System

Abstract

An object of the system according to the embodiment is to efficiently record, summarize, and present statement contents in a meeting or daily life.SOLUTION: A system includes a recording part, an analysis part, a summary generation part, and a presentation part. The recording unit records speech in a meeting or daily life. The analysis unit analyzes the utterance content recorded by the recording unit. The summary generation unit summarizes the statement content analyzed by the analysis unit. The presentation unit presents the summary generated by the summary generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been sufficient in efficiently recording, summarizing, and presenting what is said in meetings and in everyday life, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently record, summarize, and present utterances made in meetings and in daily life. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, a summary generation unit, and a presentation unit. The recording unit records utterances made in meetings or in daily life. The analysis unit analyzes the utterances recorded by the recording unit. The summary generation unit summarizes the utterances analyzed by the analysis unit. The presentation unit presents the summary generated by the summary generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently record, summarize, and present utterances made in meetings and in daily life. [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) The smartphone app according to the embodiment of the present invention is a system that records speeches made in meetings and daily life, analyzes them using a generation AI, and presents summaries. This allows the smartphone app to efficiently manage the content of user speech and prevent users from repeating the same things.

[0029] A smartphone app according to an embodiment includes a recording unit, an analysis unit, a summary generation unit, and a presentation unit. The recording unit records utterances made in meetings or in daily life. For example, the recording unit records the utterances using a smartphone's microphone. The recording unit can also record with high audio quality by connecting an external microphone. The recording unit starts recording by pressing a recording start button. For example, the recording unit records the utterances using a smartphone's built-in microphone. The recording unit can also record with high audio quality by connecting an external microphone. The recording unit starts recording by pressing a recording start button. The analysis unit analyzes the utterances recorded by the recording unit. For example, the analysis unit converts the utterances into text data using voice recognition technology. The analysis unit can also extract keywords from the utterances. The analysis unit can also analyze emotions from the utterances. For example, the analysis unit converts the utterances into text data using voice recognition technology. The analysis unit can also extract keywords from the utterances. The analysis unit can also analyze emotions from the utterances. The summary generation unit summarizes the utterances analyzed by the analysis unit. For example, the summary generation unit summarizes the utterance content using a generation AI. The summary generation unit can also extract important points to create a summary. The summary generation unit can also save the summary of the utterance content. For example, the summary generation unit summarizes the utterance content using a generation AI. The summary generation unit can also extract important points to create a summary. The summary generation unit can also save the summary of the utterance content. The presentation unit presents the summary generated by the summary generation unit. For example, the presentation unit displays the summary on a smartphone screen. The presentation unit can also play the summary by audio. The presentation unit can also send the summary by email. For example, the presentation unit displays the summary on a smartphone screen. The presentation unit can also play the summary by audio. The presentation unit can also send the summary by email. This allows the smartphone app according to the embodiment to efficiently manage the utterance content of a user and prevent the user from repeating the same thing. For example, a user can avoid repeating the same thing by checking past utterance content.Users can also check the summary of the comments to understand the important points, and can share information with others by sending the summary of the comments by email.

[0030] The recording unit simultaneously records background and environmental sounds along with speech, and the generation AI analyzes them to more accurately understand the context of the speech. For example, when recording speech, the recording unit simultaneously records surrounding background and environmental sounds, and the generation AI analyzes these sounds. For example, it analyzes conference room noise and external noise to complement the context of the speech. The recording unit also analyzes environmental sounds contained in the recorded data to provide background information for the speech. For example, it can distinguish between a conversation in a cafe and a conversation in an office, more accurately understanding the intention of the speech. The recording unit also analyzes background and environmental sounds to grasp the situation and atmosphere of the speech and deepen understanding of the content of the speech. For example, it can distinguish between speech in an emergency and everyday speech. This allows for a more accurate understanding of the context of the speech.

[0031] The recording unit records the speaker's gestures or facial expressions with a camera while recording the speech, and the generation AI analyzes this data to complement the content of the speech. For example, the recording unit records the speaker's gestures and facial expressions with a camera while recording the speech, and the generation AI analyzes this data. For example, it analyzes hand movements and facial expressions to complement the intention of the speech. The recording unit also analyzes gesture and facial expression data to deepen understanding of the content of the speech. For example, it analyzes facial expressions such as smiles and nods to understand the positive nuances of the speech. The recording unit also analyzes gestures and facial expressions recorded with a camera to provide background information for the speech. For example, it analyzes speech that emphasizes hand waving to understand the importance of the speech. This makes it possible to complement the content of the speech with gestures and facial expressions.

[0032] The analysis unit can automatically translate recorded data into different languages, enabling use at international conferences and in multilingual environments. The analysis unit, for example, automatically translates recorded data of utterances to generate text data in different languages. For example, it translates Japanese utterances into English or French. The analysis unit also integrates the automatically translated utterance data into a multilingual conference system, enabling use at international conferences. For example, it displays translated utterances in real time. The analysis unit also saves utterance data translated into different languages ​​and manages utterance history in multilingual environments. For example, it simultaneously displays utterance history in English and Spanish. This enables use at international conferences and in multilingual environments.

[0033] When generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statements or documents to create a more comprehensive summary. For example, when generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statement data and integrate related information to create a comprehensive summary. For example, the summary can be supplemented by referring to statements made in past meetings. The summary generation unit also automatically searches for related documents and reflects them in the summary of the statement. For example, it incorporates the contents of related reports or emails into the summary. The summary generation unit also generates a more accurate summary of the statement by referring to past statements and documents. For example, it creates a summary based on past statements on the same topic. This allows a more comprehensive summary to be created by referring to past statements and documents.

[0034] The summary generation unit can automatically extract keywords or phrases from the speech content and evaluate their importance in order to improve the accuracy of the summary. For example, the summary generation unit uses a generation AI to automatically extract keywords and phrases from the speech content and evaluate their importance. For example, it creates a summary based on frequently occurring keywords. The summary generation unit also extracts important points from the speech content and reflects them in the summary. For example, it incorporates phrases that are emphasized in the speech into the summary. The summary generation unit also improves the accuracy of the summary by evaluating the importance of keywords and phrases. For example, it prioritizes including highly important keywords in the summary. This can improve the accuracy of the summary.

[0035] The summary generation unit converts the summarized speech content into a visual note or mind map, making it easier to understand visually. For example, the summary generation unit converts the summarized speech content into a visual note and displays it visually. For example, it indicates important points with diagrams or icons. The summary generation unit also converts the summarized speech content into a mind map format and visually organizes related keywords and concepts. This makes it possible to understand the overall picture of the speech content at a glance. The summary generation unit also develops tools that automatically generate visual notes and mind maps, allowing users to easily visually display summaries of speech content. For example, it provides a function for visualizing summary text with drag and drop. This makes it easier to visually understand the summarized speech content.

[0036] The summary generation unit can save summaries in different formats and allow the user to select one. For example, the summary generation unit saves the summarized speech content in an audio format so that the user can check the summary by audio. For example, the text summary can be converted into audio data using speech synthesis technology. The summary generation unit can also save the summarized speech content in a video format so that the user can check the summary visually. For example, the summary content can be displayed in animation or slideshow format. The summary generation unit can also build a system that saves summaries in different formats and allows the user to select one. For example, the user can select their preferred format from text, audio, and video. This allows summaries to be saved in different formats and allow the user to select one.

[0037] When searching for past comments, the presentation unit allows the generation AI to take into account the context or topic of the comment, thereby providing more accurate search results. For example, when searching for past comments, the presentation unit allows the generation AI to analyze the context of the comment and provide search results by taking related topics into account. For example, comments on the same topic are displayed preferentially. The presentation unit also develops a search algorithm that takes into account the context of the comment, providing more accurate search results. For example, it analyzes the context of the comment to evaluate its relevance. The presentation unit also builds a system in which the generation AI provides search results by taking into account the context of the comment and related topics. For example, it displays search results based on related keywords and phrases. This makes it possible to provide more accurate search results.

[0038] The presentation unit allows the generation AI to evaluate the importance or urgency of a comment when presenting the content of the comment, and display it in a prioritized order. For example, when presenting the content of a comment, the presentation unit allows the generation AI to evaluate the importance of the comment and prioritize displaying important comments. For example, comments related to project progress are displayed with priority. The presentation unit also evaluates the urgency of the comment and prioritizes displaying highly urgent comments. For example, comments requiring urgent response are displayed at the top. The presentation unit also builds a system in which the generation AI evaluates the importance and urgency of comments and displays them in a prioritized order. For example, the presentation unit sorts comments based on their importance and urgency scores. This makes it possible to evaluate the importance and urgency of comments and display them in a prioritized order.

[0039] The presentation unit can make it possible to check the content of past statements on different devices. The presentation unit, for example, builds a system that allows the content of past statements to be checked on a smartwatch. For example, a summary of the statements is displayed on the screen of the smartwatch. The presentation unit also uses a smart speaker to make it possible to check the content of past statements by voice. For example, when you say to the smart speaker, "Tell me what you said in the past," it plays back a summary of the statements by voice. The presentation unit also develops a system that allows the content of past statements to be checked on different devices. For example, it synchronizes speech data between a smartphone, a smartwatch, and a smart speaker. This makes it possible to check the content of past statements on different devices.

[0040] The presentation unit can automatically link related materials or documents when presenting the content of a utterance, allowing the user to refer to them. The presentation unit, for example, builds a system that automatically links related materials and documents when presenting the content of a utterance. For example, it links PDF files or web pages related to the content of a utterance. The presentation unit also automatically searches for materials and documents related to the content of a utterance, allowing the user to refer to them. For example, it displays presentation materials related to the content of a utterance. The presentation unit also links related materials and documents when presenting the content of a utterance, allowing the user to easily refer to background information about the utterance. For example, it links research papers related to the content of a utterance. In this way, related materials and documents can be automatically linked when presenting the content of a utterance, allowing the user to refer to them.

[0041] The analysis unit can evaluate the similarity of the content of statements and display an alert if the similarity is high. For example, the analysis unit constructs a system in which a generation AI evaluates the similarity of the content of statements and displays an alert if the similarity is high. For example, a warning is displayed if an attempt is made to speak something similar to a past statement. The analysis unit also develops an algorithm to evaluate the similarity of the content of statements and prevent the same thing from being repeated. For example, it calculates the similarity by comparing it with a database of past statements. The analysis unit also constructs a system that detects statements with high similarity and displays an alert to the user. For example, it displays a message such as "I've talked about this before." In this way, the similarity of the content of statements can be evaluated to prevent the same thing from being repeated, and an alert can be displayed if the similarity is high.

[0042] The analysis unit can learn past utterances and analyze a user's utterance patterns to prevent repetition of utterances. For example, the analysis unit constructs a system in which a generation AI learns past utterances and analyzes a user's utterance patterns. For example, it identifies themes and phrases that a user often talks about. The analysis unit also analyzes utterance patterns to prevent the user from repeating the same story. For example, it displays a warning if the user tries to tell the same story based on content previously spoken. The analysis unit also develops a system that analyzes a user's utterance patterns and displays an alert if there are signs of repetition. For example, it displays a message such as "I've told this story before." This makes it possible to learn past utterances and analyze a user's utterance patterns to prevent repetition of utterances.

[0043] The analysis unit can analyze the content of a user's statements in real time and display an alert if there are signs of repetition. For example, the analysis unit builds a system in which a generation AI analyzes the content of a user's statements in real time and displays an alert if there are signs of repetition. For example, a warning is displayed if the user attempts to speak something similar to a previous statement. The analysis unit also develops an algorithm that analyzes the content of statements in real time to prevent the user from repeating the same thing. For example, it compares the content with a database of past statements to calculate the similarity. The analysis unit also builds a system that detects statements that show signs of repetition and displays an alert to the user. For example, it displays a message such as, "I've said this story before." This makes it possible to analyze the content of a user's statements in real time and display an alert if there are signs of repetition.

[0044] The analysis unit can analyze a user's speech history and identify repetitive patterns. For example, the analysis unit will build a system in which a generation AI analyzes a user's speech history and identifies repetitive patterns. For example, it will identify themes and phrases that a user often talks about. The analysis unit will also analyze the speech history to prevent the user from repeating the same topic. For example, it will display a warning if the user tries to talk about the same topic based on what was said in the past. The analysis unit will also develop a system that analyzes a user's speech history and displays an alert if there is a repetitive pattern. For example, it will display a message such as "I've talked about this before." This makes it possible to analyze a user's speech history and identify repetitive patterns.

[0045] Smartphone apps can be made to allow users to customize their interfaces depending on their usage. For example, a system can be built to allow users to customize their interfaces depending on their usage. For example, frequently used functions can be placed on the home screen. The smartphone app can also analyze the user's usage and automatically suggest the optimal interface. For example, it can prioritize displaying frequently used functions. The smartphone app can also provide an interface customization function, allowing users to change the app layout to suit their preferences. For example, button placement and colors can be changed. This makes it possible to customize the interface depending on the user's usage.

[0046] Smartphone apps can analyze operation history within the app and provide optimal operation guides based on the user's usage patterns. For example, a smartphone app can analyze operation history within the app and build a system that provides optimal operation guides based on the user's usage patterns. For example, it can suggest shortcuts for frequently used functions. Smartphone apps can also automatically generate optimal operation guides based on the user's operation history. For example, it can display pop-ups that explain how to use frequently used functions. Smartphone apps can also analyze operation history to identify operations that users are likely to get confused about and provide guidance for those parts. For example, it can provide step-by-step instructions on how to operate a complex settings screen. This makes it possible to analyze operation history within the app and provide optimal operation guides based on the user's usage patterns.

[0047] Smartphone apps can be linked with other applications or devices to centralize the management of speech content. For example, smartphone apps can be linked with other applications to build a system that centralizes the management of speech content. For example, a smartphone app can be linked with a calendar app to automatically record speech content at meetings. Smartphone apps can also be linked with other devices to centralize the management of speech content. For example, a smartphone app can be linked with a smart watch or smart speaker to record speech content. Smartphone apps can also be linked with other applications and devices to develop a system that centralizes the management of speech content. For example, a smartphone app can be linked with cloud storage to save speech data. This allows a smartphone app to be linked with other applications and devices to centralize the management of speech content.

[0048] Smartphone apps can be used hands-free by allowing operations within the app to be performed using voice commands. For example, a system can be built for smartphone apps that allows operations within the app to be performed using voice commands. For example, simply saying "start recording" can start recording. Smartphone apps can also use voice commands to perform various operations within the app. For example, saying "search past comments" can display past comments. Smartphone apps can also enhance their voice command functions to enable hands-free use. For example, voice recognition accuracy can be improved, allowing even complex operations to be performed by voice. This allows operations within the app to be performed using voice commands, enabling hands-free use.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The recording unit records the speaker's gestures or facial expressions with a camera while recording the speech, and the generation AI analyzes this data to complement the content of the speech. For example, the recording unit records the speaker's gestures and facial expressions with a camera while recording the speech, and the generation AI analyzes this data. For example, it analyzes hand movements and facial expressions to complement the intention of the speech. The recording unit also analyzes gesture and facial expression data to deepen understanding of the content of the speech. For example, it analyzes facial expressions such as smiles and nods to understand the positive nuances of the speech. The recording unit also analyzes gestures and facial expressions recorded with a camera to provide background information for the speech. For example, it analyzes speech that emphasizes hand waving to understand the importance of the speech. This makes it possible to complement the content of the speech with gestures and facial expressions.

[0051] The analysis unit can automatically translate recorded data into different languages, enabling use at international conferences and in multilingual environments. The analysis unit, for example, automatically translates recorded data of utterances to generate text data in different languages. For example, it translates Japanese utterances into English or French. The analysis unit also integrates the automatically translated utterance data into a multilingual conference system, enabling use at international conferences. For example, it displays translated utterances in real time. The analysis unit also saves utterance data translated into different languages ​​and manages utterance history in multilingual environments. For example, it simultaneously displays utterance history in English and Spanish. This enables use at international conferences and in multilingual environments.

[0052] When generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statements or documents to create a more comprehensive summary. For example, when generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statement data and integrate related information to create a comprehensive summary. For example, the summary can be supplemented by referring to statements made in past meetings. The summary generation unit also automatically searches for related documents and reflects them in the summary of the statement. For example, it incorporates the contents of related reports or emails into the summary. The summary generation unit also generates a more accurate summary of the statement by referring to past statements and documents. For example, it creates a summary based on past statements on the same topic. This allows a more comprehensive summary to be created by referring to past statements and documents.

[0053] The summary generation unit can automatically extract keywords or phrases from the speech content and evaluate their importance in order to improve the accuracy of the summary. For example, the summary generation unit uses a generation AI to automatically extract keywords and phrases from the speech content and evaluate their importance. For example, it creates a summary based on frequently occurring keywords. The summary generation unit also extracts important points from the speech content and reflects them in the summary. For example, it incorporates phrases that are emphasized in the speech into the summary. The summary generation unit also improves the accuracy of the summary by evaluating the importance of keywords and phrases. For example, it prioritizes including highly important keywords in the summary. This can improve the accuracy of the summary.

[0054] The summary generation unit converts the summarized speech content into a visual note or mind map, making it easier to understand visually. For example, the summary generation unit converts the summarized speech content into a visual note and displays it visually. For example, it indicates important points with diagrams or icons. The summary generation unit also converts the summarized speech content into a mind map format and visually organizes related keywords and concepts. This makes it possible to understand the overall picture of the speech content at a glance. The summary generation unit also develops tools that automatically generate visual notes and mind maps, allowing users to easily visually display summaries of speech content. For example, it provides a function for visualizing summary text with drag and drop. This makes it easier to visually understand the summarized speech content.

[0055] The summary generation unit can save summaries in different formats and allow the user to select one. For example, the summary generation unit saves the summarized speech content in an audio format so that the user can check the summary by audio. For example, the text summary can be converted into audio data using speech synthesis technology. The summary generation unit can also save the summarized speech content in a video format so that the user can check the summary visually. For example, the summary content can be displayed in animation or slideshow format. The summary generation unit can also build a system that saves summaries in different formats and allows the user to select one. For example, the user can select their preferred format from text, audio, and video. This allows summaries to be saved in different formats and allow the user to select one.

[0056] When searching for past comments, the presentation unit allows the generation AI to take into account the context or topic of the comment, thereby providing more accurate search results. For example, when searching for past comments, the presentation unit allows the generation AI to analyze the context of the comment and provide search results by taking related topics into account. For example, comments on the same topic are displayed preferentially. The presentation unit also develops a search algorithm that takes into account the context of the comment, providing more accurate search results. For example, it analyzes the context of the comment to evaluate its relevance. The presentation unit also builds a system in which the generation AI provides search results by taking into account the context of the comment and related topics. For example, it displays search results based on related keywords and phrases. This makes it possible to provide more accurate search results.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The recording unit records what is said in a meeting or in daily life. For example, the recording unit can record what is said using the microphone on a smartphone. The recording unit can also record in high quality by connecting an external microphone. The recording unit can also start recording by pressing the recording start button. Step 2: The analysis unit analyzes the speech recorded by the recording unit. For example, the analysis unit converts the speech into text data using voice recognition technology. The analysis unit can also extract keywords from the speech. The analysis unit can also analyze the emotions behind the speech. Step 3: The summary generation unit summarizes the speech content analyzed by the analysis unit. For example, the summary generation unit summarizes the speech content using a generation AI. The summary generation unit can also extract important points and create a summary. The summary generation unit can also save the summary of the speech content. Step 4: The presentation unit presents the summary generated by the summary generation unit. For example, the presentation unit may display the summary on a smartphone screen. The presentation unit may also play the summary by audio. The presentation unit may also send the summary by email.

[0059] (Example 2) The smartphone app according to the embodiment of the present invention is a system that records speeches made in meetings and daily life, analyzes them using a generation AI, and presents summaries. This allows the smartphone app to efficiently manage the content of user speech and prevent users from repeating the same things.

[0060] A smartphone app according to an embodiment includes a recording unit, an analysis unit, a summary generation unit, and a presentation unit. The recording unit records utterances made in meetings or in daily life. For example, the recording unit records the utterances using a smartphone's microphone. The recording unit can also record with high audio quality by connecting an external microphone. The recording unit starts recording by pressing a recording start button. For example, the recording unit records the utterances using a smartphone's built-in microphone. The recording unit can also record with high audio quality by connecting an external microphone. The recording unit starts recording by pressing a recording start button. The analysis unit analyzes the utterances recorded by the recording unit. For example, the analysis unit converts the utterances into text data using voice recognition technology. The analysis unit can also extract keywords from the utterances. The analysis unit can also analyze emotions from the utterances. For example, the analysis unit converts the utterances into text data using voice recognition technology. The analysis unit can also extract keywords from the utterances. The analysis unit can also analyze emotions from the utterances. The summary generation unit summarizes the utterances analyzed by the analysis unit. For example, the summary generation unit summarizes the utterance content using a generation AI. The summary generation unit can also extract important points to create a summary. The summary generation unit can also save the summary of the utterance content. For example, the summary generation unit summarizes the utterance content using a generation AI. The summary generation unit can also extract important points to create a summary. The summary generation unit can also save the summary of the utterance content. The presentation unit presents the summary generated by the summary generation unit. For example, the presentation unit displays the summary on a smartphone screen. The presentation unit can also play the summary by audio. The presentation unit can also send the summary by email. For example, the presentation unit displays the summary on a smartphone screen. The presentation unit can also play the summary by audio. The presentation unit can also send the summary by email. This allows the smartphone app according to the embodiment to efficiently manage the utterance content of a user and prevent the user from repeating the same thing. For example, a user can avoid repeating the same thing by checking past utterance content.Users can also check the summary of the comments to understand the important points, and can share information with others by sending the summary of the comments by email.

[0061] The recording unit simultaneously records background and environmental sounds along with speech, and the generation AI analyzes them to more accurately understand the context of the speech. For example, when recording speech, the recording unit simultaneously records surrounding background and environmental sounds, and the generation AI analyzes these sounds. For example, it analyzes conference room noise and external noise to complement the context of the speech. The recording unit also analyzes environmental sounds contained in the recorded data to provide background information for the speech. For example, it can distinguish between a conversation in a cafe and a conversation in an office, more accurately understanding the intention of the speech. The recording unit also analyzes background and environmental sounds to grasp the situation and atmosphere of the speech and deepen understanding of the content of the speech. For example, it can distinguish between speech in an emergency and everyday speech. This allows for a more accurate understanding of the context of the speech.

[0062] The analysis unit can analyze the tone or speed of the speaker's voice in the recorded data to estimate the emotion and urgency. For example, the analysis unit analyzes the recorded data of speech and estimates the emotion based on the tone and speed of the speaker's voice. For example, if the voice is high-pitched and fast, it is determined that the speaker is excited. The analysis unit also analyzes changes in the speaker's voice from the recorded data to estimate the urgency. For example, if the voice suddenly gets louder, it is determined that there is a high urgency. The analysis unit also understands the emotional nuances of the speech by analyzing the tone and speed of the voice. For example, it determines that a slow voice is calm. This makes it possible to estimate the emotion and urgency of the speech.

[0063] The analysis unit can analyze the speaker's emotions in real time and record changes in emotions. For example, the analysis unit analyzes recorded data of speech in real time and records changes in the speaker's emotions. For example, if the speaker's emotions change while speaking, the change is recorded along with a timestamp. The analysis unit also uses an emotion estimation function to analyze the speaker's emotions in real time and display changes in emotions in a graph. For example, it visualizes changes in emotions from the start to the end of a speech. The analysis unit also analyzes changes in the speaker's emotions in real time and provides feedback according to changes in emotions. For example, it displays advice to relax if the speaker becomes emotional. This makes it possible to record changes in the speaker's emotions in real time.

[0064] The recording unit records the speaker's gestures or facial expressions with a camera while recording the speech, and the generation AI analyzes this data to complement the content of the speech. For example, the recording unit records the speaker's gestures and facial expressions with a camera while recording the speech, and the generation AI analyzes this data. For example, it analyzes hand movements and facial expressions to complement the intention of the speech. The recording unit also analyzes gesture and facial expression data to deepen understanding of the content of the speech. For example, it analyzes facial expressions such as smiles and nods to understand the positive nuances of the speech. The recording unit also analyzes gestures and facial expressions recorded with a camera to provide background information for the speech. For example, it analyzes speech that emphasizes hand waving to understand the importance of the speech. This makes it possible to complement the content of the speech with gestures and facial expressions.

[0065] The analysis unit can automatically translate recorded data into different languages, enabling use at international conferences and in multilingual environments. The analysis unit, for example, automatically translates recorded data of utterances to generate text data in different languages. For example, it translates Japanese utterances into English or French. The analysis unit also integrates the automatically translated utterance data into a multilingual conference system, enabling use at international conferences. For example, it displays translated utterances in real time. The analysis unit also saves utterance data translated into different languages ​​and manages utterance history in multilingual environments. For example, it simultaneously displays utterance history in English and Spanish. This enables use at international conferences and in multilingual environments.

[0066] The analysis unit can use the emotion estimation function to analyze the speaker's emotions and provide feedback based on the emotions in real time. The analysis unit, for example, analyzes recorded data of a speech and estimates the speaker's emotions in real time. For example, it displays an emotion score during speech and provides feedback according to changes in emotion. The analysis unit also uses the emotion estimation function to provide advice based on the speaker's emotions in real time. For example, it suggests ways to relax if the speaker is nervous. The analysis unit also builds a system that analyzes the speaker's emotions in real time and provides feedback based on the emotions. For example, it displays compliments if the speaker has strong positive emotions. This makes it possible to provide feedback based on emotions in real time.

[0067] When generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statements or documents to create a more comprehensive summary. For example, when generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statement data and integrate related information to create a comprehensive summary. For example, the summary can be supplemented by referring to statements made in past meetings. The summary generation unit also automatically searches for related documents and reflects them in the summary of the statement. For example, it incorporates the contents of related reports or emails into the summary. The summary generation unit also generates a more accurate summary of the statement by referring to past statements and documents. For example, it creates a summary based on past statements on the same topic. This allows a more comprehensive summary to be created by referring to past statements and documents.

[0068] The summary generation unit can automatically extract keywords or phrases from the speech content and evaluate their importance in order to improve the accuracy of the summary. For example, the summary generation unit uses a generation AI to automatically extract keywords and phrases from the speech content and evaluate their importance. For example, it creates a summary based on frequently occurring keywords. The summary generation unit also extracts important points from the speech content and reflects them in the summary. For example, it incorporates phrases that are emphasized in the speech into the summary. The summary generation unit also improves the accuracy of the summary by evaluating the importance of keywords and phrases. For example, it prioritizes including highly important keywords in the summary. This can improve the accuracy of the summary.

[0069] The summary generation unit can use the emotion estimation function to reflect the emotional nuances of the utterance content in the summary. For example, the summary generation unit uses the emotion estimation function to analyze the emotional nuances of the utterance content and reflect them in the summary. For example, it incorporates utterances with strong positive emotions into the summary. Furthermore, the summary generation unit generates a more accurate summary by reflecting the emotional nuances of the utterance content in the summary. For example, it evaluates the importance of the utterance based on an emotion score. Furthermore, the summary generation unit uses the emotion estimation function to build a system that reflects the emotional elements of the utterance content in the summary. For example, it incorporates changes in emotion into the summary. This allows the emotional nuances of the utterance content to be reflected in the summary.

[0070] The summary generation unit converts the summarized speech content into a visual note or mind map, making it easier to understand visually. For example, the summary generation unit converts the summarized speech content into a visual note and displays it visually. For example, it indicates important points with diagrams or icons. The summary generation unit also converts the summarized speech content into a mind map format and visually organizes related keywords and concepts. This makes it possible to understand the overall picture of the speech content at a glance. The summary generation unit also develops tools that automatically generate visual notes and mind maps, allowing users to easily visually display summaries of speech content. For example, it provides a function for visualizing summary text with drag and drop. This makes it easier to visually understand the summarized speech content.

[0071] The summary generation unit can save summaries in different formats and allow the user to select one. For example, the summary generation unit saves the summarized speech content in an audio format so that the user can check the summary by audio. For example, the text summary can be converted into audio data using speech synthesis technology. The summary generation unit can also save the summarized speech content in a video format so that the user can check the summary visually. For example, the summary content can be displayed in animation or slideshow format. The summary generation unit can also build a system that saves summaries in different formats and allows the user to select one. For example, the user can select their preferred format from text, audio, and video. This allows summaries to be saved in different formats and allow the user to select one.

[0072] The summary generation unit uses the emotion estimation function to collect users' emotional reactions to the summarized utterances, thereby improving the accuracy of the summary. For example, the summary generation unit collects users' emotional reactions to the summarized utterances in real time and improves the accuracy of the summary based on the data. For example, it prioritizes the adoption of summaries with a high number of positive reactions. Furthermore, the summary generation unit uses the emotion estimation function to collect feedback on the summarized utterances, and regenerates the summary if there are a high number of negative reactions. Furthermore, the summary generation unit analyzes the users' emotional reaction data and identifies areas for improvement in the summary based on the results. For example, it makes suggestions for correcting parts with low emotion scores. This improves the accuracy of the summary.

[0073] When searching for past comments, the presentation unit allows the generation AI to take into account the context or topic of the comment, thereby providing more accurate search results. For example, when searching for past comments, the presentation unit allows the generation AI to analyze the context of the comment and provide search results by taking related topics into account. For example, comments on the same topic are displayed preferentially. The presentation unit also develops a search algorithm that takes into account the context of the comment, providing more accurate search results. For example, it analyzes the context of the comment to evaluate its relevance. The presentation unit also builds a system in which the generation AI provides search results by taking into account the context of the comment and related topics. For example, it displays search results based on related keywords and phrases. This makes it possible to provide more accurate search results.

[0074] The presentation unit allows the generation AI to evaluate the importance or urgency of a comment when presenting the content of the comment, and display it in a prioritized order. For example, when presenting the content of a comment, the presentation unit allows the generation AI to evaluate the importance of the comment and prioritize displaying important comments. For example, comments related to project progress are displayed with priority. The presentation unit also evaluates the urgency of the comment and prioritizes displaying highly urgent comments. For example, comments requiring urgent response are displayed at the top. The presentation unit also builds a system in which the generation AI evaluates the importance and urgency of comments and displays them in a prioritized order. For example, the presentation unit sorts comments based on their importance and urgency scores. This makes it possible to evaluate the importance and urgency of comments and display them in a prioritized order.

[0075] The presentation unit can use the emotion estimation function to analyze emotional reactions to past comment content and provide emotion-based feedback. The presentation unit, for example, uses the emotion estimation function to analyze emotional reactions to past comment content and provide emotion-based feedback. For example, it displays compliments for comments with strong positive emotions. The presentation unit also analyzes emotional reactions to past comment content and makes improvement suggestions for comments with strong negative emotions. For example, it presents improvement measures for comments that have received negative feedback. The presentation unit also uses the emotion estimation function to build a system that analyzes emotional reactions to past comment content and provides emotion-based feedback. For example, it customizes feedback based on emotion scores. This makes it possible to provide emotion-based feedback.

[0076] The presentation unit can make it possible to check the content of past statements on different devices. The presentation unit, for example, builds a system that allows the content of past statements to be checked on a smartwatch. For example, a summary of the statements is displayed on the screen of the smartwatch. The presentation unit also uses a smart speaker to make it possible to check the content of past statements by voice. For example, when you say to the smart speaker, "Tell me what you said in the past," it plays back a summary of the statements by voice. The presentation unit also develops a system that allows the content of past statements to be checked on different devices. For example, it synchronizes speech data between a smartphone, a smartwatch, and a smart speaker. This makes it possible to check the content of past statements on different devices.

[0077] The presentation unit can automatically link related materials or documents when presenting the content of a utterance, allowing the user to refer to them. The presentation unit, for example, builds a system that automatically links related materials and documents when presenting the content of a utterance. For example, it links PDF files or web pages related to the content of a utterance. The presentation unit also automatically searches for materials and documents related to the content of a utterance, allowing the user to refer to them. For example, it displays presentation materials related to the content of a utterance. The presentation unit also links related materials and documents when presenting the content of a utterance, allowing the user to easily refer to background information about the utterance. For example, it links research papers related to the content of a utterance. In this way, related materials and documents can be automatically linked when presenting the content of a utterance, allowing the user to refer to them.

[0078] The presentation unit can use the emotion estimation function to collect the user's emotional reactions to past comment content and optimize the content to be presented. The presentation unit, for example, uses the emotion estimation function to collect the user's emotional reactions to past comment content and optimize the content to be presented based on that data. For example, comments with strong positive emotions are preferentially displayed. The presentation unit also analyzes the user's emotional reaction data and optimizes the presentation order of past comment content. For example, comments with high emotion scores are displayed at the top. The presentation unit also uses the emotion estimation function to collect the user's emotional reactions to past comment content and build a system that optimizes the content to be presented. For example, the importance of the comment is evaluated based on the emotion score. This makes it possible to collect the user's emotional reactions to past comment content and optimize the content to be presented.

[0079] The analysis unit can evaluate the similarity of the content of statements and display an alert if the similarity is high. For example, the analysis unit constructs a system in which a generation AI evaluates the similarity of the content of statements and displays an alert if the similarity is high. For example, a warning is displayed if an attempt is made to speak something similar to a past statement. The analysis unit also develops an algorithm to evaluate the similarity of the content of statements and prevent the same thing from being repeated. For example, it calculates the similarity by comparing it with a database of past statements. The analysis unit also constructs a system that detects statements with high similarity and displays an alert to the user. For example, it displays a message such as "I've talked about this before." In this way, the similarity of the content of statements can be evaluated to prevent the same thing from being repeated, and an alert can be displayed if the similarity is high.

[0080] The analysis unit can learn past utterances and analyze a user's utterance patterns to prevent repetition of utterances. For example, the analysis unit constructs a system in which a generation AI learns past utterances and analyzes a user's utterance patterns. For example, it identifies themes and phrases that a user often talks about. The analysis unit also analyzes utterance patterns to prevent the user from repeating the same story. For example, it displays a warning if the user tries to tell the same story based on content previously spoken. The analysis unit also develops a system that analyzes a user's utterance patterns and displays an alert if there are signs of repetition. For example, it displays a message such as "I've told this story before." This makes it possible to learn past utterances and analyze a user's utterance patterns to prevent repetition of utterances.

[0081] The analysis unit can use the emotion estimation function to identify emotional triggers when a user repeats the same story and display an alert. The analysis unit, for example, uses the emotion estimation function to build a system that identifies emotional triggers when a user repeats the same story. For example, a warning is displayed when a specific emotional state is causing the repetition. The analysis unit also analyzes the user's emotion data to identify emotional triggers when the user repeats the same story. For example, an alert is displayed when the user repeats the same story due to tension or anxiety. The analysis unit also uses the emotion estimation function to develop a system that identifies emotional triggers when a user repeats the same story and displays an alert. For example, a message such as "I've told this story before" is displayed. This makes it possible to identify emotional triggers when a user repeats the same story and display an alert.

[0082] The analysis unit can analyze the content of a user's statements in real time and display an alert if there are signs of repetition. For example, the analysis unit builds a system in which a generation AI analyzes the content of a user's statements in real time and displays an alert if there are signs of repetition. For example, a warning is displayed if the user attempts to speak something similar to a previous statement. The analysis unit also develops an algorithm that analyzes the content of statements in real time to prevent the user from repeating the same thing. For example, it compares the content with a database of past statements to calculate the similarity. The analysis unit also builds a system that detects statements that show signs of repetition and displays an alert to the user. For example, it displays a message such as, "I've said this story before." This makes it possible to analyze the content of a user's statements in real time and display an alert if there are signs of repetition.

[0083] The analysis unit can analyze a user's speech history and identify repetitive patterns. For example, the analysis unit will build a system in which a generation AI analyzes a user's speech history and identifies repetitive patterns. For example, it will identify themes and phrases that a user often talks about. The analysis unit will also analyze the speech history to prevent the user from repeating the same topic. For example, it will display a warning if the user tries to talk about the same topic based on what was said in the past. The analysis unit will also develop a system that analyzes a user's speech history and displays an alert if there is a repetitive pattern. For example, it will display a message such as "I've talked about this before." This makes it possible to analyze a user's speech history and identify repetitive patterns.

[0084] The analysis unit can use the emotion estimation function to identify emotional triggers when a user repeats the same story and display an alert. The analysis unit, for example, uses the emotion estimation function to build a system that identifies emotional triggers when a user repeats the same story. For example, a warning is displayed when a specific emotional state is causing the repetition. The analysis unit also analyzes the user's emotion data to identify emotional triggers when the user repeats the same story. For example, an alert is displayed when the user repeats the same story due to tension or anxiety. The analysis unit also uses the emotion estimation function to develop a system that identifies emotional triggers when a user repeats the same story and displays an alert. For example, a message such as "I've told this story before" is displayed. This makes it possible to identify emotional triggers when a user repeats the same story and display an alert.

[0085] Smartphone apps can be made to allow users to customize their interfaces depending on their usage. For example, a system can be built to allow users to customize their interfaces depending on their usage. For example, frequently used functions can be placed on the home screen. The smartphone app can also analyze the user's usage and automatically suggest the optimal interface. For example, it can prioritize displaying frequently used functions. The smartphone app can also provide an interface customization function, allowing users to change the app layout to suit their preferences. For example, button placement and colors can be changed. This makes it possible to customize the interface depending on the user's usage.

[0086] Smartphone apps can analyze operation history within the app and provide optimal operation guides based on the user's usage patterns. For example, a smartphone app can analyze operation history within the app and build a system that provides optimal operation guides based on the user's usage patterns. For example, it can suggest shortcuts for frequently used functions. Smartphone apps can also automatically generate optimal operation guides based on the user's operation history. For example, it can display pop-ups that explain how to use frequently used functions. Smartphone apps can also analyze operation history to identify operations that users are likely to get confused about and provide guidance for those parts. For example, it can provide step-by-step instructions on how to operate a complex settings screen. This makes it possible to analyze operation history within the app and provide optimal operation guides based on the user's usage patterns.

[0087] A smartphone app can use an emotion estimation function to change the interface or provide feedback according to the user's emotional state. For example, a smartphone app uses the emotion estimation function to build a system that changes the interface according to the user's emotional state. For example, if the user is feeling stressed, the color scheme can be changed to a more relaxing one. The smartphone app can also analyze the user's emotional state in real time and provide feedback based on the results. For example, if the user is irritated, the smartphone app can display an encouraging message. The smartphone app can also use the emotion estimation function to develop a system that changes the interface or provides feedback according to the user's emotional state. For example, if the user is feeling strongly positive, the smartphone app can display a compliment. This makes it possible to change the interface or provide feedback according to the user's emotional state.

[0088] Smartphone apps can be linked with other applications or devices to centralize the management of speech content. For example, smartphone apps can be linked with other applications to build a system that centralizes the management of speech content. For example, a smartphone app can be linked with a calendar app to automatically record speech content at meetings. Smartphone apps can also be linked with other devices to centralize the management of speech content. For example, a smartphone app can be linked with a smart watch or smart speaker to record speech content. Smartphone apps can also be linked with other applications and devices to develop a system that centralizes the management of speech content. For example, a smartphone app can be linked with cloud storage to save speech data. This allows a smartphone app to be linked with other applications and devices to centralize the management of speech content.

[0089] Smartphone apps can be used hands-free by allowing operations within the app to be performed using voice commands. For example, a system can be built for smartphone apps that allows operations within the app to be performed using voice commands. For example, simply saying "start recording" can start recording. Smartphone apps can also use voice commands to perform various operations within the app. For example, saying "search past comments" can display past comments. Smartphone apps can also enhance their voice command functions to enable hands-free use. For example, voice recognition accuracy can be improved, allowing even complex operations to be performed by voice. This allows operations within the app to be performed using voice commands, enabling hands-free use.

[0090] A smartphone app can use an emotion estimation function to change the interface or provide feedback according to the user's emotional state. For example, a smartphone app uses the emotion estimation function to build a system that changes the interface according to the user's emotional state. For example, if the user is feeling stressed, the color scheme can be changed to a more relaxing one. The smartphone app can also analyze the user's emotional state in real time and provide feedback based on the results. For example, if the user is irritated, the smartphone app can display an encouraging message. The smartphone app can also use the emotion estimation function to develop a system that changes the interface or provides feedback according to the user's emotional state. For example, if the user is feeling strongly positive, the smartphone app can display a compliment. This makes it possible to change the interface or provide feedback according to the user's emotional state.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The recording unit records the speaker's gestures or facial expressions with a camera while recording the speech, and the generation AI analyzes this data to complement the content of the speech. For example, the recording unit records the speaker's gestures and facial expressions with a camera while recording the speech, and the generation AI analyzes this data. For example, it analyzes hand movements and facial expressions to complement the intention of the speech. The recording unit also analyzes gesture and facial expression data to deepen understanding of the content of the speech. For example, it analyzes facial expressions such as smiles and nods to understand the positive nuances of the speech. The recording unit also analyzes gestures and facial expressions recorded with a camera to provide background information for the speech. For example, it analyzes speech that emphasizes hand waving to understand the importance of the speech. This makes it possible to complement the content of the speech with gestures and facial expressions.

[0093] The analysis unit can automatically translate recorded data into different languages, enabling use at international conferences and in multilingual environments. The analysis unit, for example, automatically translates recorded data of utterances to generate text data in different languages. For example, it translates Japanese utterances into English or French. The analysis unit also integrates the automatically translated utterance data into a multilingual conference system, enabling use at international conferences. For example, it displays translated utterances in real time. The analysis unit also saves utterance data translated into different languages ​​and manages utterance history in multilingual environments. For example, it simultaneously displays utterance history in English and Spanish. This enables use at international conferences and in multilingual environments.

[0094] The analysis unit can use the emotion estimation function to analyze the speaker's emotions and provide feedback based on the emotions in real time. The analysis unit, for example, analyzes recorded data of a speech and estimates the speaker's emotions in real time. For example, it displays an emotion score during speech and provides feedback according to changes in emotion. The analysis unit also uses the emotion estimation function to provide advice based on the speaker's emotions in real time. For example, it suggests ways to relax if the speaker is nervous. The analysis unit also builds a system that analyzes the speaker's emotions in real time and provides feedback based on the emotions. For example, it displays compliments if the speaker has strong positive emotions. This makes it possible to provide feedback based on emotions in real time.

[0095] When generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statements or documents to create a more comprehensive summary. For example, when generating a summary of a statement, the summary generation unit allows the generation AI to refer to past statement data and integrate related information to create a comprehensive summary. For example, the summary can be supplemented by referring to statements made in past meetings. The summary generation unit also automatically searches for related documents and reflects them in the summary of the statement. For example, it incorporates the contents of related reports or emails into the summary. The summary generation unit also generates a more accurate summary of the statement by referring to past statements and documents. For example, it creates a summary based on past statements on the same topic. This allows a more comprehensive summary to be created by referring to past statements and documents.

[0096] The summary generation unit can automatically extract keywords or phrases from the speech content and evaluate their importance in order to improve the accuracy of the summary. For example, the summary generation unit uses a generation AI to automatically extract keywords and phrases from the speech content and evaluate their importance. For example, it creates a summary based on frequently occurring keywords. The summary generation unit also extracts important points from the speech content and reflects them in the summary. For example, it incorporates phrases that are emphasized in the speech into the summary. The summary generation unit also improves the accuracy of the summary by evaluating the importance of keywords and phrases. For example, it prioritizes including highly important keywords in the summary. This can improve the accuracy of the summary.

[0097] The summary generation unit can use the emotion estimation function to reflect the emotional nuances of the utterance content in the summary. For example, the summary generation unit uses the emotion estimation function to analyze the emotional nuances of the utterance content and reflect them in the summary. For example, it incorporates utterances with strong positive emotions into the summary. Furthermore, the summary generation unit generates a more accurate summary by reflecting the emotional nuances of the utterance content in the summary. For example, it evaluates the importance of the utterance based on an emotion score. Furthermore, the summary generation unit uses the emotion estimation function to build a system that reflects the emotional elements of the utterance content in the summary. For example, it incorporates changes in emotion into the summary. This allows the emotional nuances of the utterance content to be reflected in the summary.

[0098] The summary generation unit converts the summarized speech content into a visual note or mind map, making it easier to understand visually. For example, the summary generation unit converts the summarized speech content into a visual note and displays it visually. For example, it indicates important points with diagrams or icons. The summary generation unit also converts the summarized speech content into a mind map format and visually organizes related keywords and concepts. This makes it possible to understand the overall picture of the speech content at a glance. The summary generation unit also develops tools that automatically generate visual notes and mind maps, allowing users to easily visually display summaries of speech content. For example, it provides a function for visualizing summary text with drag and drop. This makes it easier to visually understand the summarized speech content.

[0099] The summary generation unit can save summaries in different formats and allow the user to select one. For example, the summary generation unit saves the summarized speech content in an audio format so that the user can check the summary by audio. For example, the text summary can be converted into audio data using speech synthesis technology. The summary generation unit can also save the summarized speech content in a video format so that the user can check the summary visually. For example, the summary content can be displayed in animation or slideshow format. The summary generation unit can also build a system that saves summaries in different formats and allows the user to select one. For example, the user can select their preferred format from text, audio, and video. This allows summaries to be saved in different formats and allow the user to select one.

[0100] The summary generation unit uses the emotion estimation function to collect users' emotional reactions to the summarized utterances, thereby improving the accuracy of the summary. For example, the summary generation unit collects users' emotional reactions to the summarized utterances in real time and improves the accuracy of the summary based on the data. For example, it prioritizes the adoption of summaries with a high number of positive reactions. Furthermore, the summary generation unit uses the emotion estimation function to collect feedback on the summarized utterances, and regenerates the summary if there are a high number of negative reactions. Furthermore, the summary generation unit analyzes the users' emotional reaction data and identifies areas for improvement in the summary based on the results. For example, it makes suggestions for correcting parts with low emotion scores. This improves the accuracy of the summary.

[0101] When searching for past comments, the presentation unit allows the generation AI to take into account the context or topic of the comment, thereby providing more accurate search results. For example, when searching for past comments, the presentation unit allows the generation AI to analyze the context of the comment and provide search results by taking related topics into account. For example, comments on the same topic are displayed preferentially. The presentation unit also develops a search algorithm that takes into account the context of the comment, providing more accurate search results. For example, it analyzes the context of the comment to evaluate its relevance. The presentation unit also builds a system in which the generation AI provides search results by taking into account the context of the comment and related topics. For example, it displays search results based on related keywords and phrases. This makes it possible to provide more accurate search results.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The recording unit records what is said in a meeting or in daily life. For example, the recording unit can record what is said using the microphone on a smartphone. The recording unit can also record in high quality by connecting an external microphone. The recording unit can also start recording by pressing the recording start button. Step 2: The analysis unit analyzes the speech recorded by the recording unit. For example, the analysis unit converts the speech into text data using voice recognition technology. The analysis unit can also extract keywords from the speech. The analysis unit can also analyze the emotions behind the speech. Step 3: The summary generation unit summarizes the speech content analyzed by the analysis unit. For example, the summary generation unit summarizes the speech content using a generation AI. The summary generation unit can also extract important points and create a summary. The summary generation unit can also save the summary of the speech content. Step 4: The presentation unit presents the summary generated by the summary generation unit. For example, the presentation unit may display the summary on a smartphone screen. The presentation unit may also play the summary by audio. The presentation unit may also send the summary by email.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 AI 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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 AI 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 AI 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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, in order to avoid confusion and to 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.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 recording unit that records statements made in meetings or in daily life; an analysis unit that analyzes the content of the speech recorded by the recording unit; a summary generation unit that summarizes the speech content analyzed by the analysis unit; a presentation unit that presents the summary generated by the summary generation unit. A system characterized by:

2. The recording unit The speech is recorded along with background and environmental sounds, which are then analyzed by the generation AI to more accurately understand the context of the speech.

2. The system of claim 1.

3. The analysis unit The tone or speed of the speaker's voice is analyzed for recorded data to estimate emotion and urgency.

2. The system of claim 1.

4. The analysis unit Analyze the speaker's emotions in real time and record changes in those emotions.

2. The system of claim 1.

5. The recording unit At the same time as recording the speech, the speaker's gestures or facial expressions are recorded with a camera, and the generation AI analyzes this data to complement the speech content.

2. The system of claim 1.

6. The analysis unit Automatically translates recorded data into different languages, enabling use in international conferences and multilingual environments 2. The system of claim 1.

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