system

Abstract

An object of a system according to an embodiment is to automate a process from recording of a conversation to creation of minutes.SOLUTION: A system includes voice registered glasses, a PC, and a generation AI. The voice registration glasses record the conversation and transmit the recorded voice data to the PC through wireless communication. The PC converts the received voice to text using the generated AI. The generation AI automatically creates the record based on the text.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process from recording conversations to creating minutes was done manually, which was time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to automate the process from recording conversations to creating minutes. [Means for solving the problem]

[0006] The system according to the embodiment includes voice registration glasses, a PC, and a generation AI. The voice registration glasses record conversations and transmit the recorded voice data to the PC via wireless communication. The PC converts the received voice data into text using the generation AI. The generation AI then automatically creates minutes based on the text. [Effects of the Invention]

[0007] The system according to the embodiment can automate processes from recording conversations to creating minutes. [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 automatic minutes-taking system according to an embodiment of the present invention is a system that automatically takes minutes of conversations by linking voice-registered glasses with a PC. This allows the automatic minutes-taking system to accurately record the content of conversations, making it convenient for later reference.

[0029] An automatic minutes-taking system according to an embodiment includes voice-registered glasses, a PC, and a generation AI. The voice-registered glasses record conversations and transmit the recorded voice data to the PC via wireless communication. For example, the voice-registered glasses transmit the voice data to the PC using Bluetooth or Wi-Fi. The voice-registered glasses also have a translation function, allowing them to understand conversations in different languages. For example, they can translate spoken English into Japanese. The PC converts the received voice data into text using a generation AI. For example, the generation AI analyzes the voice data using voice recognition technology to generate accurate text. The generation AI also analyzes the flow of the conversation and key points to compile minutes in an appropriate format. For example, minutes are created by organizing the meeting start time, participants, agenda, remarks, and decisions. This allows the automatic minutes-taking system according to an embodiment to automatically take minutes of conversations. For example, accurate minutes can be created in real time for international conferences or business meetings where multiple languages ​​are spoken.

[0030] Voice registration glasses can be equipped with a noise canceling function to enable clear audio recording even in noisy environments. Voice registration glasses, for example, can remove ambient noise to record clear audio. For example, noise in a conference room can be removed. The noise canceling function can also be used to enable clear audio recording even in noisy environments. For example, this is effective when recording conversations in cafes or public places. Voice registration glasses can also be equipped with a noise canceling function to enable clear audio recording even in noisy environments. For example, this is effective when recording conversations in construction sites or places with heavy traffic. This allows clear audio recording even in noisy environments.

[0031] Voice registration glasses can be equipped with a facial recognition function to identify speakers and reflect their names in the minutes. Voice registration glasses, for example, can identify speakers and reflect their names in the minutes. For example, they can recognize the faces of speakers during a meeting and record their names in the minutes. They can also use the facial recognition function to identify speakers and reflect their names in the minutes. For example, if there are multiple speakers, they can accurately record each speaker's remarks. Voice registration glasses can be equipped with a facial recognition function to identify speakers and reflect their names in the minutes. For example, they can recognize the faces of speakers and record the content of their remarks in the minutes. This allows speakers to be identified and reflected in the minutes.

[0032] The voice registration glasses are equipped with a camera and can also record video of conversations, with the video and audio being recorded in sync. The voice registration glasses, for example, can also record video of conversations. For example, the video and audio of a meeting can be recorded in sync. The voice registration glasses can also use a camera to record video of conversations, with the video and audio being recorded in sync. For example, the video and audio of a presentation can be recorded simultaneously. The voice registration glasses are equipped with a camera and can also record video of conversations, with the video and audio being recorded in sync. For example, the video and audio of an interview can be recorded simultaneously. This allows the video and audio to be recorded in sync.

[0033] The voice registration glasses are equipped with a biometric sensor and can record the speaker's biometric information. The voice registration glasses, for example, record the speaker's biometric information. For example, they measure the speaker's heart rate and stress level. The biometric sensor is also used to record the speaker's biometric information. For example, the speaker's heart rate and stress level are reflected in the minutes of the meeting. The voice registration glasses are equipped with a biometric sensor and can record the speaker's biometric information. For example, the speaker's heart rate and stress level are measured in real time. This allows the speaker's biometric information to be recorded.

[0034] When transmitting audio data, the transmission speed can be improved by using data compression technology. When transmitting audio data, for example, the transmission speed is improved by using data compression technology. For example, by compressing and transmitting audio data, the communication time is shortened. In addition, the transmission speed of audio data is improved by using data compression technology. For example, by compressing and transmitting audio data, the communication bandwidth is saved. In addition, when transmitting audio data, the transmission speed is improved by using data compression technology. For example, by compressing and transmitting audio data, the stability of communication is improved. This improves the transmission speed.

[0035] When transmitting voice data, data security can be strengthened by using encryption technology. When transmitting voice data, for example, data security can be strengthened by using encryption technology. For example, by encrypting and transmitting voice data, unauthorized access can be prevented. Furthermore, data security can be strengthened by using encryption technology. For example, by encrypting and transmitting voice data, data confidentiality can be protected. Furthermore, when transmitting voice data, data security can be strengthened by using encryption technology. For example, by encrypting and transmitting voice data, data tampering can be prevented. This makes it possible to strengthen data security.

[0036] For transmitting voice data, high-speed and stable data transmission can be achieved by using 5G communication technology. For transmitting voice data, for example, high-speed and stable data transmission can be achieved by using 5G communication technology. For example, voice data is transmitted using a 5G network. Furthermore, 5G communication technology is used to improve the transmission speed of voice data. For example, voice data is transmitted in real time using a 5G network. Furthermore, for transmitting voice data, high-speed and stable data transmission can be achieved by using 5G communication technology. For example, voice data is transmitted stably using a 5G network. This allows high-speed and stable data transmission to be achieved.

[0037] A mesh network can be used to transmit audio data and share data among multiple devices. For example, a mesh network can be used to transmit audio data and share data among multiple devices. For example, multiple pairs of voice-registered glasses can share data. A mesh network can also be used to share audio data among multiple devices. For example, multiple devices can share audio data during a meeting. A mesh network can also be used to transmit audio data and share data among multiple devices. For example, multiple devices can cooperate to transmit audio data. This allows data to be shared among multiple devices.

[0038] Generative AI can add a context understanding function to understand the flow of conversation and generate more natural text. For example, generative AI can add a context understanding function to understand the flow of conversation and generate more natural text. For example, it generates text taking into account the context of the conversation. Furthermore, using the context understanding function, generative AI can understand the flow of conversation and generate natural text. For example, it generates text taking into account the intention and background of what is said. Furthermore, generative AI can add a context understanding function to understand the flow of conversation and generate more natural text. For example, it analyzes the flow of conversation and generates text in the appropriate context. This allows it to understand the flow of conversation and generate natural text.

[0039] The generative AI can add a terminology dictionary and perform text conversion specialized for a specific industry or field. For example, the generative AI can add a terminology dictionary and perform text conversion specialized for a specific industry or field. For example, it can accurately convert terminology in the medical field into text. The generative AI can also use a terminology dictionary to perform text conversion specialized for a specific industry or field. For example, it can accurately convert terminology in the technical field into text. The generative AI can also add a terminology dictionary and perform text conversion specialized for a specific industry or field. For example, it can accurately convert terminology in the legal field into text. This allows it to perform text conversion specialized for a specific industry or field.

[0040] Generative AI can be used to convert voice data into text in multiple languages ​​simultaneously. For example, generative AI can convert voice data into text in multiple languages ​​simultaneously. For example, it can convert voice data into text in English and Japanese simultaneously. Generative AI can also be developed to convert voice data into multiple languages ​​in real time. For example, it can convert speech during a meeting into text in multiple languages ​​simultaneously. Generative AI can also convert voice data into text in multiple languages ​​simultaneously. For example, it can convert speech at an international conference into text in multiple languages ​​simultaneously. This makes it possible to convert voice data into text in multiple languages ​​simultaneously.

[0041] Generative AI can be used to summarize audio data and extract only the important points. For example, generative AI can summarize audio data and extract only the important points. For example, it can automatically extract the main points of a meeting. Generative AI with a summarization function can also be developed to analyze audio data and extract the important points. For example, it can summarize important information from speech. Generative AI can also summarize audio data and extract only the important points. For example, it can grasp the main points of a long meeting in a short amount of time. This makes it possible to summarize audio data and extract only the important points.

[0042] Generative AI can add a summarization function to extract the important points of a conversation and reflect them in the minutes. For example, generative AI can add a summarization function to extract the important points of a conversation and reflect them in the minutes. For example, it can automatically extract the main points of a meeting. Generative AI with a summarization function can also be developed to extract the important points of a conversation and reflect them in the minutes. For example, it can summarize important information from what is said. Generative AI can also add a summarization function to extract the important points of a conversation and reflect them in the minutes. For example, it can grasp the main points of a long meeting in a short amount of time. This makes it possible to extract the important points of a conversation and reflect them in the minutes.

[0043] The generation AI can add a template function and create minutes in a format appropriate for the type of meeting. For example, the generation AI can add a template function and create minutes in a format appropriate for the type of meeting. For example, it can use a format appropriate for regular meetings or project meetings. In addition, a generation AI with a template function can be developed to create minutes in a format appropriate for the type of meeting. For example, it can select a format appropriate for the purpose of the meeting and the participants. In addition, the generation AI can add a template function and create minutes in a format appropriate for the type of meeting. For example, it can automatically select an appropriate format depending on the content of the meeting. This makes it possible to create minutes in a format appropriate for the type of meeting.

[0044] Using generative AI, minutes can be created in multiple languages ​​simultaneously. Generative AI can, for example, create minutes in multiple languages ​​simultaneously. For example, minutes can be created in English and Japanese simultaneously. Generative AI can also be developed to create minutes in multiple languages ​​simultaneously, and the content of the meeting can be reflected in multiple languages ​​in real time. For example, minutes of an international conference can be created in multiple languages ​​simultaneously. Generative AI can also create minutes in multiple languages ​​simultaneously. For example, the content of the meeting can be recorded in multiple languages ​​simultaneously. This allows minutes to be created in multiple languages ​​simultaneously.

[0045] Generative AI can be used to visualize minutes and automatically generate graphs and charts. For example, generative AI can visualize minutes and automatically generate graphs and charts. For example, it can visually display the contents of a meeting. Generative AI with visualization functions can also be developed to display minutes in graphs and charts. For example, it can visually show the frequency of comments and important points. Generative AI can also visualize minutes and automatically generate graphs and charts. For example, it can make the contents of a meeting easier to understand visually. This makes it possible to visualize minutes and automatically generate graphs and charts.

[0046] By utilizing the translation function of voice-registered glasses, conversations in different languages ​​can be reflected in the minutes. Voice-registered glasses, for example, utilize a translation function to reflect conversations in different languages ​​in the minutes. For example, what is spoken in English can be translated into Japanese and recorded in the minutes. The translation function can also be used to translate conversations in different languages ​​in real time and reflected in the minutes. For example, if multiple languages ​​are used during a meeting, each language can be translated and recorded in the minutes. Voice-registered glasses can also utilize a translation function to reflect conversations in different languages ​​in the minutes. For example, what is said at an international conference can be translated and recorded in the minutes. This allows conversations in different languages ​​to be reflected in the minutes.

[0047] Generative AI can be used to analyze translated text and reflect it appropriately in the minutes. For example, generative AI analyzes translated text and reflects it appropriately in the minutes. For example, minutes are created based on the translated text. Generative AI with translation functionality can also be developed to analyze translated text and reflect it appropriately in the minutes. For example, important points of the translated text can be recorded in the minutes. Generative AI can also analyze translated text and reflect it appropriately in the minutes. For example, minutes are created based on the translated text, accurately recording the contents of the meeting. This allows translated text to be analyzed and reflected appropriately in the minutes.

[0048] The translation function can be used to translate conversations in different languages ​​in real time and reflect them in the minutes. The translation function, for example, translates conversations in different languages ​​in real time and reflects them in the minutes. For example, content spoken in English can be translated into Japanese and recorded in the minutes in real time. A generative AI with real-time translation functionality has also been developed to translate conversations in different languages ​​in real time and reflect them in the minutes. For example, when multiple languages ​​are used during a meeting, each language can be translated in real time and recorded in the minutes. The translation function can also translate conversations in different languages ​​in real time and reflect them in the minutes. For example, remarks at an international conference can be translated in real time and recorded in the minutes. This allows conversations in different languages ​​to be translated in real time and reflected in the minutes.

[0049] Generative AI can be used to analyze translated text and reflect it appropriately in the minutes. For example, generative AI analyzes translated text and reflects it appropriately in the minutes. For example, minutes are created based on the translated text. Generative AI with translation functionality can also be developed to analyze translated text and reflect it appropriately in the minutes. For example, important points of the translated text can be recorded in the minutes. Generative AI can also analyze translated text and reflect it appropriately in the minutes. For example, minutes are created based on the translated text, accurately recording the contents of the meeting. This allows translated text to be analyzed and reflected appropriately in the minutes.

[0050] Generative AI can be used to analyze translated text and reflect it appropriately in the minutes. For example, generative AI analyzes translated text and reflects it appropriately in the minutes. For example, minutes are created based on the translated text. Generative AI with translation functionality can also be developed to analyze translated text and reflect it appropriately in the minutes. For example, important points of the translated text can be recorded in the minutes. Generative AI can also analyze translated text and reflect it appropriately in the minutes. For example, minutes are created based on the translated text, accurately recording the contents of the meeting. This allows translated text to be analyzed and reflected appropriately in the minutes.

[0051] The translation function can be used to translate conversations in different languages ​​in real time and reflect them in the minutes. The translation function, for example, translates conversations in different languages ​​in real time and reflects them in the minutes. For example, content spoken in English can be translated into Japanese and recorded in the minutes in real time. A generative AI with real-time translation functionality has also been developed to translate conversations in different languages ​​in real time and reflect them in the minutes. For example, when multiple languages ​​are used during a meeting, each language can be translated in real time and recorded in the minutes. The translation function can also translate conversations in different languages ​​in real time and reflect them in the minutes. For example, remarks at an international conference can be translated in real time and recorded in the minutes. This allows conversations in different languages ​​to be translated in real time and reflected in the minutes.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The automatic minutes-creation system also adds an audio data backup function to prevent data loss. For example, audio data can be automatically backed up to the cloud. The frequency of backups can also be set. For example, backups can be performed at a set time every day. Data can also be easily restored using the audio data backup function. For example, data that has been accidentally deleted can be quickly restored. This helps prevent data loss.

[0054] The automatic minutes-creation system can also add a filtering function for audio data, allowing it to automatically remove unnecessary parts. For example, it can remove silent parts and noise. It is also possible to set filtering criteria. For example, it can leave only parts that contain specific keywords. It is also possible to use the filtering function for audio data to extract only important parts. For example, it can automatically extract the main points of a meeting. This allows it to remove unnecessary parts and create minutes efficiently.

[0055] An automatic transcription system can also add a tagging function for audio data, making it easy to search for specific statements. For example, tags can be added based on the speaker's name or the content of what was said. It is also possible to set tagging criteria. For example, important statements or decisions can be tagged. The audio data tagging function can also be used to easily search for specific statements. For example, you can quickly find a specific statement after a meeting. This makes it easy to search for specific statements.

[0056] The automatic minutes-creation system can also add a transcription function for audio data, converting audio into text. For example, it can convert the audio of a meeting into text in real time. It is also possible to set the accuracy of the transcription. For example, it can perform highly accurate text conversion. The audio data transcription function can also be used to easily edit the text data. For example, incorrectly converted parts can be manually corrected. This allows for efficient conversion of audio into text and the creation of minutes.

[0057] The automatic meeting minutes system also adds a voice data translation function, allowing conversations in different languages ​​to be reflected in the minutes. For example, what is said in English can be translated into Japanese and recorded in the minutes. The accuracy of the translation can also be set. For example, high accuracy translation can be performed. The voice data translation function can also be used to translate conversations in multiple languages ​​in real time. For example, remarks made at an international conference can be translated in real time and recorded in the minutes. This allows conversations in different languages ​​to be reflected in the minutes.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The voice-registering glasses record the conversation and send the recorded voice data to a PC via wireless communication. For example, the voice-registering glasses can send the voice data to a PC using Bluetooth or Wi-Fi. The voice-registering glasses also have a translation function, allowing them to understand conversations in different languages. For example, they can translate what is spoken in English into Japanese. Step 2: The PC converts the received voice data into text using a generation AI. For example, the generation AI analyzes the voice data using voice recognition technology and generates accurate text. Step 3: The generation AI automatically creates minutes based on the text. The generation AI analyzes the flow of the conversation and key points, and compiles minutes in an appropriate format. For example, it creates minutes by organizing the meeting start time, participants, agenda, content of remarks, and decisions.

[0060] (Example 2) The automatic minutes-taking system according to an embodiment of the present invention is a system that automatically takes minutes of conversations by linking voice-registered glasses with a PC. This allows the automatic minutes-taking system to accurately record the content of conversations, making it convenient for later reference.

[0061] An automatic minutes-taking system according to an embodiment includes voice-registered glasses, a PC, and a generation AI. The voice-registered glasses record conversations and transmit the recorded voice data to the PC via wireless communication. For example, the voice-registered glasses transmit the voice data to the PC using Bluetooth or Wi-Fi. The voice-registered glasses also have a translation function, allowing them to understand conversations in different languages. For example, they can translate spoken English into Japanese. The PC converts the received voice data into text using a generation AI. For example, the generation AI analyzes the voice data using voice recognition technology to generate accurate text. The generation AI also analyzes the flow of the conversation and key points to compile minutes in an appropriate format. For example, minutes are created by organizing the meeting start time, participants, agenda, remarks, and decisions. This allows the automatic minutes-taking system according to an embodiment to automatically take minutes of conversations. For example, accurate minutes can be created in real time for international conferences or business meetings where multiple languages ​​are spoken.

[0062] The voice registration glasses are equipped with an emotion estimation function, and can record changes in emotion during a conversation in real time and reflect them in the minutes. The voice registration glasses, for example, analyze the emotion of the speaker during a conversation in real time. For example, they analyze the tone and pitch of the speaker's voice to detect changes in emotion. They also use the emotion estimation function to record changes in emotion during a conversation and reflect that data in the minutes. For example, if the speaker is excited, that emotion is noted in the minutes. The voice registration glasses also record changes in emotion during a conversation in real time and reflect them in the minutes. For example, they note in the minutes the timing when the speaker's emotion changes. This allows changes in emotion during a conversation to be reflected in the minutes.

[0063] Voice registration glasses can be equipped with a noise canceling function to enable clear audio recording even in noisy environments. Voice registration glasses, for example, can remove ambient noise to record clear audio. For example, noise in a conference room can be removed. The noise canceling function can also be used to enable clear audio recording even in noisy environments. For example, this is effective when recording conversations in cafes or public places. Voice registration glasses can also be equipped with a noise canceling function to enable clear audio recording even in noisy environments. For example, this is effective when recording conversations in construction sites or places with heavy traffic. This allows clear audio recording even in noisy environments.

[0064] Voice registration glasses can be equipped with a facial recognition function to identify speakers and reflect their names in the minutes. Voice registration glasses, for example, can identify speakers and reflect their names in the minutes. For example, they can recognize the faces of speakers during a meeting and record their names in the minutes. They can also use the facial recognition function to identify speakers and reflect their names in the minutes. For example, if there are multiple speakers, they can accurately record each speaker's remarks. Voice registration glasses can be equipped with a facial recognition function to identify speakers and reflect their names in the minutes. For example, they can recognize the faces of speakers and record the content of their remarks in the minutes. This allows speakers to be identified and reflected in the minutes.

[0065] The voice registration glasses are equipped with a camera and can also record video of conversations, with the video and audio being recorded in sync. The voice registration glasses, for example, can also record video of conversations. For example, the video and audio of a meeting can be recorded in sync. The voice registration glasses can also use a camera to record video of conversations, with the video and audio being recorded in sync. For example, the video and audio of a presentation can be recorded simultaneously. The voice registration glasses are equipped with a camera and can also record video of conversations, with the video and audio being recorded in sync. For example, the video and audio of an interview can be recorded simultaneously. This allows the video and audio to be recorded in sync.

[0066] The voice registration glasses are equipped with a biometric sensor and can record the speaker's biometric information. The voice registration glasses, for example, record the speaker's biometric information. For example, they measure the speaker's heart rate and stress level. The biometric sensor is also used to record the speaker's biometric information. For example, the speaker's heart rate and stress level are reflected in the minutes of the meeting. The voice registration glasses are equipped with a biometric sensor and can record the speaker's biometric information. For example, the speaker's heart rate and stress level are measured in real time. This allows the speaker's biometric information to be recorded.

[0067] The voice registration glasses are equipped with an emotion estimation function and can automatically adjust the quality of voice recordings according to the user's emotions. The voice registration glasses, for example, automatically adjust the quality of voice recordings according to the user's emotions. For example, if the user is excited, the recording quality is improved. The emotion estimation function is also used to automatically adjust the quality of voice recordings according to the user's emotions. For example, if the user is relaxed, the recording quality is optimized. The voice registration glasses are also equipped with an emotion estimation function and can automatically adjust the quality of voice recordings according to the user's emotions. For example, if the user's emotions change, the recording quality is adjusted in real time. This makes it possible to automatically adjust the quality of voice recordings according to the user's emotions.

[0068] When transmitting audio data, the transmission speed can be improved by using data compression technology. When transmitting audio data, for example, the transmission speed is improved by using data compression technology. For example, by compressing and transmitting audio data, the communication time is shortened. In addition, the transmission speed of audio data is improved by using data compression technology. For example, by compressing and transmitting audio data, the communication bandwidth is saved. In addition, when transmitting audio data, the transmission speed is improved by using data compression technology. For example, by compressing and transmitting audio data, the stability of communication is improved. This improves the transmission speed.

[0069] When transmitting voice data, data security can be strengthened by using encryption technology. When transmitting voice data, for example, data security can be strengthened by using encryption technology. For example, by encrypting and transmitting voice data, unauthorized access can be prevented. Furthermore, data security can be strengthened by using encryption technology. For example, by encrypting and transmitting voice data, data confidentiality can be protected. Furthermore, when transmitting voice data, data security can be strengthened by using encryption technology. For example, by encrypting and transmitting voice data, data tampering can be prevented. This makes it possible to strengthen data security.

[0070] When transmitting voice data, emotion data can be simultaneously transmitted using an emotion estimation function, and reflected in the minutes. When transmitting voice data, emotion data can also be simultaneously transmitted using an emotion estimation function, for example. For example, the speaker's emotion data can be transmitted together with the voice data. Furthermore, the emotion estimation function can be used to simultaneously transmit the voice data and emotion data, and reflected in the minutes. For example, the timing at which the speaker's emotion changes can be noted in the minutes. Furthermore, when transmitting voice data, emotion data can also be simultaneously transmitted using an emotion estimation function, and reflected in the minutes. For example, the speaker's emotion data can be transmitted in real time. This allows the emotion data to be reflected in the minutes.

[0071] For transmitting voice data, high-speed and stable data transmission can be achieved by using 5G communication technology. For transmitting voice data, for example, high-speed and stable data transmission can be achieved by using 5G communication technology. For example, voice data is transmitted using a 5G network. Furthermore, 5G communication technology is used to improve the transmission speed of voice data. For example, voice data is transmitted in real time using a 5G network. Furthermore, for transmitting voice data, high-speed and stable data transmission can be achieved by using 5G communication technology. For example, voice data is transmitted stably using a 5G network. This allows high-speed and stable data transmission to be achieved.

[0072] A mesh network can be used to transmit audio data and share data among multiple devices. For example, a mesh network can be used to transmit audio data and share data among multiple devices. For example, multiple pairs of voice-registered glasses can share data. A mesh network can also be used to share audio data among multiple devices. For example, multiple devices can share audio data during a meeting. A mesh network can also be used to transmit audio data and share data among multiple devices. For example, multiple devices can cooperate to transmit audio data. This allows data to be shared among multiple devices.

[0073] When audio data is transmitted, the emotion estimation function is used to analyze the emotion data in real time and the emotion data can be reflected in the minutes. When audio data is transmitted, for example, the emotion estimation function is used to analyze the emotion data in real time. For example, the speaker's emotion data is analyzed in real time and reflected in the minutes. Also, the emotion estimation function is used to analyze the audio data and emotion data in real time and reflect the data in the minutes. For example, the timing when the speaker's emotion changes is noted in the minutes. Also, when audio data is transmitted, the emotion estimation function is used to analyze the emotion data in real time and reflect the data in the minutes. For example, the speaker's emotion data is analyzed in real time and reflected in the minutes. In this way, emotion data can be analyzed in real time and reflected in the minutes.

[0074] The generation AI can incorporate an emotion estimation function and perform emotion-based text conversion. The generation AI, for example, incorporates an emotion estimation function and performs emotion-based text conversion. For example, it generates text taking into account the speaker's emotions. The generation AI also uses the emotion estimation function to perform emotion-based text conversion. For example, it reflects the timing when the speaker's emotions change in the text. The generation AI also incorporates an emotion estimation function and performs emotion-based text conversion. For example, it generates text taking into account the speaker's emotions and reflects this in the minutes. This allows for emotion-based text conversion.

[0075] Generative AI can add a context understanding function to understand the flow of conversation and generate more natural text. For example, generative AI can add a context understanding function to understand the flow of conversation and generate more natural text. For example, it generates text taking into account the context of the conversation. Furthermore, using the context understanding function, generative AI can understand the flow of conversation and generate natural text. For example, it generates text taking into account the intention and background of what is said. Furthermore, generative AI can add a context understanding function to understand the flow of conversation and generate more natural text. For example, it analyzes the flow of conversation and generates text in the appropriate context. This allows it to understand the flow of conversation and generate natural text.

[0076] The generative AI can add a terminology dictionary and perform text conversion specialized for a specific industry or field. For example, the generative AI can add a terminology dictionary and perform text conversion specialized for a specific industry or field. For example, it can accurately convert terminology in the medical field into text. The generative AI can also use a terminology dictionary to perform text conversion specialized for a specific industry or field. For example, it can accurately convert terminology in the technical field into text. The generative AI can also add a terminology dictionary and perform text conversion specialized for a specific industry or field. For example, it can accurately convert terminology in the legal field into text. This allows it to perform text conversion specialized for a specific industry or field.

[0077] Generative AI can be used to convert voice data into text in multiple languages ​​simultaneously. For example, generative AI can convert voice data into text in multiple languages ​​simultaneously. For example, it can convert voice data into text in English and Japanese simultaneously. Generative AI can also be developed to convert voice data into multiple languages ​​in real time. For example, it can convert speech during a meeting into text in multiple languages ​​simultaneously. Generative AI can also convert voice data into text in multiple languages ​​simultaneously. For example, it can convert speech at an international conference into text in multiple languages ​​simultaneously. This makes it possible to convert voice data into text in multiple languages ​​simultaneously.

[0078] Generative AI can be used to summarize audio data and extract only the important points. For example, generative AI can summarize audio data and extract only the important points. For example, it can automatically extract the main points of a meeting. Generative AI with a summarization function can also be developed to analyze audio data and extract the important points. For example, it can summarize important information from speech. Generative AI can also summarize audio data and extract only the important points. For example, it can grasp the main points of a long meeting in a short amount of time. This makes it possible to summarize audio data and extract only the important points.

[0079] The generation AI can incorporate an emotion estimation function, perform text conversion based on emotion, and reflect this in the minutes. The generation AI, for example, can incorporate an emotion estimation function, perform text conversion based on emotion, and reflect this in the minutes. For example, it can generate text taking into account the speaker's emotion and record it in the minutes. The generation AI can also use the emotion estimation function to perform text conversion based on emotion and reflect this in the minutes. For example, it can record the timing when the speaker's emotion changes in the minutes. The generation AI can also incorporate an emotion estimation function, perform text conversion based on emotion, and reflect this in the minutes. For example, it can generate text taking into account the speaker's emotion and reflect this in the minutes. This allows text conversion based on emotion to be performed and reflected in the minutes.

[0080] The generation AI can incorporate an emotion estimation function and automatically create meeting minutes based on emotions. The generation AI can, for example, incorporate an emotion estimation function and automatically create meeting minutes based on emotions. For example, minutes can be created taking into account the emotions of the speaker. The generation AI can also use the emotion estimation function to automatically create meeting minutes based on emotions. For example, the timing when the speaker's emotions changed can be noted in the minutes. The generation AI can also incorporate an emotion estimation function and automatically create meeting minutes based on emotions. For example, minutes can be created taking into account the emotions of the speaker, reflecting the atmosphere of the meeting. This makes it possible to automatically create meeting minutes based on emotions.

[0081] Generative AI can add a summarization function to extract the important points of a conversation and reflect them in the minutes. For example, generative AI can add a summarization function to extract the important points of a conversation and reflect them in the minutes. For example, it can automatically extract the main points of a meeting. Generative AI with a summarization function can also be developed to extract the important points of a conversation and reflect them in the minutes. For example, it can summarize important information from what is said. Generative AI can also add a summarization function to extract the important points of a conversation and reflect them in the minutes. For example, it can grasp the main points of a long meeting in a short amount of time. This makes it possible to extract the important points of a conversation and reflect them in the minutes.

[0082] The generation AI can add a template function and create minutes in a format appropriate for the type of meeting. For example, the generation AI can add a template function and create minutes in a format appropriate for the type of meeting. For example, it can use a format appropriate for regular meetings or project meetings. In addition, a generation AI with a template function can be developed to create minutes in a format appropriate for the type of meeting. For example, it can select a format appropriate for the purpose of the meeting and the participants. In addition, the generation AI can add a template function and create minutes in a format appropriate for the type of meeting. For example, it can automatically select an appropriate format depending on the content of the meeting. This makes it possible to create minutes in a format appropriate for the type of meeting.

[0083] Using generative AI, minutes can be created in multiple languages ​​simultaneously. Generative AI can, for example, create minutes in multiple languages ​​simultaneously. For example, minutes can be created in English and Japanese simultaneously. Generative AI can also be developed to create minutes in multiple languages ​​simultaneously, and the content of the meeting can be reflected in multiple languages ​​in real time. For example, minutes of an international conference can be created in multiple languages ​​simultaneously. Generative AI can also create minutes in multiple languages ​​simultaneously. For example, the content of the meeting can be recorded in multiple languages ​​simultaneously. This allows minutes to be created in multiple languages ​​simultaneously.

[0084] Generative AI can be used to visualize minutes and automatically generate graphs and charts. For example, generative AI can visualize minutes and automatically generate graphs and charts. For example, it can visually display the contents of a meeting. Generative AI with visualization functions can also be developed to display minutes in graphs and charts. For example, it can visually show the frequency of comments and important points. Generative AI can also visualize minutes and automatically generate graphs and charts. For example, it can make the contents of a meeting easier to understand visually. This makes it possible to visualize minutes and automatically generate graphs and charts.

[0085] By utilizing the translation function of voice-registered glasses, conversations in different languages ​​can be reflected in the minutes. Voice-registered glasses, for example, utilize a translation function to reflect conversations in different languages ​​in the minutes. For example, what is spoken in English can be translated into Japanese and recorded in the minutes. The translation function can also be used to translate conversations in different languages ​​in real time and reflected in the minutes. For example, if multiple languages ​​are used during a meeting, each language can be translated and recorded in the minutes. Voice-registered glasses can also utilize a translation function to reflect conversations in different languages ​​in the minutes. For example, what is said at an international conference can be translated and recorded in the minutes. This allows conversations in different languages ​​to be reflected in the minutes.

[0086] Generative AI can be used to analyze translated text and reflect it appropriately in the minutes. For example, generative AI analyzes translated text and reflects it appropriately in the minutes. For example, minutes are created based on the translated text. Generative AI with translation functionality can also be developed to analyze translated text and reflect it appropriately in the minutes. For example, important points of the translated text can be recorded in the minutes. Generative AI can also analyze translated text and reflect it appropriately in the minutes. For example, minutes are created based on the translated text, accurately recording the contents of the meeting. This allows translated text to be analyzed and reflected appropriately in the minutes.

[0087] The translation function can be used to translate conversations in different languages ​​in real time and reflect them in the minutes. The translation function, for example, translates conversations in different languages ​​in real time and reflects them in the minutes. For example, content spoken in English can be translated into Japanese and recorded in the minutes in real time. A generative AI with real-time translation functionality has also been developed to translate conversations in different languages ​​in real time and reflect them in the minutes. For example, when multiple languages ​​are used during a meeting, each language can be translated in real time and recorded in the minutes. The translation function can also translate conversations in different languages ​​in real time and reflect them in the minutes. For example, remarks at an international conference can be translated in real time and recorded in the minutes. This allows conversations in different languages ​​to be translated in real time and reflected in the minutes.

[0088] Generative AI can be used to analyze translated text and reflect it appropriately in the minutes. For example, generative AI analyzes translated text and reflects it appropriately in the minutes. For example, minutes are created based on the translated text. Generative AI with translation functionality can also be developed to analyze translated text and reflect it appropriately in the minutes. For example, important points of the translated text can be recorded in the minutes. Generative AI can also analyze translated text and reflect it appropriately in the minutes. For example, minutes are created based on the translated text, accurately recording the contents of the meeting. This allows translated text to be analyzed and reflected appropriately in the minutes.

[0089] Generative AI can be used to analyze translated text and reflect it appropriately in the minutes. For example, generative AI analyzes translated text and reflects it appropriately in the minutes. For example, minutes are created based on the translated text. Generative AI with translation functionality can also be developed to analyze translated text and reflect it appropriately in the minutes. For example, important points of the translated text can be recorded in the minutes. Generative AI can also analyze translated text and reflect it appropriately in the minutes. For example, minutes are created based on the translated text, accurately recording the contents of the meeting. This allows translated text to be analyzed and reflected appropriately in the minutes.

[0090] The translation function can be used to translate conversations in different languages ​​in real time and reflect them in the minutes. The translation function, for example, translates conversations in different languages ​​in real time and reflects them in the minutes. For example, content spoken in English can be translated into Japanese and recorded in the minutes in real time. A generative AI with real-time translation functionality has also been developed to translate conversations in different languages ​​in real time and reflect them in the minutes. For example, when multiple languages ​​are used during a meeting, each language can be translated in real time and recorded in the minutes. The translation function can also translate conversations in different languages ​​in real time and reflect them in the minutes. For example, remarks at an international conference can be translated in real time and recorded in the minutes. This allows conversations in different languages ​​to be translated in real time and reflected in the minutes.

[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 automated minutes-creation system can also estimate the user's emotions and adjust the content of the minutes based on the estimated emotions. For example, if a speaker expresses strong emotions, it can highlight those parts. It can also dynamically change the format of the minutes according to changes in emotions. For example, if a discussion is heated, it can record detailed timestamps of comments. This allows the minutes to reflect changes in emotions.

[0093] The automatic minutes-taking system can also estimate the user's emotions and summarize the conversation based on the estimated emotions. For example, if the speaker is excited, it will summarize that part in detail. It can also change the emphasis of the summary according to changes in emotions. For example, if the speaker is relaxed, it will briefly summarize only the main points. This makes it possible to create a summary that reflects changes in emotions.

[0094] The automatic minutes-taking system can also estimate the user's emotions and determine the importance of a conversation based on the estimated emotions. For example, if a speaker expresses strong emotions, it will mark that part as important. It can also dynamically change the criteria for determining importance according to changes in emotions. For example, if the strength of emotions differs between the beginning and end of a meeting, the system will adjust the importance accordingly. This allows for importance determination that reflects changes in emotions.

[0095] The automated minutes-taking system can also estimate the user's emotions and adjust the tone of the conversation based on the estimated emotions. For example, if the speaker is angry, the system will record that part in a calm tone. It can also dynamically adjust the tone according to changes in emotions. For example, if the speaker is relaxed, the system will record in a friendly tone. This allows the tone to be adjusted to reflect changes in emotions.

[0096] The automatic minutes-taking system can also estimate the user's emotions and analyze the flow of conversation based on the estimated emotions. For example, if the speaker is excited, that part will be analyzed in detail. It can also dynamically change the analysis method of the flow of conversation according to changes in emotions. For example, if the speaker is relaxed, the overall flow will be analyzed briefly. This allows the flow of conversation to be analyzed while reflecting changes in emotions.

[0097] The automatic minutes-creation system also adds an audio data backup function to prevent data loss. For example, audio data can be automatically backed up to the cloud. The frequency of backups can also be set. For example, backups can be performed at a set time every day. Data can also be easily restored using the audio data backup function. For example, data that has been accidentally deleted can be quickly restored. This helps prevent data loss.

[0098] The automatic minutes-creation system can also add a filtering function for audio data, allowing it to automatically remove unnecessary parts. For example, it can remove silent parts and noise. It is also possible to set filtering criteria. For example, it can leave only parts that contain specific keywords. It is also possible to use the filtering function for audio data to extract only important parts. For example, it can automatically extract the main points of a meeting. This allows it to remove unnecessary parts and create minutes efficiently.

[0099] An automatic transcription system can also add a tagging function for audio data, making it easy to search for specific statements. For example, tags can be added based on the speaker's name or the content of what was said. It is also possible to set tagging criteria. For example, important statements or decisions can be tagged. The audio data tagging function can also be used to easily search for specific statements. For example, you can quickly find a specific statement after a meeting. This makes it easy to search for specific statements.

[0100] The automatic minutes-creation system can also add a transcription function for audio data, converting audio into text. For example, it can convert the audio of a meeting into text in real time. It is also possible to set the accuracy of the transcription. For example, it can perform highly accurate text conversion. The audio data transcription function can also be used to easily edit the text data. For example, incorrectly converted parts can be manually corrected. This allows for efficient conversion of audio into text and the creation of minutes.

[0101] The automatic meeting minutes system also adds a voice data translation function, allowing conversations in different languages ​​to be reflected in the minutes. For example, what is said in English can be translated into Japanese and recorded in the minutes. The accuracy of the translation can also be set. For example, high accuracy translation can be performed. The voice data translation function can also be used to translate conversations in multiple languages ​​in real time. For example, remarks made at an international conference can be translated in real time and recorded in the minutes. This allows conversations in different languages ​​to be reflected in the minutes.

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

[0103] Step 1: The voice-registering glasses record the conversation and send the recorded voice data to a PC via wireless communication. For example, the voice-registering glasses can send the voice data to a PC using Bluetooth or Wi-Fi. The voice-registering glasses also have a translation function, allowing them to understand conversations in different languages. For example, they can translate what is spoken in English into Japanese. Step 2: The PC converts the received voice data into text using a generation AI. For example, the generation AI analyzes the voice data using voice recognition technology and generates accurate text. Step 3: The generation AI automatically creates minutes based on the text. The generation AI analyzes the flow of the conversation and key points, and compiles minutes in an appropriate format. For example, it creates minutes by organizing the meeting start time, participants, agenda, content of remarks, and decisions.

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

[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 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.

[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 type 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 type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 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.

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

[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 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.

[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, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[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. Voice registration glasses and PC and Equipped with a generative AI, The voice registration glasses include: Record the conversation and transmit the recorded audio data to the PC via wireless communication. The PC is Converting the received voice data into text using the generation AI; The generated AI is Automatically create minutes based on the text A system characterized by:

2. The voice registration glasses include: Equipped with an emotion estimation function, it records changes in emotions during conversations in real time and reflects them in the minutes.

2. The system of claim 1.

3. The voice registration glasses include: Noise canceling function has been added to enable clear audio recording even in noisy environments.

2. The system of claim 1.

4. The voice registration glasses include: Add a facial recognition function to identify speakers and reflect them in the minutes.

2. The system of claim 1.

5. The voice registration glasses include: It is equipped with a camera and can also record video of conversations, synchronizing the video and audio.

2. The system of claim 1.

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