also prioritize extraction of terms and abbreviations that were submitted more recently. In this way, by determining the priority based on the time of submission, the latest information can be preferentially extracted.

[0044] The extraction unit adjusts the extraction order based on the relevance of the commentary during extraction. The extraction unit uses AI to adjust the extraction order based on the relevance of the commentary during extraction. Relevance evaluation criteria include, but are not limited to, co-occurrence relationships and topic models. For example, in the case of commentary on an important game or race, the extraction unit prioritizes extracting highly relevant terms and abbreviations. In addition, in the case of commentary on a general game or race, the extraction unit can also extract less relevant terms and abbreviations. In addition, in the case of specific events or highlight scenes, the extraction unit can prioritize extracting highly relevant terms and abbreviations. As a result, by adjusting the extraction order based on relevance, important information can be preferentially extracted.

[0045] The extraction unit adjusts the use of technical terms during extraction according to the user's level of expertise. The extraction unit uses AI to adjust the use of technical terms during extraction according to the user's level of expertise. Methods for evaluating the level of expertise include, but are not limited to, survey results, past behavioral history, and user profiles. For example, if the user is a beginner, the extraction unit may preferentially extract basic technical terms. If the user is an intermediate user, the extraction unit may also extract detailed technical terms. If the user is an advanced user, the extraction unit may also preferentially extract technical terms and abbreviations. This allows the provision of information that is easy for users to understand by adjusting the use of technical terms according to the level of expertise.

[0046] The explanation unit adjusts the level of detail of the explanation based on the importance of the term or abbreviation when providing the explanation. The explanation unit uses AI to adjust the level of detail of the explanation based on the importance of the term or abbreviation when providing the explanation. Criteria for evaluating the importance of a term or abbreviation include, but are not limited to, frequency, influence, and user interest. For example, the explanation unit provides detailed explanations for important terms and abbreviations. The explanation unit can also provide basic explanations for general terms and abbreviations. The explanation unit can also provide detailed explanations for terms and abbreviations related to specific events or highlight scenes. In this way, important information can be provided to the user by adjusting the level of detail of the explanation based on importance.

[0047] The commentary unit applies different commentary algorithms depending on the category of the term or abbreviation when providing commentary. The commentary unit uses AI to apply different commentary algorithms depending on the category of the term or abbreviation when providing commentary. Types and application methods of commentary algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms, for example. For example, the commentary unit applies a commentary algorithm specialized for soccer in the case of soccer terms. Furthermore, the commentary unit can apply a commentary algorithm specialized for baseball in the case of baseball terms. Furthermore, the commentary unit can also apply a commentary algorithm specialized for motorsports in the case of motorsport terms. In this way, by applying a commentary algorithm according to the category, more appropriate commentary can be provided.

[0048] The commentary unit improves the accuracy of the commentary by referring to the user's past commentary results when providing commentary. The commentary unit uses AI to improve the accuracy of the commentary by referring to the user's past commentary results when providing commentary. Methods for improving the accuracy of the commentary include, but are not limited to, using past data and introducing a feedback loop. For example, the commentary unit preferentially provides commentary methods that the user has previously preferred. The commentary unit can also avoid commentary methods that the user has previously dissatisfied with. The commentary unit can also apply an optimal commentary algorithm based on the user's past commentary results. This improves the accuracy of the commentary by referring to past commentary results.

[0049] The commentary unit determines the priority of commentary based on the time of submission of terms and abbreviations during commentary. The commentary unit uses AI to determine the priority of commentary based on the time of submission of terms and abbreviations during commentary. Evaluation criteria for submission time include, but are not limited to, the submission date and time, the timing of the event, and the like. For example, in the case of terms and abbreviations related to important matches or races, the commentary unit prioritizes the most recently submitted terms and abbreviations. In addition, in the case of terms and abbreviations related to general matches or races, the commentary unit can also provide commentary on older submitted terms and abbreviations. In addition, in the case of specific events or highlight scenes, the commentary unit can prioritize the most recently submitted terms and abbreviations. In this way, by determining the priority based on the time of submission, the most recent information can be given priority in commentary.

[0050] The commentary unit adjusts the order of commentary based on the relevance of terms and abbreviations during commentary. The commentary unit uses AI to adjust the order of commentary based on the relevance of terms and abbreviations during commentary. Relevance evaluation criteria include, but are not limited to, co-occurrence relationships and topic models. For example, the commentary unit prioritizes explanation of highly relevant terms and abbreviations for important matches or races. The commentary unit can also explain less relevant terms and abbreviations for general matches or races. The commentary unit can also prioritize explanation of highly relevant terms and abbreviations for specific events or highlight scenes. This allows important information to be prioritized by adjusting the order of commentary based on relevance.

[0051] The explanation unit adjusts the use of technical terms in the explanation according to the user's level of expertise during the explanation. The explanation unit uses AI to adjust the use of technical terms in the explanation according to the user's level of expertise during the explanation. Methods for evaluating the level of expertise include, but are not limited to, survey results, past behavioral history, and user profiles. For example, if the user is a beginner, the explanation unit prioritizes explaining basic technical terms. Furthermore, if the user is an intermediate user, the explanation unit can explain detailed technical terms. Furthermore, if the user is an advanced user, the explanation unit can prioritize explaining technical terms and abbreviations. In this way, by adjusting the use of technical terms according to the level of expertise, explanations that are easy for the user to understand can be provided.

[0052] The providing unit adjusts the level of detail of the commentary provided based on the importance of the commentary when providing the commentary. The providing unit uses AI to adjust the level of detail of the commentary provided based on the importance of the commentary when providing the commentary. Criteria for evaluating the importance of the commentary include, but are not limited to, frequency, influence, and user interest. For example, the providing unit provides detailed explanations for important terms and abbreviations. The providing unit can also provide basic explanations for general terms and abbreviations. The providing unit can also provide detailed explanations for terms and abbreviations related to specific events or highlight scenes. In this way, by adjusting the level of detail of the commentary provided based on importance, important information can be provided to the user.

[0053] The providing unit applies different provision algorithms depending on the category of the commentary when providing the commentary. The providing unit uses AI to apply different provision algorithms depending on the category of the commentary when providing the commentary. The types and application methods of the provision algorithms include, for example, machine learning algorithms and rule-based algorithms, but are not limited to these examples. For example, in the case of soccer terms, the providing unit applies a provision algorithm specialized for soccer. Furthermore, in the case of baseball terms, the providing unit can apply a provision algorithm specialized for baseball. Furthermore, in the case of motorsports terms, the providing unit can also apply a provision algorithm specialized for motorsports. In this way, by applying a provision algorithm according to the category, more appropriate commentary can be provided.

[0054] The providing unit improves the accuracy of the provision when providing the information by referring to the user's past provision results. The providing unit uses AI to improve the accuracy of the provision when providing the information by referring to the user's past provision results. Methods for improving the accuracy of the provision include, but are not limited to, using past data and introducing a feedback loop. For example, the providing unit preferentially provides a provision method that the user has previously preferred. The providing unit can also avoid a provision method that the user has previously been dissatisfied with. The providing unit can also apply an optimal provision algorithm based on the user's past provision results. In this way, the accuracy of the provision is improved by referring to the past provision results.

[0055] The providing unit determines the priority of provision based on the time of submission of the commentary at the time of provision. The providing unit uses AI to determine the priority of provision based on the time of submission of the commentary at the time of provision. Evaluation criteria for the time of submission include, but are not limited to, for example, the date and time of submission, the timing of the event, etc. For example, in the case of commentary on an important match or race, the providing unit provides commentary that has been submitted more recently with priority. In addition, in the case of commentary on a general match or race, the providing unit can also provide commentary that has been submitted older with priority. In addition, in the case of commentary on a specific event or highlight scene, the providing unit can also provide commentary that has been submitted more recently with priority. In this way, by determining the priority based on the time of submission, the latest information can be provided preferentially.

[0056] The providing unit adjusts the order of provision based on the relevance of the commentary when providing the commentary. The providing unit uses AI to adjust the order of provision based on the relevance of the commentary when providing the commentary. Relevance evaluation criteria include, but are not limited to, co-occurrence relationships, topic models, etc. For example, in the case of commentary on an important game or race, the providing unit prioritizes providing highly relevant commentary. In addition, in the case of commentary on a general game or race, the providing unit can also provide less relevant commentary. In addition, in the case of a specific event or highlight scene, the providing unit can prioritize providing highly relevant commentary. In this way, by adjusting the order of provision based on relevance, important information can be provided preferentially.

[0057] The providing unit adjusts the use of the provided terminology according to the user's level of expertise when providing the information. The providing unit uses AI to adjust the use of the provided terminology according to the user's level of expertise when providing the information. Methods for evaluating the level of expertise include, but are not limited to, survey results, past behavioral history, and user profiles. For example, if the user is a beginner, the providing unit may provide basic terminology preferentially. Furthermore, if the user is an intermediate user, the providing unit may provide detailed terminology. Furthermore, if the user is an advanced user, the providing unit may provide technical terms and abbreviations preferentially. This allows the provision of explanations that are easy for the user to understand by adjusting the use of terminology according to the level of expertise.

[0058] The display unit selects the optimal display method by referring to the user's past operation history when displaying. The display unit uses AI to select the optimal display method by referring to the user's past operation history when displaying. Methods of collecting operation history include, but are not limited to, click history, scroll history, and tap history. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also avoid a display method that the user has previously been dissatisfied with. The display unit can also suggest the optimal display method based on the user's past operation history. In this way, the optimal display method for the user can be provided by referring to the past operation history.

[0059] The display unit customizes the display content according to the user's current task when displaying the information. The display unit uses AI to customize the display content according to the user's current task when displaying the information. Methods for identifying the current task include, but are not limited to, active applications, work content, etc. For example, if the user is watching a game, the display unit can prioritize displaying information related to the game. Furthermore, if the user is watching a race, the display unit can prioritize displaying information related to the race. Furthermore, if the user is interested in a particular athlete, the display unit can prioritize displaying information related to that athlete. In this way, by customizing the display content according to the current task, it is possible to provide information that is highly relevant to the user.

[0060] The display unit selects the optimal display method when displaying information by taking into account the user's device information. The display unit uses AI to select the optimal display method when displaying information by taking into account the user's device information. Methods for acquiring device information include, but are not limited to, the device type, OS, and screen size. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method for the user to be provided by taking into account the device information.

[0061] The display unit selects the optimal display method when displaying information by taking into account the user's device information. The display unit uses AI to select the optimal display method when displaying information by taking into account the user's device information. Methods for acquiring device information include, but are not limited to, the device type, OS, and screen size. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method for the user to be provided by taking into account the device information.

[0062] The display unit makes the display content multilingual according to the user's language setting when displaying. The display unit uses AI to make the display content multilingual according to the user's language setting when displaying. Methods for acquiring the language setting include, but are not limited to, user settings, device settings, and application settings. The display unit automatically sets the display content based on the language setting of the user's device, for example. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide a display that is easy for the user to understand by supporting multiple languages ​​according to the language setting.

[0063] The display unit analyzes the user's social media activity and provides related information when displaying the information. The display unit uses AI to analyze the user's social media activity and provides related information when displaying the information. Methods for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. The display unit provides, for example, information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide information that is highly relevant to the user.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit analyzes the user's past identification code input history and selects an input method. The reception unit uses AI to analyze the user's past identification code input history and selects the optimal input method. Input methods include, but are not limited to, voice input, text input, and touch input. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of identification codes entered by the user in the past and provide an auto-completion function to reduce the effort required for input. The reception unit can also predict and suggest identification codes to be used in specific time periods based on the user's past input history. In this way, the optimal input method can be provided for the user by analyzing the past input history.

[0066] The reception unit performs filtering based on the user's current viewing status and areas of interest when the identification code is input. The reception unit uses AI to perform filtering based on the user's current viewing status and areas of interest when the identification code is input. Methods for identifying the viewing status and areas of interest include, but are not limited to, viewing history, survey results, and user profiles. For example, if the user is watching a soccer game, the reception unit displays only identification codes related to soccer. Furthermore, if the user is interested in a particular player, the reception unit can also preferentially display identification codes related to that player. Furthermore, the reception unit can suggest related identification codes based on the history of games the user has watched in the past. In this way, by filtering based on the viewing status and areas of interest, it is possible to provide identification codes that are highly relevant to the user.

[0067] The reception unit selects the optimal input means depending on the user's input method when inputting an identification code. The reception unit uses AI to select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting an identification code. Examples of input means include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit may preferentially provide a voice recognition function. Furthermore, if the user prefers text input, the reception unit may preferentially provide keyboard input. Furthermore, if the user prefers image input, the reception unit may also provide a QR code scanning function. This improves input convenience by selecting the optimal input means depending on the user's input method.

[0068] The extraction unit adjusts the level of detail of the extraction based on the importance of the commentary during extraction. The extraction unit uses AI to adjust the level of detail of the extraction based on the importance of the commentary during extraction. Methods for evaluating the importance of the commentary include, but are not limited to, the number of viewers, the scale of the event, and past data. For example, the extraction unit extracts detailed technical terms and abbreviations in the case of important matches or races. In addition, the extraction unit can extract only basic technical terms and abbreviations in the case of general matches or races. In addition, the extraction unit can prioritize extracting related technical terms and abbreviations in the case of specific events or highlight scenes. In this way, by adjusting the level of detail of the extraction based on the importance of the commentary, important information can be preferentially extracted.

[0069] The extraction unit applies different extraction algorithms depending on the category of the commentary during extraction. The extraction unit uses AI to apply different extraction algorithms depending on the category of the commentary during extraction. Types of extraction algorithms and application methods include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, in the case of a soccer game, the extraction unit may apply an extraction algorithm specialized for soccer. Furthermore, in the case of a baseball game, the extraction unit may apply an extraction algorithm specialized for baseball. Furthermore, in the case of a motorsport race, the extraction unit may apply an extraction algorithm specialized for motorsport. In this way, by applying an extraction algorithm according to the category, more appropriate technical terms and abbreviations can be extracted.

[0070] The extraction unit improves the accuracy of extraction by referring to the user's past extraction results during extraction. The extraction unit uses AI to improve the accuracy of extraction by referring to the user's past extraction results during extraction. Methods for improving the accuracy of extraction include, but are not limited to, using past data and introducing a feedback loop. For example, the extraction unit preferentially extracts technical terms and abbreviations that the user has previously preferred. The extraction unit can also avoid extraction results that the user has previously dissatisfied with. The extraction unit can also apply an optimal extraction algorithm based on the user's past extraction results. This improves the accuracy of extraction by referring to past extraction results.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit allows the user to input an identification code. The identification code may include, but is not limited to, numbers, letters, a specific format, etc. The reception unit allows the user to input the identification code announced via live broadcast into the smartphone app. Step 2: The extraction unit uses AI to extract technical terms and abbreviations from the commentary based on the identification code entered by the reception unit. The extraction unit, for example, analyzes the audio data and text data of the commentary to identify technical terms and abbreviations. It can convert the audio data into text data using speech recognition technology and extract technical terms and abbreviations from the text data. It can also use natural language processing technology to identify technical terms and abbreviations from the text data. Step 3: The explanation unit uses AI to explain the meaning of the technical terms and abbreviations extracted by the extraction unit. For example, the explanation unit explains the meaning of the extracted terms and abbreviations in sync with the live commentary of the game or race the user is watching. For example, if a user is watching a soccer game, the meaning of the word "offside" can be explained the moment it appears in the commentary.

[0073] (Example 2) A sports commentary system according to an embodiment of the present invention allows a user to input an identification code, and AI extracts technical terms and abbreviations from the commentary and provides explanations of their meanings. The sports commentary system allows a user to input an identification code into a smartphone app, and AI extracts technical terms and abbreviations from the commentary and provides explanations of their meanings, allowing more people to enjoy sports broadcasts. For example, in a sports commentary system, a user inputs an identification code. The user simply inputs the identification code announced during the broadcast. Next, the sports commentary system uses the input identification code to extract technical terms and abbreviations from the commentary. The AI ​​analyzes the audio and text data of the commentary to identify technical terms and abbreviations. For example, if the term "offside" appears in a soccer game, it provides an explanation of its meaning. The extracted terms and abbreviations are explained in sync with the commentary of the game or race being watched by the user. For example, when a user is watching a soccer game, the meaning of the term "offside" is explained the moment it appears in the commentary. This allows a wider audience to enjoy sports broadcasts. This allows even those unfamiliar with sports to enjoy the live broadcast while understanding the meaning of technical terms and abbreviations. Furthermore, since the commentary is provided through a smartphone app, users can easily access it. For example, by simply entering an identification code into the smartphone app, the commentary will be automatically displayed. In this way, more people can enjoy live sports broadcasts.

[0074] A sports broadcast commentary system according to an embodiment includes a reception unit, an extraction unit, and a commentary unit. The reception unit receives an identification code input by a user. The identification code may be, for example, numbers, letters, or a specific format, but is not limited to these examples. For example, the reception unit allows a user to input the identification code announced during the broadcast into a smartphone app. The extraction unit uses AI to extract technical terms and abbreviations from the commentary based on the identification code input by the reception unit. The extraction unit, for example, analyzes audio data and text data of the commentary to identify technical terms and abbreviations. For example, the extraction unit can convert the audio data of the commentary into text data using speech recognition technology and extract technical terms and abbreviations from the text data. The extraction unit can also identify technical terms and abbreviations from the text data using natural language processing technology. For example, the extraction unit receives audio data as input and uses an AI model that outputs text data to generate text data from the audio data and extract technical terms and abbreviations from the text data. The commentary unit uses AI to explain the meanings of the technical terms and abbreviations extracted by the extraction unit. For example, the commentary unit provides commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. For example, if the user is watching a soccer game, the commentary unit can provide an explanation of the meaning of the term "offside" the moment it appears in the live commentary. As a result, the sports broadcast commentary system according to the embodiment extracts technical terms and abbreviations based on the identification codes and provides explanations of their meanings, allowing more people to enjoy sports broadcasts.

[0075] The commentary unit includes a providing unit that provides the commentary to the user. The providing unit uses AI to provide the commentary to the user. The providing unit includes, for example, a display unit that displays the commentary content on the user's smartphone. The providing unit can provide the commentary to the user by, for example, audio, text, video, or other methods. For example, the providing unit can display the commentary content in text format. The providing unit can also provide the commentary content in audio format. Furthermore, the providing unit can also provide the commentary content in video format. This makes it easier for the user to understand the commentary provided to the user.

[0076] The providing unit includes a display unit that displays the commentary content on the user's smartphone. The display unit uses AI to display the commentary content on the user's smartphone. The display unit can display the commentary content in the form of, for example, a pop-up, a notification, a dedicated app, or the like. For example, the display unit can display the commentary content in a pop-up format. The display unit can also display the commentary content in a notification format. Furthermore, the display unit can display the commentary content in a dedicated app. This allows the user to easily check the commentary by displaying the commentary content on their smartphone.

[0077] The extraction unit analyzes the commentary audio data or text data to identify technical terms or abbreviations. The extraction unit uses AI to analyze the commentary audio data or text data to identify technical terms or abbreviations. Examples of audio data and text data include, but are not limited to, real-time audio, recorded data, and text logs. The extraction unit can, for example, convert the commentary audio data into text data using speech recognition technology and extract technical terms and abbreviations from the text data. The extraction unit can also identify technical terms and abbreviations from the text data using natural language processing technology. For example, the extraction unit uses an AI model that inputs audio data and outputs text data to generate text data from the audio data and extracts technical terms and abbreviations from the text data. This allows technical terms and abbreviations to be accurately identified by analyzing the audio data and text data.

[0078] The commentary unit provides commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. The commentary unit uses AI to provide commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. Specific methods and standards for synchronization include, but are not limited to, real-time synchronization and synchronization with a fixed time delay. For example, when a user is watching a soccer game, the commentary unit can provide an explanation of the meaning of the term "offside" the moment the term appears in the commentary. The commentary unit can also provide commentary on the meanings of the extracted terms and abbreviations in synchronization with the live commentary of the game or race being watched by the user. For example, the commentary unit can provide commentary in real time in synchronization with the live commentary. The commentary unit can also provide synchronization with a fixed time delay. This allows commentary to be synchronized with the live commentary, making it easier for users to understand in real time.

[0079] The reception unit estimates the user's emotions and adjusts the timing of inputting the identification code based on the estimated user emotions. The reception unit estimates the user's emotions using AI and adjusts the timing of inputting the identification code based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. For example, if the user is excited, the reception unit may frequently display notifications prompting the user to input the identification code. Furthermore, if the user is relaxed, the reception unit may display notifications prompting the user to input the identification code less frequently. Furthermore, if the user is concentrating, the reception unit may temporarily stop the notifications prompting the user to input the identification code and redisplay them at an appropriate time. In this way, the input timing can be adjusted according to the user's emotions, allowing the user to input the identification code at a more appropriate time.

[0080] The reception unit analyzes the user's past identification code input history and selects an input method. The reception unit uses AI to analyze the user's past identification code input history and selects the optimal input method. Input methods include, but are not limited to, voice input, text input, and touch input. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of identification codes entered by the user in the past and provide an auto-completion function to reduce the effort required for input. The reception unit can also predict and suggest identification codes to be used in specific time periods based on the user's past input history. In this way, the optimal input method can be provided for the user by analyzing the past input history.

[0081] The reception unit performs filtering based on the user's current viewing status and areas of interest when the identification code is input. The reception unit uses AI to perform filtering based on the user's current viewing status and areas of interest when the identification code is input. Methods for identifying the viewing status and areas of interest include, but are not limited to, viewing history, survey results, and user profiles. For example, if the user is watching a soccer game, the reception unit displays only identification codes related to soccer. Furthermore, if the user is interested in a particular player, the reception unit can also preferentially display identification codes related to that player. Furthermore, the reception unit can suggest related identification codes based on the history of games the user has watched in the past. In this way, by filtering based on the viewing status and areas of interest, it is possible to provide identification codes that are highly relevant to the user.

[0082] The reception unit selects the optimal input means depending on the user's input method when inputting an identification code. The reception unit uses AI to select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting an identification code. Examples of input means include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit may preferentially provide a voice recognition function. Also, if the user prefers text input, the reception unit may preferentially provide keyboard input. Also, if the user prefers image input, the reception unit may provide a two-dimensional code (e.g., QR code) scanning function. This improves input convenience by selecting the optimal input means depending on the user's input method.

[0083] The reception unit estimates the user's emotions and determines the priority of identification codes to be input based on the estimated user emotions. The reception unit estimates the user's emotions using AI and determines the priority of identification codes to be input based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. For example, the reception unit prioritizes displaying important identification codes when the user is excited. Furthermore, the reception unit can also display less relevant identification codes when the user is relaxed. Furthermore, the reception unit can display only the most relevant identification codes when the user is concentrating. In this way, by determining the priority of identification codes based on the user's emotions, important identification codes can be input preferentially.

[0084] When inputting an identification code, the reception unit prioritizes inputting a highly relevant code in consideration of the user's geographical location information. When inputting an identification code, the reception unit uses AI to prioritize inputting a highly relevant code in consideration of the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, IP address, location information services, etc. For example, if the user is in a specific stadium, the reception unit may prioritize displaying an identification code related to the stadium. Furthermore, if the user is in a specific region, the reception unit may prioritize displaying an identification code related to the region. Furthermore, if the user is traveling, the reception unit may prioritize displaying an identification code related to the user's travel destination. In this way, by taking geographical location information into consideration, it is possible to provide an identification code that is highly relevant to the user.

[0085] The reception unit analyzes the user's social media activity when the identification code is input and inputs a related code. The reception unit uses AI to analyze the user's social media activity when the identification code is input and inputs a related code. Methods for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. The reception unit displays, for example, an identification code related to a location where the user checked in on social media. The reception unit can also analyze the content of the user's posts on social media and display a related identification code. The reception unit can also display a related identification code based on the activity of the user's friends on social media. In this way, an identification code that is highly relevant to the user can be provided by analyzing social media activity.

[0086] The reception unit customizes the input method by reflecting the user's past feedback when the identification code is input. The reception unit uses AI to customize the input method by reflecting the user's past feedback when the identification code is input. Methods for collecting past feedback include, but are not limited to, survey results, user comments, and evaluation data. For example, the reception unit preferentially provides input methods that the user has previously preferred. The reception unit can also avoid input methods that the user has previously been dissatisfied with. The reception unit can also suggest the optimal input method based on the user's past feedback. In this way, the optimal input method for the user can be provided by reflecting past feedback.

[0087] The extraction unit estimates the user's emotions and adjusts the terminology or abbreviation extraction method based on the estimated user emotions. The extraction unit uses AI to estimate the user's emotions and adjusts the terminology or abbreviation extraction method based on the estimated user emotions. Criteria for adjusting the extraction method include, but are not limited to, algorithm parameter adjustment and filtering conditions. For example, when the user is excited, the extraction unit prioritizes extracting important terminology or abbreviations. Furthermore, when the user is relaxed, the extraction unit can also extract less relevant terminology or abbreviations. Furthermore, when the user is focused, the extraction unit can extract only the most relevant terminology or abbreviations. This allows the extraction method to be adjusted based on the user's emotions, thereby extracting more appropriate terminology and abbreviations.

[0088] The extraction unit adjusts the level of detail of the extraction based on the importance of the commentary during extraction. The extraction unit uses AI to adjust the level of detail of the extraction based on the importance of the commentary during extraction. Methods for evaluating the importance of the commentary include, but are not limited to, the number of viewers, the scale of the event, and past data. For example, the extraction unit extracts detailed technical terms and abbreviations in the case of important matches or races. In addition, the extraction unit can extract only basic technical terms and abbreviations in the case of general matches or races. In addition, the extraction unit can prioritize extracting related technical terms and abbreviations in the case of specific events or highlight scenes. In this way, by adjusting the level of detail of the extraction based on the importance of the commentary, important information can be preferentially extracted.

[0089] The extraction unit applies different extraction algorithms depending on the category of the commentary during extraction. The extraction unit uses AI to apply different extraction algorithms depending on the category of the commentary during extraction. Types of extraction algorithms and application methods include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, in the case of a soccer game, the extraction unit may apply an extraction algorithm specialized for soccer. Furthermore, in the case of a baseball game, the extraction unit may apply an extraction algorithm specialized for baseball. Furthermore, in the case of a motorsport race, the extraction unit may apply an extraction algorithm specialized for motorsport. In this way, by applying an extraction algorithm according to the category, more appropriate technical terms and abbreviations can be extracted.

[0090] The extraction unit improves the accuracy of extraction by referring to the user's past extraction results during extraction. The extraction unit uses AI to improve the accuracy of extraction by referring to the user's past extraction results during extraction. Methods for improving the accuracy of extraction include, but are not limited to, using past data and introducing a feedback loop. For example, the extraction unit preferentially extracts technical terms and abbreviations that the user has previously preferred. The extraction unit can also avoid extraction results that the user has previously dissatisfied with. The extraction unit can also apply an optimal extraction algorithm based on the user's past extraction results. This improves the accuracy of extraction by referring to past extraction results.

[0091] The extraction unit estimates the user's emotions and determines the priority of terms or abbreviations to be extracted based on the estimated user emotions. The extraction unit uses AI to estimate the user's emotions and determines the priority of terms and abbreviations to be extracted based on the estimated user emotions. Criteria for determining the priority of terms and abbreviations include, but are not limited to, importance, frequency, and user interest. For example, when the user is excited, the extraction unit preferentially extracts important terms and abbreviations. Furthermore, when the user is relaxed, the extraction unit can also extract less relevant terms and abbreviations. Furthermore, when the user is focused, the extraction unit can extract only the most relevant terms and abbreviations. In this way, by determining the priority based on the user's emotions, important terms and abbreviations can be preferentially extracted.

[0092] The extraction unit determines the extraction priority based on the time of submission of the commentary at the time of extraction. The extraction unit uses AI to determine the extraction priority based on the time of submission of the commentary at the time of extraction. Evaluation criteria for submission time include, but are not limited to, for example, the submission date and time, the timing of the event, etc. For example, in the case of commentary of an important game or race, the extraction unit prioritizes extraction of terms and abbreviations that were submitted recently. In addition, in the case of commentary of a general game or race, the extraction unit can also extract terms and abbreviations that were submitted older. In addition, in the case of a specific event or highlight scene, the extraction unit can also prioritize extraction of terms and abbreviations that were submitted more recently. In this way, by determining the priority based on the time of submission, the latest information can be preferentially extracted.

[0093] The extraction unit adjusts the extraction order based on the relevance of the commentary during extraction. The extraction unit uses AI to adjust the extraction order based on the relevance of the commentary during extraction. Relevance evaluation criteria include, but are not limited to, co-occurrence relationships and topic models. For example, in the case of commentary on an important game or race, the extraction unit prioritizes extracting highly relevant terms and abbreviations. In addition, in the case of commentary on a general game or race, the extraction unit can also extract less relevant terms and abbreviations. In addition, in the case of specific events or highlight scenes, the extraction unit can prioritize extracting highly relevant terms and abbreviations. As a result, by adjusting the extraction order based on relevance, important information can be preferentially extracted.

[0094] The extraction unit adjusts the use of technical terms during extraction according to the user's level of expertise. The extraction unit uses AI to adjust the use of technical terms during extraction according to the user's level of expertise. Methods for evaluating the level of expertise include, but are not limited to, survey results, past behavioral history, and user profiles. For example, if the user is a beginner, the extraction unit may preferentially extract basic technical terms. If the user is an intermediate user, the extraction unit may also extract detailed technical terms. If the user is an advanced user, the extraction unit may also preferentially extract technical terms and abbreviations. This allows the provision of information that is easy for users to understand by adjusting the use of technical terms according to the level of expertise.

[0095] The commentary unit estimates the user's emotions and adjusts the way the commentary is expressed based on the estimated user emotions. The commentary unit uses AI to estimate the user's emotions and adjusts the way the commentary is expressed based on the estimated user emotions. Criteria for adjusting the way the commentary is expressed include, but are not limited to, the language used, the level of detail, and the media used. For example, if the user is excited, the commentary unit provides a concise and to-the-point commentary. Furthermore, if the user is relaxed, the commentary unit can provide a detailed commentary. Furthermore, if the user is focused, the commentary unit can provide a more specialized commentary. This allows the provision of more appropriate commentary by adjusting the way the commentary is expressed based on the user's emotions.

[0096] The explanation unit adjusts the level of detail of the explanation based on the importance of the term or abbreviation when providing the explanation. The explanation unit uses AI to adjust the level of detail of the explanation based on the importance of the term or abbreviation when providing the explanation. Criteria for evaluating the importance of a term or abbreviation include, but are not limited to, frequency, influence, and user interest. For example, the explanation unit provides detailed explanations for important terms and abbreviations. The explanation unit can also provide basic explanations for general terms and abbreviations. The explanation unit can also provide detailed explanations for terms and abbreviations related to specific events or highlight scenes. In this way, important information can be provided to the user by adjusting the level of detail of the explanation based on importance.

[0097] The commentary unit applies different commentary algorithms depending on the category of the term or abbreviation when providing commentary. The commentary unit uses AI to apply different commentary algorithms depending on the category of the term or abbreviation when providing commentary. Types and application methods of commentary algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms, for example. For example, the commentary unit applies a commentary algorithm specialized for soccer in the case of soccer terms. Furthermore, the commentary unit can apply a commentary algorithm specialized for baseball in the case of baseball terms. Furthermore, the commentary unit can also apply a commentary algorithm specialized for motorsports in the case of motorsport terms. In this way, by applying a commentary algorithm according to the category, more appropriate commentary can be provided.

[0098] The commentary unit improves the accuracy of the commentary by referring to the user's past commentary results when providing commentary. The commentary unit uses AI to improve the accuracy of the commentary by referring to the user's past commentary results when providing commentary. Methods for improving the accuracy of the commentary include, but are not limited to, using past data and introducing a feedback loop. For example, the commentary unit preferentially provides commentary methods that the user has previously preferred. The commentary unit can also avoid commentary methods that the user has previously dissatisfied with. The commentary unit can also apply an optimal commentary algorithm based on the user's past commentary results. This improves the accuracy of the commentary by referring to past commentary results.

[0099] The commentary unit estimates the user's emotions and adjusts the length of the commentary based on the estimated user emotions. The commentary unit uses AI to estimate the user's emotions and adjusts the length of the commentary based on the estimated user emotions. Criteria for adjusting the length of the commentary include, but are not limited to, the number of characters, time, and amount of information. For example, when the user is excited, the commentary unit provides a concise and to-the-point commentary. Furthermore, when the user is relaxed, the commentary unit can provide a detailed commentary. Furthermore, when the user is focused, the commentary unit can provide a more specialized commentary. This allows for the provision of a more appropriate commentary by adjusting the length of the commentary based on the user's emotions.

[0100] The commentary unit determines the priority of commentary based on the time of submission of terms and abbreviations during commentary. The commentary unit uses AI to determine the priority of commentary based on the time of submission of terms and abbreviations during commentary. Evaluation criteria for submission time include, but are not limited to, the submission date and time, the timing of the event, and the like. For example, in the case of terms and abbreviations related to important matches or races, the commentary unit prioritizes the most recently submitted terms and abbreviations. In addition, in the case of terms and abbreviations related to general matches or races, the commentary unit can also provide commentary on older submitted terms and abbreviations. In addition, in the case of specific events or highlight scenes, the commentary unit can prioritize the most recently submitted terms and abbreviations. In this way, by determining the priority based on the time of submission, the most recent information can be given priority in commentary.

[0101] The commentary unit adjusts the order of commentary based on the relevance of terms and abbreviations during commentary. The commentary unit uses AI to adjust the order of commentary based on the relevance of terms and abbreviations during commentary. Relevance evaluation criteria include, but are not limited to, co-occurrence relationships and topic models. For example, the commentary unit prioritizes explanation of highly relevant terms and abbreviations for important matches or races. The commentary unit can also explain less relevant terms and abbreviations for general matches or races. The commentary unit can also prioritize explanation of highly relevant terms and abbreviations for specific events or highlight scenes. This allows important information to be prioritized by adjusting the order of commentary based on relevance.

[0102] The explanation unit adjusts the use of technical terms in the explanation according to the user's level of expertise during the explanation. The explanation unit uses AI to adjust the use of technical terms in the explanation according to the user's level of expertise during the explanation. Methods for evaluating the level of expertise include, but are not limited to, survey results, past behavioral history, and user profiles. For example, if the user is a beginner, the explanation unit prioritizes explaining basic technical terms. Furthermore, if the user is an intermediate user, the explanation unit can explain detailed technical terms. Furthermore, if the user is an advanced user, the explanation unit can prioritize explaining technical terms and abbreviations. In this way, by adjusting the use of technical terms according to the level of expertise, explanations that are easy for the user to understand can be provided.

[0103] The providing unit estimates the user's emotions and adjusts the method of providing commentary based on the estimated user emotions. The providing unit uses AI to estimate the user's emotions and adjusts the method of providing commentary based on the estimated user emotions. Criteria for adjusting the method of commentary include, but are not limited to, audio, text, video, etc. For example, when the user is excited, the providing unit provides a concise and to-the-point commentary. Furthermore, when the user is relaxed, the providing unit can provide a detailed commentary. Furthermore, when the user is focused, the providing unit can provide a more specialized commentary. In this way, by adjusting the method of commentary based on the user's emotions, more appropriate commentary can be provided.

[0104] The providing unit adjusts the level of detail of the commentary provided based on the importance of the commentary when providing the commentary. The providing unit uses AI to adjust the level of detail of the commentary provided based on the importance of the commentary when providing the commentary. Criteria for evaluating the importance of the commentary include, but are not limited to, frequency, influence, and user interest. For example, the providing unit provides detailed explanations for important terms and abbreviations. The providing unit can also provide basic explanations for general terms and abbreviations. The providing unit can also provide detailed explanations for terms and abbreviations related to specific events or highlight scenes. In this way, by adjusting the level of detail of the commentary provided based on importance, important information can be provided to the user.

[0105] The providing unit applies different provision algorithms depending on the category of the commentary when providing the commentary. The providing unit uses AI to apply different provision algorithms depending on the category of the commentary when providing the commentary. The types and application methods of the provision algorithms include, for example, machine learning algorithms and rule-based algorithms, but are not limited to these examples. For example, in the case of soccer terms, the providing unit applies a provision algorithm specialized for soccer. Furthermore, in the case of baseball terms, the providing unit can apply a provision algorithm specialized for baseball. Furthermore, in the case of motorsports terms, the providing unit can also apply a provision algorithm specialized for motorsports. In this way, by applying a provision algorithm according to the category, more appropriate commentary can be provided.

[0106] The providing unit improves the accuracy of the provision when providing the information by referring to the user's past provision results. The providing unit uses AI to improve the accuracy of the provision when providing the information by referring to the user's past provision results. Methods for improving the accuracy of the provision include, but are not limited to, using past data and introducing a feedback loop. For example, the providing unit preferentially provides a provision method that the user has previously preferred. The providing unit can also avoid a provision method that the user has previously been dissatisfied with. The providing unit can also apply an optimal provision algorithm based on the user's past provision results. In this way, the accuracy of the provision is improved by referring to the past provision results.

[0107] The providing unit estimates the user's emotions and determines the priority of commentary to be provided based on the estimated user emotions. The providing unit estimates the user's emotions using AI and determines the priority of commentary to be provided based on the estimated user emotions. Criteria for determining the priority of commentary include, but are not limited to, importance, frequency, and user interest, for example. For example, when the user is excited, the providing unit provides important commentary with priority. Furthermore, when the user is relaxed, the providing unit can also provide less relevant commentary. Furthermore, when the user is focused, the providing unit can provide only the most relevant commentary. In this way, by determining the priority based on the user's emotions, important commentary can be provided with priority.

[0108] The providing unit determines the priority of provision based on the time of submission of the commentary at the time of provision. The providing unit uses AI to determine the priority of provision based on the time of submission of the commentary at the time of provision. Evaluation criteria for the time of submission include, but are not limited to, for example, the date and time of submission, the timing of the event, etc. For example, in the case of commentary on an important match or race, the providing unit provides commentary that has been submitted more recently with priority. In addition, in the case of commentary on a general match or race, the providing unit can also provide commentary that has been submitted older with priority. In addition, in the case of commentary on a specific event or highlight scene, the providing unit can also provide commentary that has been submitted more recently with priority. In this way, by determining the priority based on the time of submission, the latest information can be provided preferentially.

[0109] The providing unit adjusts the order of provision based on the relevance of the commentary when providing the commentary. The providing unit uses AI to adjust the order of provision based on the relevance of the commentary when providing the commentary. Relevance evaluation criteria include, but are not limited to, co-occurrence relationships, topic models, etc. For example, in the case of commentary on an important game or race, the providing unit prioritizes providing highly relevant commentary. In addition, in the case of commentary on a general game or race, the providing unit can also provide less relevant commentary. In addition, in the case of a specific event or highlight scene, the providing unit can prioritize providing highly relevant commentary. In this way, by adjusting the order of provision based on relevance, important information can be provided preferentially.

[0110] The providing unit adjusts the use of the provided terminology according to the user's level of expertise when providing the information. The providing unit uses AI to adjust the use of the provided terminology according to the user's level of expertise when providing the information. Methods for evaluating the level of expertise include, but are not limited to, survey results, past behavioral history, and user profiles. For example, if the user is a beginner, the providing unit may provide basic terminology preferentially. Furthermore, if the user is an intermediate user, the providing unit may provide detailed terminology. Furthermore, if the user is an advanced user, the providing unit may provide technical terms and abbreviations preferentially. This allows the provision of explanations that are easy for the user to understand by adjusting the use of terminology according to the level of expertise.

[0111] The display unit estimates the user's emotions and adjusts the display method based on the estimated user emotions. The display unit uses AI to estimate the user's emotions and adjusts the display method based on the estimated user emotions. Criteria for adjusting the display method include, but are not limited to, font size, color, layout, etc. For example, when the user is excited, the display unit provides a concise and to-the-point display method. Furthermore, when the user is relaxed, the display unit can provide a detailed display method. Furthermore, when the user is concentrating, the display unit can provide a specialized display method. In this way, by adjusting the display method based on the user's emotions, a more appropriate display can be provided.

[0112] The display unit selects the optimal display method by referring to the user's past operation history when displaying. The display unit uses AI to select the optimal display method by referring to the user's past operation history when displaying. Methods of collecting operation history include, but are not limited to, click history, scroll history, and tap history. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also avoid a display method that the user has previously been dissatisfied with. The display unit can also suggest the optimal display method based on the user's past operation history. In this way, the optimal display method for the user can be provided by referring to the past operation history.

[0113] The display unit customizes the display content according to the user's current task when displaying the information. The display unit uses AI to customize the display content according to the user's current task when displaying the information. Methods for identifying the current task include, but are not limited to, active applications, work content, etc. For example, if the user is watching a game, the display unit can prioritize displaying information related to the game. Furthermore, if the user is watching a race, the display unit can prioritize displaying information related to the race. Furthermore, if the user is interested in a particular athlete, the display unit can prioritize displaying information related to that athlete. In this way, by customizing the display content according to the current task, it is possible to provide information that is highly relevant to the user.

[0114] The display unit selects the optimal display method when displaying information by taking into account the user's device information. The display unit uses AI to select the optimal display method when displaying information by taking into account the user's device information. Methods for acquiring device information include, but are not limited to, the device type, OS, and screen size. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method for the user to be provided by taking into account the device information.

[0115] The display unit estimates the user's emotions and adjusts the displayed operation procedures based on the estimated user emotions. The display unit estimates the user's emotions using AI and adjusts the displayed operation procedures based on the estimated user emotions. Criteria for adjusting the operation procedures include, but are not limited to, the number of steps, the order of operations, and the difficulty of operations. For example, when the user is excited, the display unit provides concise and to-the-point operation procedures. Furthermore, when the user is relaxed, the display unit can provide detailed operation procedures. Furthermore, when the user is concentrating, the display unit can provide specialized operation procedures. In this way, by adjusting the operation procedures based on the user's emotions, more appropriate operation procedures can be provided.

[0116] The display unit selects the optimal display method when displaying information by taking into account the user's device information. The display unit uses AI to select the optimal display method when displaying information by taking into account the user's device information. Methods for acquiring device information include, but are not limited to, the device type, OS, and screen size. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method for the user to be provided by taking into account the device information.

[0117] The display unit makes the display content multilingual according to the user's language setting when displaying. The display unit uses AI to make the display content multilingual according to the user's language setting when displaying. Methods for acquiring the language setting include, but are not limited to, user settings, device settings, and application settings. The display unit automatically sets the display content based on the language setting of the user's device, for example. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide a display that is easy for the user to understand by supporting multiple languages ​​according to the language setting.

[0118] The display unit analyzes the user's social media activity and provides related information when displaying the information. The display unit uses AI to analyze the user's social media activity and provides related information when displaying the information. Methods for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. The display unit provides, for example, information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide information that is highly relevant to the user. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, extraction unit, explanation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and allows the user to input an identification code. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts technical terms and abbreviations from the commentary. The explanation unit is realized by the specific processing unit 290 of the data processing device 12 and explains the meanings of the extracted terms and abbreviations. The provision unit is realized by the control unit 46A of the smart device 14 and displays the explanation content on the user's smartphone. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, extraction unit, explanation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and allows a user to input an identification code. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts technical terms and abbreviations from the commentary. The explanation unit is realized by the specific processing unit 290 of the data processing device 12 and explains the meanings of the extracted terms and abbreviations. The provision unit is realized by the control unit 46A of the smart glasses 214 and displays the explanation content on the user's smartphone. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, extraction unit, explanation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and allows the user to input an identification code. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts technical terms and abbreviations from the commentary. The explanation unit is realized by the specific processing unit 290 of the data processing device 12 and explains the meanings of the extracted terms and abbreviations. The provision unit is realized by the control unit 46A of the headset type terminal 314 and displays the explanation content on the user's smartphone. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, extraction unit, explanation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and allows the user to input an identification code. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts technical terms and abbreviations from the commentary. The explanation unit is realized by the specific processing unit 290 of the data processing device 12 and explains the meanings of the extracted terms and abbreviations. The provision unit is realized by the control unit 46A of the robot 414 and displays the explanation content on the user's smartphone.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The reception unit analyzes the user's past identification code input history and selects an input method. The reception unit uses AI to analyze the user's past identification code input history and selects the optimal input method. Input methods include, but are not limited to, voice input, text input, and touch input. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of identification codes entered by the user in the past and provide an auto-completion function to reduce the effort required for input. The reception unit can also predict and suggest identification codes to be used in specific time periods based on the user's past input history. In this way, the optimal input method can be provided for the user by analyzing the past input history.

[0121] The reception unit performs filtering based on the user's current viewing status and areas of interest when the identification code is input. The reception unit uses AI to perform filtering based on the user's current viewing status and areas of interest when the identification code is input. Methods for identifying the viewing status and areas of interest include, but are not limited to, viewing history, survey results, and user profiles. For example, if the user is watching a soccer game, the reception unit displays only identification codes related to soccer. Furthermore, if the user is interested in a particular player, the reception unit can also preferentially display identification codes related to that player. Furthermore, the reception unit can suggest related identification codes based on the history of games the user has watched in the past. In this way, by filtering based on the viewing status and areas of interest, it is possible to provide identification codes that are highly relevant to the user.

[0122] The reception unit selects the optimal input means depending on the user's input method when inputting an identification code. The reception unit uses AI to select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting an identification code. Examples of input means include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit may preferentially provide a voice recognition function. Also, if the user prefers text input, the reception unit may preferentially provide keyboard input. Also, if the user prefers image input, the reception unit may provide a two-dimensional code (e.g., QR code) scanning function. This improves input convenience by selecting the optimal input means depending on the user's input method.

[0123] The reception unit estimates the user's emotions and adjusts the timing of inputting the identification code based on the estimated user emotions. The reception unit estimates the user's emotions using AI and adjusts the timing of inputting the identification code based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. For example, if the user is excited, the reception unit may frequently display notifications prompting the user to input the identification code. Furthermore, if the user is relaxed, the reception unit may display notifications prompting the user to input the identification code less frequently. Furthermore, if the user is concentrating, the reception unit may temporarily stop the notifications prompting the user to input the identification code and redisplay them at an appropriate time. In this way, the input timing can be adjusted according to the user's emotions, allowing the user to input the identification code at a more appropriate time.

[0124] The reception unit estimates the user's emotions and determines the priority of identification codes to be input based on the estimated user emotions. The reception unit estimates the user's emotions using AI and determines the priority of identification codes to be input based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. For example, the reception unit prioritizes displaying important identification codes when the user is excited. Furthermore, the reception unit can also display less relevant identification codes when the user is relaxed. Furthermore, the reception unit can display only the most relevant identification codes when the user is concentrating. In this way, by determining the priority of identification codes based on the user's emotions, important identification codes can be input preferentially.

[0125] The extraction unit adjusts the level of detail of the extraction based on the importance of the commentary during extraction. The extraction unit uses AI to adjust the level of detail of the extraction based on the importance of the commentary during extraction. Methods for evaluating the importance of the commentary include, but are not limited to, the number of viewers, the scale of the event, and past data. For example, the extraction unit extracts detailed technical terms and abbreviations in the case of important matches or races. In addition, the extraction unit can extract only basic technical terms and abbreviations in the case of general matches or races. In addition, the extraction unit can prioritize extracting related technical terms and abbreviations in the case of specific events or highlight scenes. In this way, by adjusting the level of detail of the extraction based on the importance of the commentary, important information can be preferentially extracted.

[0126] The extraction unit applies different extraction algorithms depending on the category of the commentary during extraction. The extraction unit uses AI to apply different extraction algorithms depending on the category of the commentary during extraction. Types of extraction algorithms and application methods include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, in the case of a soccer game, the extraction unit may apply an extraction algorithm specialized for soccer. Furthermore, in the case of a baseball game, the extraction unit may apply an extraction algorithm specialized for baseball. Furthermore, in the case of a motorsport race, the extraction unit may apply an extraction algorithm specialized for motorsport. In this way, by applying an extraction algorithm according to the category, more appropriate technical terms and abbreviations can be extracted.

[0127] The extraction unit estimates the user's emotions and adjusts the terminology or abbreviation extraction method based on the estimated user emotions. The extraction unit uses AI to estimate the user's emotions and adjusts the terminology or abbreviation extraction method based on the estimated user emotions. Criteria for adjusting the extraction method include, but are not limited to, algorithm parameter adjustment and filtering conditions. For example, when the user is excited, the extraction unit prioritizes extracting important terminology or abbreviations. Furthermore, when the user is relaxed, the extraction unit can also extract less relevant terminology or abbreviations. Furthermore, when the user is focused, the extraction unit can extract only the most relevant terminology or abbreviations. This allows the extraction method to be adjusted based on the user's emotions, thereby extracting more appropriate terminology and abbreviations.

[0128] The extraction unit improves the accuracy of extraction by referring to the user's past extraction results during extraction. The extraction unit uses AI to improve the accuracy of extraction by referring to the user's past extraction results during extraction. Methods for improving the accuracy of extraction include, but are not limited to, using past data and introducing a feedback loop. For example, the extraction unit preferentially extracts technical terms and abbreviations that the user has previously preferred. The extraction unit can also avoid extraction results that the user has previously dissatisfied with. The extraction unit can also apply an optimal extraction algorithm based on the user's past extraction results. This improves the accuracy of extraction by referring to past extraction results.

[0129] The extraction unit estimates the user's emotions and determines the priority of terms or abbreviations to be extracted based on the estimated user emotions. The extraction unit uses AI to estimate the user's emotions and determines the priority of terms and abbreviations to be extracted based on the estimated user emotions. Criteria for determining the priority of terms and abbreviations include, but are not limited to, importance, frequency, and user interest. For example, when the user is excited, the extraction unit preferentially extracts important terms and abbreviations. Furthermore, when the user is relaxed, the extraction unit can also extract less relevant terms and abbreviations. Furthermore, when the user is focused, the extraction unit can extract only the most relevant terms and abbreviations. In this way, by determining the priority based on the user's emotions, important terms and abbreviations can be preferentially extracted.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The reception unit allows the user to input an identification code. The identification code may include, but is not limited to, numbers, letters, a specific format, etc. The reception unit allows the user to input the identification code announced via live broadcast into the smartphone app. Step 2: The extraction unit uses AI to extract technical terms and abbreviations from the commentary based on the identification code entered by the reception unit. The extraction unit, for example, analyzes the audio data and text data of the commentary to identify technical terms and abbreviations. It can convert the audio data into text data using speech recognition technology and extract technical terms and abbreviations from the text data. It can also use natural language processing technology to identify technical terms and abbreviations from the text data. Step 3: The explanation unit uses AI to explain the meaning of the technical terms and abbreviations extracted by the extraction unit. For example, the explanation unit explains the meaning of the extracted terms and abbreviations in sync with the live commentary of the game or race the user is watching. For example, if a user is watching a soccer game, the meaning of the word "offside" can be explained the moment it appears in the commentary.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

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

[0139] The 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.

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

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

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

[0143] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] 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.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] 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.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0175] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0176] 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.

[0177] 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.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0187] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0188] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0189] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0192] 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.

[0193] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0194] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0195] 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.

[0196] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0197] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0198] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0200] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0201] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0202] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0203] [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting an identification code; an extraction unit that extracts technical terms or abbreviations from the commentary based on the identification code input by the reception unit; an explanation unit that explains the meaning of the technical terms or abbreviations extracted by the extraction unit; Equipped with A system characterized by:

2. The commentary section A providing unit that provides an explanation to the user is provided.

2. The system of claim 1.

3. The providing unit Equipped with a display unit that displays commentary content on the user's smartphone 3. The system of claim 2.

4. The extraction unit Analyzing the audio or text data of the commentary to identify technical terms or abbreviations 2. The system of claim 1.

5. The commentary section The meanings of extracted terms and abbreviations are explained in sync with the live commentary of the game or race the user is watching.

2. The system of claim 1.

6. The reception unit The user's emotions are estimated, and the timing of inputting the identification code is adjusted based on the estimated user's emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past identification code input history and select the input method 2. The system of claim 1.

8. The reception unit Filtering based on the user's current viewing habits and interests when an identification code is entered 2. The system of claim 1.

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