system

Abstract

We provide the system. [Solution] A method using a terminal to acquire audio data, Means for transmitting the aforementioned audio data to a server, The server includes means for converting the voice data into text data using speech recognition technology and for identifying the speaker, A means for extracting and highlighting important information from the aforementioned text data using natural language processing technology, A means for automatically summarizing the aforementioned character data when it exceeds a certain length, means for transmitting the processed character data to the terminal and displaying it, An information processing system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a meeting, it is important to accurately record the content of the speech in real time and present it in a form that can be referred to later. However, with the current methods, it is difficult to record the content of the speech without omission or to highlight important points and display them clearly. In addition, identifying the speech of each speaker and summarizing long speeches to clarify the key points are major issues in improving the efficiency of meetings and reducing the labor of creating meeting minutes.

Means for Solving the Problems

[0005] This invention ensures real-time performance by using a terminal that acquires audio data in real time and transmits it to a server. The server converts the audio data into text data using speech recognition technology and identifies the speaker by analyzing the characteristics of their voice. Furthermore, it extracts important information from the text data using natural language processing technology and visually highlights it, allowing participants to immediately grasp important statements. In addition, it automatically summarizes long statements, extracting and presenting the main points, so that users can acquire information efficiently. As a result, it is possible to improve the productivity of meetings and reduce the burden of creating meeting minutes.

[0006] "Audio data" refers to information obtained by converting sound waveforms into digital signals, and includes data containing audio information such as that from meetings.

[0007] A "terminal" is a device for acquiring or displaying audio data, and is a computer system that provides an interface with the user.

[0008] A "server" is a centralized computer system that processes data via a network, and is a device used for transcribing audio data and performing information processing.

[0009] "Speech recognition technology" is a technology that analyzes speech as digital data and converts its content into text.

[0010] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract important information from text data.

[0011] "Speaker identification" is a technology that analyzes different speakers contained in audio data to identify the speaker.

[0012] "Summarizing" is the process of extracting the main points from a long text or data and putting them into a concise summary.

[0013] "Visual highlighting" refers to displaying information in a specific format (e.g., red text or bold text) to make it stand out visually. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is an information processing system that quickly and accurately records audio during meetings, converts it into text information in real time, and enables participants to immediately grasp important information. The system acquires audio data, performs advanced analysis processing via a server, and displays it visually on a terminal.

[0036] First, the terminal acquires the conference audio data through the microphone and converts it into digital data in real time. This data is streamed to the server at regular buffer intervals. The server converts the received audio data into text data using speech recognition technology. In this process, the server analyzes the voice characteristics of each speaker to identify who spoke.

[0037] Next, the converted text data is analyzed by the server using natural language processing technology. The server extracts important keywords and phrases from the spoken content and tags them in a highlighted format (e.g., red or bold). Furthermore, if the spoken content is long, an automatic summarization function is used to condense the content and present the main points. This allows users to quickly understand the important information of the meeting.

[0038] Users can view information sent from the server to their terminals through the user interface. The screen, which updates in real time, displays the content of each speaker's remarks, highlighting important points, thus supporting users in quickly grasping the main points of the meeting and taking appropriate action.

[0039] As a concrete example, if User A says "The deadline for the next project is very important" during a meeting, the terminal sends this audio to the server, which uses speech recognition to generate the text "User A: The deadline for the next project is very important." This text is further analyzed, and keywords such as "deadline" and "important" are highlighted in red. Finally, this information is displayed on the user's terminal, allowing the user to check it in real time.

[0040] Thus, the present invention aims to enable rapid recording of meeting contents and efficient provision of important information, thereby realizing the immediate information access that users require.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The terminal captures ambient sound during the meeting using an audio input device (microphone). The acquired audio is digitally converted and stored in a buffer as a data stream in real time.

[0044] Step 2:

[0045] The terminal sends the audio data stored in the buffer to the server at regular intervals using a streaming protocol. The connection to the server is made via a low-latency network to minimize the loss of audio data.

[0046] Step 3:

[0047] The server inputs the received audio data into the speech recognition engine and performs the process of converting the audio information of each utterance into text. The server also captures speaker-specific vocal characteristics from the audio data to identify who made the statement.

[0048] Step 4:

[0049] The server applies natural language processing techniques to the text generated by speech recognition to extract important keywords and phrases. The extracted information is then tagged for visual emphasis.

[0050] Step 5:

[0051] The server automatically summarizes slogans exceeding a certain length. Specifically, a natural language processing model extracts the main points of the text and reconstructs the original information concisely.

[0052] Step 6:

[0053] The server sends the processed text data to the terminal. The terminal displays this information on its user interface, allowing the user to see the updated content and highlights of the message in real time.

[0054] Step 7:

[0055] Based on the information displayed on their devices, users can grasp the meeting content in real time and take additional notes as needed. After the meeting, they can review the information saved on their devices and create meeting minutes or share information as necessary.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] This solution addresses the challenge of quickly and accurately recording and analyzing spoken information in large group settings such as meetings, making it difficult for participants to immediately grasp important information. Furthermore, accurately extracting key information from lengthy speeches is also challenging. This makes it difficult for participants to avoid overlooking or misunderstanding information and to conduct discussions efficiently.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for converting audio information into text information and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for automatically summarizing the text information if it exceeds a certain length. This makes it possible to convert audio information into text information in real time and to highlight and display important points.

[0061] A "computation device" is an information processing device that has the function of acquiring audio information and displaying the processing results via a user interface.

[0062] An "information processing device" is a server that analyzes audio information received from a computer and converts it into text information.

[0063] "Speech recognition technology" is a technology that analyzes speech information and converts it into text information, and has the function of identifying the speaker.

[0064] "Natural language processing technology" is a technique for extracting important information from textual data and visually highlighting the analyzed information.

[0065] "Automatic summarization" is a technology that shortens the content of a statement and extracts the main points when the text information exceeds a certain length.

[0066] A "graphical user interface" is a screen display method that allows users to visually access information and grasp it in real time.

[0067] This information processing system uses a computer and an information processing device to quickly and accurately convert audio information from meetings into text, enabling immediate access to important information. The computer has the function of acquiring audio information via a microphone and transmitting it as digital data to the information processing device in real time. The information processing device converts the received audio information into text using advanced speech recognition technology and identifies the speaker.

[0068] Highly accurate speech recognition software is used for speech recognition, and for example, the Google® Speech-to-Text API can be applied. This converted text information is further analyzed using natural language processing technology. The information processing device extracts important keywords and phrases from the text information and visually highlights them on a graphical user interface. Libraries such as NLTK and SpaCy can be used for natural language processing.

[0069] Furthermore, if a meeting discussion is lengthy, automatic summarization technology can be applied to shorten and extract the main points. This technology allows users to understand the main points of the discussion in real time. The processed information is sent back to the computer and provided through the user interface.

[0070] As a concrete example, consider a scenario in a meeting where User A states, "The deadline for the next project is extremely important." The computer sends this statement to the information processing unit. The information processing unit uses the aforementioned speech recognition technology to generate the text, "User A: The deadline for the next project is extremely important." Natural language processing is then performed, emphasizing keywords such as "deadline" and "important." This processing result is returned to the computer and displayed for the user to quickly review.

[0071] Thus, the present invention enables rapid recording and analysis of spoken content, supporting users' immediate access to information. An example of a prompt sentence generated using the AI ​​model is, "Analyze the audio data from the meeting in real time, extract important keywords, and highlight them." This enables efficient processing and provision of information.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The terminal acquires audio information during a meeting via a microphone. The audio signal is converted into digital data. Specifically, it converts the analog signal into a digital signal using an A / D converter and generates sample data. The input is analog audio from the microphone, and the output is digital audio data.

[0075] Step 2:

[0076] The terminal streams digital audio data to the server at regular buffer time intervals. Specifically, this involves dividing the audio data into segments of a fixed duration (e.g., 5 seconds) and sending them to the server over the network. The input is digital audio data, and the output is the data stream sent to the server.

[0077] Step 3:

[0078] The server converts the received digital audio data into text information using speech recognition technology. This process utilizes the Google Speech-to-Text API to analyze the audio signal and output the audio as text data. The input is digital audio data, and the output is text information generated by speech recognition.

[0079] Step 4:

[0080] The server analyzes the text information obtained through speech recognition using natural language processing techniques. Specifically, it uses NLTK and SpaCy to extract important keywords and phrases from the text and tags them for highlighting. The input is text information, and the output is text information with highlighting tags applied.

[0081] Step 5:

[0082] The server automatically summarizes the converted and parsed text if it exceeds a certain length. In this process, an automatic summarization algorithm understands the context, extracts the main points, and shortens the text. The input is parsed long text information, and the output is summarized text information.

[0083] Step 6:

[0084] The server sends the final processed information to the terminal. During this process, the analysis results are updated in real time. Specifically, textual information, including highlighted points and summaries, is prepared for visualization through the interface. The input is the final processed textual information, and the output is the data sent to the terminal.

[0085] Step 7:

[0086] Users view information sent from the server through a user interface on the computing device. The screen is updated in real time, and important information is highlighted. Specifically, users can operate the interface on the terminal, scrolling through information and taking notes as needed. Input is the data sent to the terminal, and output is the information displayed on the user interface.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In meetings and seminars, there is a problem in that participants often have difficulty grasping important information in real time and effectively. In particular, when the amount of information presented is vast, it is difficult to quickly determine which information is important. Furthermore, because individual participants perceive information from different perspectives, it is difficult to achieve a consistent understanding. In such environments, there is a need for technological means to facilitate the smooth acquisition and sharing of important information.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for converting audio data into text data, means for identifying speakers, and means for highlighting important information using natural language processing techniques. This allows participants to grasp the key points of a meeting in real time. In particular, by extracting and visually highlighting text entities, information is organized in a way that is easy for users to understand, supporting rapid decision-making.

[0092] "Audio data" refers to digital data that records audio, and is an information resource used when analyzing the content of meetings and conversations.

[0093] "Hardware" refers to physical devices used to acquire and process audio data, such as terminals and microphones.

[0094] A "processing unit" is a device or system that performs calculations to analyze transmitted audio data and convert it into text data.

[0095] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data, and includes the process of converting the content of speech into text.

[0096] "Text data" refers to text information converted by speech recognition technology, which encodes the content of a speech.

[0097] "Means for identifying speakers" refers to technologies that analyze the voice characteristics of multiple speakers and have the function of identifying each speaker.

[0098] "Natural language processing technology" is a technique for analyzing text data and extracting important information and concepts, and is particularly used to process human language on computers.

[0099] "Means of highlighting" refer to methods used to visually make important information or keywords stand out, and include techniques such as changing the color or style of the text.

[0100] "Automatic summarization" is a process that shortens long texts while retaining the main points, and is used to efficiently convey information.

[0101] "Interface" refers to the screen display and means of operation that allow users to visually view and manipulate data.

[0102] "Methods for extracting entities" refer to techniques that detect important elements such as specific words or phrases from text and classify the information.

[0103] The system implementing this invention performs the acquisition, analysis, and visualization of audio data in an integrated manner. Specifically, it uses the following hardware and software. The terminal uses a microphone to collect audio data from meetings and seminars. This enables real-time conversion of audio into digital data.

[0104] The terminal streams the collected audio data to the processing unit (server). The server uses speech recognition technology to convert the received audio data into text data. At this time, speaker identification technology can be used to accurately identify each speaker.

[0105] Next, the server analyzes the converted text data using natural language processing technology and extracts important information. This allows the server to highlight the information the user needs and present it in a visually easy-to-understand manner. In the case of long statements, an automatic summarization function is used to effectively aggregate the information.

[0106] The converted and analyzed information is then sent back to the terminal and visualized through the user interface. This allows users to recognize important information in real time and make appropriate decisions.

[0107] For example, if someone says in a meeting, "The deadline for the next project is very important," the server will convert this statement into text and highlight the key keywords, "deadline" and "important." Users can then instantly check this information on their devices and take appropriate action.

[0108] Natural language processing technology incorporating a generative AI model supports advanced analysis and enables the provision of highly accurate information. A concrete example of a prompt is, "Meeting content: Please highlight the key points about the next product." Thus, this development provides an effective information processing system that supports the rapid understanding and sharing of meeting content.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The terminal acquires meeting audio data via a microphone. The input is the meeting audio, and the output is digital audio data. This digitized audio data is captured in real time to prepare it for future processing.

[0112] Step 2:

[0113] The terminal streams the acquired digital audio data to the processing unit (server). The input is digital audio data, and the output is the audio data sent to the server. In this process, data is transmitted quickly and efficiently over the network.

[0114] Step 3:

[0115] The server uses speech recognition technology to convert transmitted audio data into text data. The input is streamed audio data, and the output is the converted text data. The server analyzes the features of the speech and maps the speech to text using an acoustic model.

[0116] Step 4:

[0117] The server identifies speakers within the text data. The input is text data obtained through speech recognition, and the output is text data with each speaker identified. Speaker identification technology is used to analyze the speech patterns of different speakers and assign speakers to them.

[0118] Step 5:

[0119] The server uses natural language processing techniques to extract important information from text data and add markup for highlighting. The input is identified text data, and the output is text data with the important information highlighted. A generative AI model is used to detect entities and identify important words and phrases.

[0120] Step 6:

[0121] The server automatically summarizes text data using a natural language processing model when the data exceeds a certain length. The input is long text data, and the output is text data containing only the summarized main points. Statistical analysis of the text selects the most important information and summarizes it concisely.

[0122] Step 7:

[0123] The server sends processed text data to the terminal and displays it visually through the user interface. The input is highlighted and summarized text data, and the output is a visual information display viewable by the user. Based on the displayed information, the user can instantly grasp the meeting content.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is an information processing system that records audio in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. This system promotes a deeper understanding of information by combining an emotion engine at each stage of audio data acquisition, processing, and display.

[0126] First, the terminal acquires audio during the meeting and transmits the data to the server in real time. The audio data is transferred in digital format, and the server converts it into text data using speech recognition technology. Speaker identification is performed by analyzing the characteristics of the voice.

[0127] Simultaneously, the device captures emotional characteristics such as the user's facial expressions and tone of voice. This data is analyzed by an emotion engine to determine the user's emotional state. The server records this emotional information in combination with text data and assigns it to the text as an emotion tag.

[0128] Next, the server analyzes the text data and uses natural language processing techniques to extract and highlight important information. An automatic summarization function shortens long statements to display the main points. Furthermore, an emotion map generated by the emotion engine visualizes the flow of emotions within the meeting, allowing users to intuitively understand the atmosphere.

[0129] Users can view the processed audio content, highlighted information, and emotional flow through the device's user interface. For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this to text, and the emotion engine assigns an emotion tag such as "anxiety." The device then displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0130] Thus, the present invention aims to improve the quality of meetings by enabling participants to gain deeper insights through multi-layered data processing.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device captures audio data using its microphone at the start of the meeting. The audio data is converted into a digital signal on the spot. In parallel, the device records the user's facial expressions and tone of voice through its camera and microphone, collecting emotion-related data.

[0134] Step 2:

[0135] The device transmits recorded audio and emotion data to the server at regular intervals. Audio data is transferred to the server in real time using a streaming protocol. Similarly, emotion data is also transmitted and processed in parallel.

[0136] Step 3:

[0137] The server converts the received audio data into text data using speech recognition technology. During this process, a speaker identification algorithm is applied to identify the speaker based on the characteristics of the voice. As a result, each statement is clearly recorded, indicating who spoke it and when.

[0138] Step 4:

[0139] The server uses an emotion analysis engine in parallel with processing the audio data to analyze the received emotion data. Based on the user's voice tone and facial expressions, it identifies their emotional state (e.g., joy, anger, anxiety, etc.). This emotion information is tagged in a way that corresponds to their speech.

[0140] Step 5:

[0141] The server further analyzes the converted text data and uses natural language processing techniques to extract important keywords and phrases. The extracted information is then enhanced with highlighting data and presented in a visually recognizable format.

[0142] Step 6:

[0143] The server applies an automated summarization algorithm to summarize statements exceeding a certain length. This summary is designed to retain the main points in a shortened form.

[0144] Step 7:

[0145] The server sends processed text data, sentiment tags, and summary information to the terminal. The terminal displays this information on the user interface and updates it in real time. This system allows users to instantly see the content of speech and the flow of sentiment, and quickly grasp the progress of the meeting.

[0146] Step 8:

[0147] Users can refer to the information provided on their devices, take notes during meetings, and record their reactions to agenda items. Since the information can also be viewed as a log after the meeting, meeting minutes can be reviewed and shared efficiently.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In today's world, while many meetings and discussions take place, it's difficult for participants to accurately grasp all the content and understand the emotional nuances. In particular, simply receiving information without considering the emotional context or the speaker's emotional state can lead to misunderstandings and communication problems among participants. To solve these problems, it's necessary not only to convert audio into text data, but also to add emotional information so that the atmosphere of the meeting can be intuitively understood.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for converting acoustic information into text information using acoustic recognition technology and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for analyzing emotional characteristics, combining emotional information with text information, and generating an emotional map that shows the emotional state of the speaker. This makes it possible to improve the quality of meetings by deepening the understanding of the meeting content and providing emotional context to participants.

[0153] "Acoustic information" refers to data that represents audio signals acquired in settings such as meetings and discussions in a digital format.

[0154] "Equipment" refers to hardware and software used to acquire acoustic information during a meeting, including terminals and microphones.

[0155] A "computer" is a digital device used to receive and process acoustic information, and typically functions as a server.

[0156] "Acoustic recognition technology" is a technology for converting acoustic information into textual information, and it is a technology that uses a speech recognition engine to convert speech into text.

[0157] "Textual information" refers to text data converted from acoustic information using acoustic recognition technology.

[0158] "Means for identifying speakers" refers to analytical techniques for identifying a specific speaker from converted text information.

[0159] "Natural language processing technology" is a technology for analyzing and processing the meaning and importance of textual information.

[0160] "Important information" refers to the key points or conclusions that deserve particular attention within the meeting's content.

[0161] "Emotional characteristics" refer to elements that indicate a speaker's emotional state, such as facial expressions and tone of voice.

[0162] An "emotion map" is a diagram that visually represents the flow of a speaker's emotions during a meeting, and it shows the emotional state of the participants.

[0163] This invention is an information processing system that records acoustic information in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. The entire system mainly consists of a server and terminals.

[0164] The terminal acquires audio information during the meeting via a microphone. This terminal is equipped with high-performance audio filtering software, such as Audacity, to remove background noise and obtain clear audio. This audio information is transmitted to the server in real time via the network.

[0165] The server receives acoustic information and uses a speech recognition engine (e.g., Google Speech-to-Text API) based on acoustic recognition technology to convert the acoustic information into text. The server then identifies the speaker based on the characteristics of the voice. This ensures that who said what is recorded accurately.

[0166] Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and tone of voice, and performs emotion analysis based on this data. An emotion analysis engine (e.g., Microsoft® Azure® Emotion API) is used for the analysis to determine the user's emotional state. The server combines this emotion information with textual data and assigns it as an emotion tag. This ensures that not only the content of what is said, but also the underlying emotional nuances are recorded.

[0167] The server analyzes textual information using natural language processing technologies (e.g., NLTK and spaCy), extracting and highlighting important information. An automatic summarization function can shorten long statements to only the essential points. Furthermore, based on the sentiment-tagged results, it generates an sentiment map showing the overall emotional flow of the meeting.

[0168] In this system embodiment, users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. The visually clear layout allows for an intuitive understanding of the meeting's atmosphere and the emotional state of the participants.

[0169] For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this into text, and sentiment analysis assigns the emotion tag "anxiety." As a result, the terminal displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0170] An example of a prompt for a generative AI model would be: "Please tell me how to add sentiment analysis to a real-time meeting recording system. Also, please suggest ways to make the sentiment map intuitively understandable to the user."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device acquires acoustic information in real time during meetings using a microphone. The input is ambient sound, and the output is digital acoustic information. During audio acquisition, audio filtering software is used to remove background noise. This process ensures that the meeting audio is captured clearly.

[0174] Step 2:

[0175] The terminal transmits the acquired acoustic information to the server via the network. The input is digital acoustic information, and the output is the acoustic information transferred to the server. During transmission, network bandwidth is managed to prevent data interruptions.

[0176] Step 3:

[0177] The server uses a speech recognition engine based on acoustic recognition technology to convert received acoustic information into text information. The input is acoustic information, and the output is text-based character information. During this process, the audio data is analyzed, and profiling is performed to identify a specific speaker.

[0178] Step 4:

[0179] The device uses a camera and microphone to capture the facial expressions and tone of voice of meeting participants. The input is the participants' video and audio, and the output is digital data based on emotional characteristics. This data is then analyzed by an emotion analysis engine, which performs data processing to identify the user's emotional state.

[0180] Step 5:

[0181] The server integrates textual information with sentiment information obtained through sentiment analysis and assigns sentiment tags to the textual information. The input is textual information and sentiment feature data, and the output is textual information with sentiment tags. This processing is performed to add emotional context to the meeting content.

[0182] Step 6:

[0183] The server uses natural language processing technology to analyze textual information, extracting and highlighting important information. The input is sentiment-tagged textual information, and the output is highlighted important information. Furthermore, if the textual information is long, an automatic summarization function is used to concisely summarize the key points.

[0184] Step 7:

[0185] The server analyzes the emotion tags assigned to all statements and generates an emotion map that visually represents the flow of emotions. The input is text information with emotion tags, and the output is an emotion map. This allows meeting participants to intuitively grasp the overall emotional changes.

[0186] Step 8:

[0187] Users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. Input is highlighted information and emotional maps sent from the server, while output is a visual display on the terminal. This allows users to track and understand the meeting content in detail.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] Traditional conferencing systems could acquire audio information and convert it to text, but they had the problem of not being able to grasp, analyze, and share the speaker's emotions in real time. As a result, meeting participants could not accurately understand the emotional changes of other participants, making it difficult to improve the quality of communication. In addition, the extraction of important information and automatic summarization were insufficient, which could lead to information being overlooked or misunderstood in long meetings. There is a need to solve these problems.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes means for transmitting audio information to a processing device, means for converting the audio information into text information using speech recognition technology and identifying the speaker, means for extracting and highlighting important information using information processing technology, and means for analyzing emotions and assigning emotion tags. This makes it possible to analyze and visualize the emotions of speakers during a meeting in real time, thereby improving the quality of communication among participants.

[0193] "Audio information" refers to audio data as an acoustic signal, which can be converted into textual information or emotional information through analysis.

[0194] A "processing device" refers to a computer system that receives, converts, and analyzes acquired audio information.

[0195] "Speech recognition technology" is a technology that analyzes speech and automatically converts it into corresponding text information.

[0196] "Textual information" refers to text data converted using speech recognition technology, which allows for the visual presentation of information.

[0197] "Speaker identification" is an analytical technique used to identify the person who made a statement within audio data.

[0198] "Information processing technology" refers to the technology of extracting important information from textual information and performing tasks such as highlighting and summarizing.

[0199] An "emotion tag" is a label that indicates the emotional state of the speaker and is added to textual information.

[0200] A "user interface" is an interface that provides processed information to the user visually and allows them to operate it.

[0201] The following system is a concrete example of how this invention can be implemented.

[0202] The server receives audio information in real time from multiple audio capture devices installed within the factory. This audio information is first quickly converted into text using the Google Speech-to-Text API. Then, sentiment analysis is performed using Microsoft Azure Text Analytics, and appropriate sentiment tags are assigned to each utterance. These sentiment tags are used to visualize the information as an sentiment map. Through this entire process, the audio information is stored not merely as text, but as data with emotional context.

[0203] The terminal receives text information and emotion tags from the server and displays them in real time on the user interface. Using data visualization libraries such as D3.js, the flow of emotions is visually represented, making it easy for users to understand changes in their emotions.

[0204] Based on the displayed information, users can grasp the progress of meetings and intuitively understand the emotions of the speakers. For example, if an employee says, "We've been having a lot of equipment breakdowns lately, and it's really bothering us," the audio information based on that statement will be recorded with the emotion tag "confused." This allows other participants to not only understand the content but also recognize the underlying emotional elements.

[0205] An example of a prompt might be, "Please show how to analyze and display the emotions expressed by employee A during their report." Through such prompts, the aim is to improve the accuracy of emotion analysis via the AI ​​model.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server receives audio information from an audio capture device installed within the factory. The audio capture device records the audio of meetings and conferences in real time and transmits the data to the server in digital format. The audio information is captured by the server as an unprocessed acoustic signal. At this stage, the input is a digital audio signal, and the output is audio data stored on the server.

[0209] Step 2:

[0210] The server converts received audio information into text using the Google Speech-to-Text API. This speech recognition process parses the audio data into corresponding text data. The input is the audio data stored on the server, and the output is the converted text data. This makes the audio information available in a format that can be visually processed.

[0211] Step 3:

[0212] The server performs sentiment analysis on the converted text data using Microsoft Azure Text Analytics. Sentiment analysis evaluates the emotional characteristics of each statement and assigns appropriate sentiment tags. The input is text data, and the output is text data with sentiment tags attached. This adds emotional context to the textual information.

[0213] Step 4:

[0214] The terminal displays text information and emotion tags received from the server in real time on the user interface. D3.js is used for this visualization, generating graphs and maps that visually represent the flow of emotions. The input is emotion-tagged text data sent from the server, and the output is visual information displayed on the terminal. This allows users to instantly understand the emotions associated with what they say.

[0215] Step 5:

[0216] Based on the information displayed on the device, the user monitors the progress of the meeting or conference and understands the emotions of the speakers. Through this monitoring process, the user effectively adjusts communication within the meeting and, if necessary, inputs prompt sentences into the AI ​​model to improve the accuracy of emotion analysis. The input is the displayed information, and the output is the user's understanding and actions.

[0217] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 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.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is an information processing system that quickly and accurately records audio during meetings, converts it into text information in real time, and enables participants to immediately grasp important information. The system acquires audio data, performs advanced analysis processing via a server, and displays it visually on a terminal.

[0234] First, the terminal acquires the conference audio data through the microphone and converts it into digital data in real time. This data is streamed to the server at regular buffer intervals. The server converts the received audio data into text data using speech recognition technology. In this process, the server analyzes the voice characteristics of each speaker to identify who spoke.

[0235] Next, the converted text data is analyzed by the server using natural language processing technology. The server extracts important keywords and phrases from the spoken content and tags them in a highlighted format (e.g., red or bold). Furthermore, if the spoken content is long, an automatic summarization function is used to condense the content and present the main points. This allows users to quickly understand the important information of the meeting.

[0236] Users can view information sent from the server to their terminals through the user interface. The screen, which updates in real time, displays the content of each speaker's remarks, highlighting important points, thus supporting users in quickly grasping the main points of the meeting and taking appropriate action.

[0237] As a concrete example, if User A says "The deadline for the next project is very important" during a meeting, the terminal sends this audio to the server, which uses speech recognition to generate the text "User A: The deadline for the next project is very important." This text is further analyzed, and keywords such as "deadline" and "important" are highlighted in red. Finally, this information is displayed on the user's terminal, allowing the user to check it in real time.

[0238] Thus, the present invention aims to enable rapid recording of meeting contents and efficient provision of important information, thereby realizing the immediate information access that users require.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The terminal captures ambient sound during the meeting using an audio input device (microphone). The acquired audio is digitally converted and stored in a buffer as a data stream in real time.

[0242] Step 2:

[0243] The terminal sends the audio data stored in the buffer to the server at regular intervals using a streaming protocol. The connection to the server is made via a low-latency network to minimize the loss of audio data.

[0244] Step 3:

[0245] The server inputs the received audio data into the speech recognition engine and performs the process of converting the audio information of each utterance into text. The server also captures speaker-specific vocal characteristics from the audio data to identify who made the statement.

[0246] Step 4:

[0247] The server applies natural language processing techniques to the text generated by speech recognition to extract important keywords and phrases. The extracted information is then tagged for visual emphasis.

[0248] Step 5:

[0249] The server automatically summarizes slogans exceeding a certain length. Specifically, a natural language processing model extracts the main points of the text and reconstructs the original information concisely.

[0250] Step 6:

[0251] The server sends the processed text data to the terminal. The terminal displays this information on its user interface, allowing the user to see the updated content and highlights of the message in real time.

[0252] Step 7:

[0253] Based on the information displayed on their devices, users can grasp the meeting content in real time and take additional notes as needed. After the meeting, they can review the information saved on their devices and create meeting minutes or share information as necessary.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] This solution addresses the challenge of quickly and accurately recording and analyzing spoken information in large group settings such as meetings, making it difficult for participants to immediately grasp important information. Furthermore, accurately extracting key information from lengthy speeches is also challenging. This makes it difficult for participants to avoid overlooking or misunderstanding information and to conduct discussions efficiently.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes means for converting audio information into text information and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for automatically summarizing the text information if it exceeds a certain length. This makes it possible to convert audio information into text information in real time and to highlight and display important points.

[0259] A "computation device" is an information processing device that has the function of acquiring audio information and displaying the processing results via a user interface.

[0260] An "information processing device" is a server that analyzes audio information received from a computer and converts it into text information.

[0261] "Speech recognition technology" is a technology that analyzes speech information and converts it into text information, and has the function of identifying the speaker.

[0262] "Natural language processing technology" is a technique for extracting important information from textual data and visually highlighting the analyzed information.

[0263] "Automatic summarization" is a technology that shortens the content of a statement and extracts the main points when the text information exceeds a certain length.

[0264] A "graphical user interface" is a screen display method that allows users to visually access information and grasp it in real time.

[0265] This information processing system uses a computer and an information processing device to quickly and accurately convert audio information from meetings into text, enabling immediate access to important information. The computer has the function of acquiring audio information via a microphone and transmitting it as digital data to the information processing device in real time. The information processing device converts the received audio information into text using advanced speech recognition technology and identifies the speaker.

[0266] Highly accurate speech recognition software is used for speech recognition, such as the Google Speech-to-Text API. This converted text information is then analyzed using natural language processing techniques. The information processing device extracts important keywords and phrases from the text information and visually highlights them on a graphical user interface. Libraries such as NLTK and SpaCy are available for natural language processing.

[0267] Furthermore, if a meeting discussion is lengthy, automatic summarization technology can be applied to shorten and extract the main points. This technology allows users to understand the main points of the discussion in real time. The processed information is sent back to the computer and provided through the user interface.

[0268] As a concrete example, consider a scenario in a meeting where User A states, "The deadline for the next project is extremely important." The computer sends this statement to the information processing unit. The information processing unit uses the aforementioned speech recognition technology to generate the text, "User A: The deadline for the next project is extremely important." Natural language processing is then performed, emphasizing keywords such as "deadline" and "important." This processing result is returned to the computer and displayed for the user to quickly review.

[0269] Thus, the present invention enables rapid recording and analysis of spoken content, supporting users' immediate access to information. An example of a prompt sentence generated using the AI ​​model is, "Analyze the audio data from the meeting in real time, extract important keywords, and highlight them." This enables efficient processing and provision of information.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The terminal acquires audio information during a meeting via a microphone. The audio signal is converted into digital data. Specifically, it converts the analog signal into a digital signal using an A / D converter and generates sample data. The input is analog audio from the microphone, and the output is digital audio data.

[0273] Step 2:

[0274] The terminal streams digital audio data to the server at regular buffer time intervals. Specifically, this involves dividing the audio data into segments of a fixed duration (e.g., 5 seconds) and sending them to the server over the network. The input is digital audio data, and the output is the data stream sent to the server.

[0275] Step 3:

[0276] The server converts the received digital audio data into text information using speech recognition technology. This process utilizes the Google Speech-to-Text API to analyze the audio signal and output the audio as text data. The input is digital audio data, and the output is text information generated by speech recognition.

[0277] Step 4:

[0278] The server analyzes the text information obtained through speech recognition using natural language processing techniques. Specifically, it uses NLTK and SpaCy to extract important keywords and phrases from the text and tags them for highlighting. The input is text information, and the output is text information with highlighting tags applied.

[0279] Step 5:

[0280] The server automatically summarizes the converted and parsed text if it exceeds a certain length. In this process, an automatic summarization algorithm understands the context, extracts the main points, and shortens the text. The input is parsed long text information, and the output is summarized text information.

[0281] Step 6:

[0282] The server finally transmits the processed information to the terminal. At this time, the analysis result is updated in real time. Specifically, character information including highlighted points and summaries is prepared to be visualized through the interface. The input is the finally processed character information, and the output is the data transmitted to the terminal.

[0283] Step 7:

[0284] The user browses the information sent from the server through the user interface on the computing device. The screen is updated in real time, and important information is highlighted. As a specific operation, the user can operate the interface on the terminal, scroll the information, and take notes as needed. The input is the data transmitted to the terminal, and the output is the information displayed on the user interface.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] [[ID=D19]] In places such as meetings and seminars, there is a problem that it is difficult for participants to effectively grasp important information in real time. Especially when the content of the speech is huge, it is difficult to quickly judge which information is important. Furthermore, since each individual participant grasps information from different perspectives, it is difficult to have a consistent understanding. In such an environment, technical means for smoothly grasping and sharing important information are required.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for converting audio data into text data, means for identifying speakers, and means for highlighting important information using natural language processing techniques. This allows participants to grasp the key points of a meeting in real time. In particular, by extracting and visually highlighting text entities, information is organized in a way that is easy for users to understand, supporting rapid decision-making.

[0290] "Audio data" refers to digital data that records audio, and is an information resource used when analyzing the content of meetings and conversations.

[0291] "Hardware" refers to physical devices used to acquire and process audio data, such as terminals and microphones.

[0292] A "processing unit" is a device or system that performs calculations to analyze transmitted audio data and convert it into text data.

[0293] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data, and includes the process of converting the content of speech into text.

[0294] "Text data" refers to text information converted by speech recognition technology, which encodes the content of a speech.

[0295] "Means for identifying speakers" refers to technologies that analyze the voice characteristics of multiple speakers and have the function of identifying each speaker.

[0296] "Natural language processing technology" is a technique for analyzing text data and extracting important information and concepts, and is particularly used to process human language on computers.

[0297] "Means of highlighting" refer to methods used to visually make important information or keywords stand out, and include techniques such as changing the color or style of the text.

[0298] "Automatic summarization" is a process that shortens long texts while retaining the main points, and is used to efficiently convey information.

[0299] "Interface" refers to the screen display and means of operation that allow users to visually view and manipulate data.

[0300] "Methods for extracting entities" refer to techniques that detect important elements such as specific words or phrases from text and classify the information.

[0301] The system implementing this invention performs the acquisition, analysis, and visualization of audio data in an integrated manner. Specifically, it uses the following hardware and software. The terminal uses a microphone to collect audio data from meetings and seminars. This enables real-time conversion of audio into digital data.

[0302] The terminal streams the collected audio data to the processing unit (server). The server uses speech recognition technology to convert the received audio data into text data. At this time, speaker identification technology can be used to accurately identify each speaker.

[0303] Next, the server analyzes the converted text data using natural language processing technology and extracts important information. This allows the server to highlight the information the user needs and present it in a visually easy-to-understand manner. In the case of long statements, an automatic summarization function is used to effectively aggregate the information.

[0304] The converted and analyzed information is then sent back to the terminal and visualized through the user interface. This allows users to recognize important information in real time and make appropriate decisions.

[0305] As a specific example, when there is a statement in a meeting such as "The deadline for the next project is very important", the server converts this statement into text and highlights the important keywords "deadline" and "important". The user can immediately check this information through the terminal and take appropriate actions as needed.

[0306] The natural language processing technology implementing the generative AI model supports advanced analysis and enables the provision of highly accurate information. As an example of a specific prompt sentence, "Content of the meeting: Please obtain and highlight the important points regarding the next product" can be cited. Thus, this development provides an effective information processing system that supports the rapid understanding and sharing of meeting content.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The terminal acquires the voice data of the meeting through the microphone. The input is the voice of the meeting, and the output is the voice data in digital format. This digitized voice data is captured in real time to prepare for future processing.

[0310] Step 2:

[0311] The terminal streams and transmits the acquired digital voice data to the computing device (server). The input is the digital voice data, and the output is the voice data transmitted to the server. In this process, the data is transmitted quickly and efficiently via the network.

[0312] Step 3:

[0313] The server uses speech recognition technology to convert transmitted audio data into text data. The input is streamed audio data, and the output is the converted text data. The server analyzes the features of the speech and maps the speech to text using an acoustic model.

[0314] Step 4:

[0315] The server identifies speakers within the text data. The input is text data obtained through speech recognition, and the output is text data with each speaker identified. Speaker identification technology is used to analyze the speech patterns of different speakers and assign speakers to them.

[0316] Step 5:

[0317] The server uses natural language processing techniques to extract important information from text data and add markup for highlighting. The input is identified text data, and the output is text data with the important information highlighted. A generative AI model is used to detect entities and identify important words and phrases.

[0318] Step 6:

[0319] The server automatically summarizes text data using a natural language processing model when the data exceeds a certain length. The input is long text data, and the output is text data containing only the summarized main points. Statistical analysis of the text selects the most important information and summarizes it concisely.

[0320] Step 7:

[0321] The server sends processed text data to the terminal and displays it visually through the user interface. The input is highlighted and summarized text data, and the output is a visual information display viewable by the user. Based on the displayed information, the user can instantly grasp the meeting content.

[0322] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0323] This invention is an information processing system that records audio in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. This system promotes a deeper understanding of information by combining an emotion engine at each stage of audio data acquisition, processing, and display.

[0324] First, the terminal acquires audio during the meeting and transmits the data to the server in real time. The audio data is transferred in digital format, and the server converts it into text data using speech recognition technology. Speaker identification is performed by analyzing the characteristics of the voice.

[0325] Simultaneously, the device captures emotional characteristics such as the user's facial expressions and tone of voice. This data is analyzed by an emotion engine to determine the user's emotional state. The server records this emotional information in combination with text data and assigns it to the text as an emotion tag.

[0326] Next, the server analyzes the text data and uses natural language processing techniques to extract and highlight important information. An automatic summarization function shortens long statements to display the main points. Furthermore, an emotion map generated by the emotion engine visualizes the flow of emotions within the meeting, allowing users to intuitively understand the atmosphere.

[0327] Users can view the processed audio content, highlighted information, and emotional flow through the device's user interface. For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this to text, and the emotion engine assigns an emotion tag such as "anxiety." The device then displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0328] Thus, the present invention aims to improve the quality of meetings by enabling participants to gain deeper insights through multi-layered data processing.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The device captures audio data using its microphone at the start of the meeting. The audio data is converted into a digital signal on the spot. In parallel, the device records the user's facial expressions and tone of voice through its camera and microphone, collecting emotion-related data.

[0332] Step 2:

[0333] The device transmits recorded audio and emotion data to the server at regular intervals. Audio data is transferred to the server in real time using a streaming protocol. Similarly, emotion data is also transmitted and processed in parallel.

[0334] Step 3:

[0335] The server converts the received audio data into text data using speech recognition technology. During this process, a speaker identification algorithm is applied to identify the speaker based on the characteristics of the voice. As a result, each statement is clearly recorded, indicating who spoke it and when.

[0336] Step 4:

[0337] The server uses an emotion analysis engine in parallel with processing the audio data to analyze the received emotion data. Based on the user's voice tone and facial expressions, it identifies their emotional state (e.g., joy, anger, anxiety, etc.). This emotion information is tagged in a way that corresponds to their speech.

[0338] Step 5:

[0339] The server further analyzes the converted text data and uses natural language processing techniques to extract important keywords and phrases. The extracted information is then enhanced with highlighting data and presented in a visually recognizable format.

[0340] Step 6:

[0341] The server applies an automated summarization algorithm to summarize statements exceeding a certain length. This summary is designed to retain the main points in a shortened form.

[0342] Step 7:

[0343] The server sends processed text data, sentiment tags, and summary information to the terminal. The terminal displays this information on the user interface and updates it in real time. This system allows users to instantly see the content of speech and the flow of sentiment, and quickly grasp the progress of the meeting.

[0344] Step 8:

[0345] Users can refer to the information provided on their devices, take notes during meetings, and record their reactions to agenda items. Since the information can also be viewed as a log after the meeting, meeting minutes can be reviewed and shared efficiently.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] In today's world, while many meetings and discussions take place, it's difficult for participants to accurately grasp all the content and understand the emotional nuances. In particular, simply receiving information without considering the emotional context or the speaker's emotional state can lead to misunderstandings and communication problems among participants. To solve these problems, it's necessary not only to convert audio into text data, but also to add emotional information so that the atmosphere of the meeting can be intuitively understood.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for converting acoustic information into text information using acoustic recognition technology and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for analyzing emotional characteristics, combining emotional information with text information, and generating an emotional map that shows the emotional state of the speaker. This makes it possible to improve the quality of meetings by deepening the understanding of the meeting content and providing emotional context to participants.

[0351] "Acoustic information" refers to data that represents audio signals acquired in settings such as meetings and discussions in a digital format.

[0352] "Equipment" refers to hardware and software used to acquire acoustic information during a meeting, including terminals and microphones.

[0353] A "computer" is a digital device used to receive and process acoustic information, and typically functions as a server.

[0354] "Acoustic recognition technology" is a technology for converting acoustic information into textual information, and it is a technology that uses a speech recognition engine to convert speech into text.

[0355] "Textual information" refers to text data converted from acoustic information using acoustic recognition technology.

[0356] "Means for identifying speakers" refers to analytical techniques for identifying a specific speaker from converted text information.

[0357] "Natural language processing technology" is a technology for analyzing and processing the meaning and importance of textual information.

[0358] "Important information" refers to the key points or conclusions that deserve particular attention within the meeting's content.

[0359] "Emotional characteristics" refer to elements that indicate a speaker's emotional state, such as facial expressions and tone of voice.

[0360] An "emotion map" is a diagram that visually represents the flow of a speaker's emotions during a meeting, and it shows the emotional state of the participants.

[0361] This invention is an information processing system that records acoustic information in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. The entire system mainly consists of a server and terminals.

[0362] The terminal acquires audio information during the meeting via a microphone. This terminal is equipped with high-performance audio filtering software, such as Audacity, to remove background noise and obtain clear audio. This audio information is transmitted to the server in real time via the network.

[0363] The server receives acoustic information and uses a speech recognition engine (e.g., Google Speech-to-Text API) based on acoustic recognition technology to convert the acoustic information into text. The server then identifies the speaker based on the characteristics of the voice. This ensures that who said what is recorded accurately.

[0364] Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and tone of voice, and performs emotion analysis based on this data. An emotion analysis engine (e.g., Microsoft Azure Emotion API) is used to determine the user's emotional state. The server combines this emotion information with textual data and assigns it as an emotion tag. This ensures that not only the content of what is said, but also the underlying emotional nuances are recorded.

[0365] The server analyzes textual information using natural language processing technologies (e.g., NLTK and spaCy), extracting and highlighting important information. An automatic summarization function can shorten long statements to only the essential points. Furthermore, based on the sentiment-tagged results, it generates an sentiment map showing the overall emotional flow of the meeting.

[0366] In this system embodiment, users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. The visually clear layout allows for an intuitive understanding of the meeting's atmosphere and the emotional state of the participants.

[0367] For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this into text, and sentiment analysis assigns the emotion tag "anxiety." As a result, the terminal displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0368] An example of a prompt for a generative AI model would be: "Please tell me how to add sentiment analysis to a real-time meeting recording system. Also, please suggest ways to make the sentiment map intuitively understandable to the user."

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The device acquires acoustic information in real time during meetings using a microphone. The input is ambient sound, and the output is digital acoustic information. During audio acquisition, audio filtering software is used to remove background noise. This process ensures that the meeting audio is captured clearly.

[0372] Step 2:

[0373] The terminal transmits the acquired acoustic information to the server via the network. The input is digital acoustic information, and the output is the acoustic information transferred to the server. During transmission, network bandwidth is managed to prevent data interruptions.

[0374] Step 3:

[0375] The server uses a speech recognition engine based on acoustic recognition technology to convert received acoustic information into text information. The input is acoustic information, and the output is text-based character information. During this process, the audio data is analyzed, and profiling is performed to identify a specific speaker.

[0376] Step 4:

[0377] The device uses a camera and microphone to capture the facial expressions and tone of voice of meeting participants. The input is the participants' video and audio, and the output is digital data based on emotional characteristics. This data is then analyzed by an emotion analysis engine, which performs data processing to identify the user's emotional state.

[0378] Step 5:

[0379] The server integrates textual information with sentiment information obtained through sentiment analysis and assigns sentiment tags to the textual information. The input is textual information and sentiment feature data, and the output is textual information with sentiment tags. This processing is performed to add emotional context to the meeting content.

[0380] Step 6:

[0381] The server uses natural language processing technology to analyze textual information, extracting and highlighting important information. The input is sentiment-tagged textual information, and the output is highlighted important information. Furthermore, if the textual information is long, an automatic summarization function is used to concisely summarize the key points.

[0382] Step 7:

[0383] The server analyzes the emotion tags assigned to all statements and generates an emotion map that visually represents the flow of emotions. The input is text information with emotion tags, and the output is an emotion map. This allows meeting participants to intuitively grasp the overall emotional changes.

[0384] Step 8:

[0385] Users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. Input is highlighted information and emotional maps sent from the server, while output is a visual display on the terminal. This allows users to track and understand the meeting content in detail.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] Traditional conferencing systems could acquire audio information and convert it to text, but they had the problem of not being able to grasp, analyze, and share the speaker's emotions in real time. As a result, meeting participants could not accurately understand the emotional changes of other participants, making it difficult to improve the quality of communication. In addition, the extraction of important information and automatic summarization were insufficient, which could lead to information being overlooked or misunderstood in long meetings. There is a need to solve these problems.

[0389] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0390] In this invention, the server includes means for transmitting audio information to a processing device, means for converting the audio information into text information using speech recognition technology and identifying the speaker, means for extracting and highlighting important information using information processing technology, and means for analyzing emotions and assigning emotion tags. This makes it possible to analyze and visualize the emotions of speakers during a meeting in real time, thereby improving the quality of communication among participants.

[0391] "Audio information" refers to audio data as an acoustic signal, which can be converted into textual information or emotional information through analysis.

[0392] A "processing device" refers to a computer system that receives, converts, and analyzes acquired audio information.

[0393] "Speech recognition technology" is a technology that analyzes speech and automatically converts it into corresponding text information.

[0394] "Textual information" refers to text data converted using speech recognition technology, which allows for the visual presentation of information.

[0395] "Speaker identification" is an analytical technique used to identify the person who made a statement within audio data.

[0396] "Information processing technology" refers to the technology of extracting important information from textual information and performing tasks such as highlighting and summarizing.

[0397] An "emotion tag" is a label that indicates the emotional state of the speaker and is added to textual information.

[0398] A "user interface" is an interface that provides processed information to the user visually and allows them to operate it.

[0399] The following system is a concrete example of how this invention can be implemented.

[0400] The server receives audio information in real time from multiple audio capture devices installed within the factory. This audio information is first quickly converted into text using the Google Speech-to-Text API. Then, sentiment analysis is performed using Microsoft Azure Text Analytics, and appropriate sentiment tags are assigned to each utterance. These sentiment tags are used to visualize the information as an sentiment map. Through this entire process, the audio information is stored not merely as text, but as data with emotional context.

[0401] The terminal receives text information and emotion tags from the server and displays them in real time on the user interface. Using data visualization libraries such as D3.js, the flow of emotions is visually represented, making it easy for users to understand changes in their emotions.

[0402] Based on the displayed information, users can grasp the progress of meetings and intuitively understand the emotions of the speakers. For example, if an employee says, "We've been having a lot of equipment breakdowns lately, and it's really bothering us," the audio information based on that statement will be recorded with the emotion tag "confused." This allows other participants to not only understand the content but also recognize the underlying emotional elements.

[0403] An example of a prompt might be, "Please show how to analyze and display the emotions expressed by employee A during their report." Through such prompts, the aim is to improve the accuracy of emotion analysis via the AI ​​model.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The server receives audio information from an audio capture device installed within the factory. The audio capture device records the audio of meetings and conferences in real time and transmits the data to the server in digital format. The audio information is captured by the server as an unprocessed acoustic signal. At this stage, the input is a digital audio signal, and the output is audio data stored on the server.

[0407] Step 2:

[0408] The server converts received audio information into text using the Google Speech-to-Text API. This speech recognition process parses the audio data into corresponding text data. The input is the audio data stored on the server, and the output is the converted text data. This makes the audio information available in a format that can be visually processed.

[0409] Step 3:

[0410] The server performs sentiment analysis on the converted text data using Microsoft Azure Text Analytics. Sentiment analysis evaluates the emotional characteristics of each statement and assigns appropriate sentiment tags. The input is text data, and the output is text data with sentiment tags attached. This adds emotional context to the textual information.

[0411] Step 4:

[0412] The terminal displays text information and emotion tags received from the server in real time on the user interface. D3.js is used for this visualization, generating graphs and maps that visually represent the flow of emotions. The input is emotion-tagged text data sent from the server, and the output is visual information displayed on the terminal. This allows users to instantly understand the emotions associated with what they say.

[0413] Step 5:

[0414] Based on the information displayed on the device, the user monitors the progress of the meeting or conference and understands the emotions of the speakers. Through this monitoring process, the user effectively adjusts communication within the meeting and, if necessary, inputs prompt sentences into the AI ​​model to improve the accuracy of emotion analysis. The input is the displayed information, and the output is the user's understanding and actions.

[0415] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0426] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention is an information processing system that quickly and accurately records audio during meetings, converts it into text information in real time, and enables participants to immediately grasp important information. The system acquires audio data, performs advanced analysis processing via a server, and displays it visually on a terminal.

[0432] First, the terminal acquires the conference audio data through the microphone and converts it into digital data in real time. This data is streamed to the server at regular buffer intervals. The server converts the received audio data into text data using speech recognition technology. In this process, the server analyzes the voice characteristics of each speaker to identify who spoke.

[0433] Next, the converted text data is analyzed by the server using natural language processing technology. The server extracts important keywords and phrases from the spoken content and tags them in a highlighted format (e.g., red or bold). Furthermore, if the spoken content is long, an automatic summarization function is used to condense the content and present the main points. This allows users to quickly understand the important information of the meeting.

[0434] Users can view information sent from the server to their terminals through the user interface. The screen, which updates in real time, displays the content of each speaker's remarks, highlighting important points, thus supporting users in quickly grasping the main points of the meeting and taking appropriate action.

[0435] As a concrete example, if User A says "The deadline for the next project is very important" during a meeting, the terminal sends this audio to the server, which uses speech recognition to generate the text "User A: The deadline for the next project is very important." This text is further analyzed, and keywords such as "deadline" and "important" are highlighted in red. Finally, this information is displayed on the user's terminal, allowing the user to check it in real time.

[0436] Thus, the present invention aims to enable rapid recording of meeting contents and efficient provision of important information, thereby realizing the immediate information access that users require.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The terminal captures ambient sound during the meeting using an audio input device (microphone). The acquired audio is digitally converted and stored in a buffer as a data stream in real time.

[0440] Step 2:

[0441] The terminal sends the audio data stored in the buffer to the server at regular intervals using a streaming protocol. The connection to the server is made via a low-latency network to minimize the loss of audio data.

[0442] Step 3:

[0443] The server inputs the received audio data into the speech recognition engine and performs the process of converting the audio information of each utterance into text. The server also captures speaker-specific vocal characteristics from the audio data to identify who made the statement.

[0444] Step 4:

[0445] The server applies natural language processing techniques to the text generated by speech recognition to extract important keywords and phrases. The extracted information is then tagged for visual emphasis.

[0446] Step 5:

[0447] The server automatically summarizes slogans exceeding a certain length. Specifically, a natural language processing model extracts the main points of the text and reconstructs the original information concisely.

[0448] Step 6:

[0449] The server sends the processed text data to the terminal. The terminal displays this information on its user interface, allowing the user to see the updated content and highlights of the message in real time.

[0450] Step 7:

[0451] Based on the information displayed on their devices, users can grasp the meeting content in real time and take additional notes as needed. After the meeting, they can review the information saved on their devices and create meeting minutes or share information as necessary.

[0452] (Example 1)

[0453] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] This solution addresses the challenge of quickly and accurately recording and analyzing spoken information in large group settings such as meetings, making it difficult for participants to immediately grasp important information. Furthermore, accurately extracting key information from lengthy speeches is also challenging. This makes it difficult for participants to avoid overlooking or misunderstanding information and to conduct discussions efficiently.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0456] In this invention, the server includes means for converting audio information into text information and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for automatically summarizing the text information if it exceeds a certain length. This makes it possible to convert audio information into text information in real time and to highlight and display important points.

[0457] A "computation device" is an information processing device that has the function of acquiring audio information and displaying the processing results via a user interface.

[0458] An "information processing device" is a server that analyzes audio information received from a computer and converts it into text information.

[0459] "Speech recognition technology" is a technology that analyzes speech information and converts it into text information, and has the function of identifying the speaker.

[0460] "Natural language processing technology" is a technique for extracting important information from textual data and visually highlighting the analyzed information.

[0461] "Automatic summarization" is a technology that shortens the content of a statement and extracts the main points when the text information exceeds a certain length.

[0462] A "graphical user interface" is a screen display method that allows users to visually access information and grasp it in real time.

[0463] This information processing system uses a computer and an information processing device to quickly and accurately convert audio information from meetings into text, enabling immediate access to important information. The computer has the function of acquiring audio information via a microphone and transmitting it as digital data to the information processing device in real time. The information processing device converts the received audio information into text using advanced speech recognition technology and identifies the speaker.

[0464] Highly accurate speech recognition software is used for speech recognition, such as the Google Speech-to-Text API. This converted text information is then analyzed using natural language processing techniques. The information processing device extracts important keywords and phrases from the text information and visually highlights them on a graphical user interface. Libraries such as NLTK and SpaCy are available for natural language processing.

[0465] Furthermore, if a meeting discussion is lengthy, automatic summarization technology can be applied to shorten and extract the main points. This technology allows users to understand the main points of the discussion in real time. The processed information is sent back to the computer and provided through the user interface.

[0466] As a concrete example, consider a scenario in a meeting where User A states, "The deadline for the next project is extremely important." The computer sends this statement to the information processing unit. The information processing unit uses the aforementioned speech recognition technology to generate the text, "User A: The deadline for the next project is extremely important." Natural language processing is then performed, emphasizing keywords such as "deadline" and "important." This processing result is returned to the computer and displayed for the user to quickly review.

[0467] Thus, the present invention enables rapid recording and analysis of spoken content, supporting users' immediate access to information. An example of a prompt sentence generated using the AI ​​model is, "Analyze the audio data from the meeting in real time, extract important keywords, and highlight them." This enables efficient processing and provision of information.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The terminal acquires audio information during a meeting via a microphone. The audio signal is converted into digital data. Specifically, it converts the analog signal into a digital signal using an A / D converter and generates sample data. The input is analog audio from the microphone, and the output is digital audio data.

[0471] Step 2:

[0472] The terminal streams digital audio data to the server at regular buffer time intervals. Specifically, this involves dividing the audio data into segments of a fixed duration (e.g., 5 seconds) and sending them to the server over the network. The input is digital audio data, and the output is the data stream sent to the server.

[0473] Step 3:

[0474] The server converts the received digital audio data into text information using speech recognition technology. This process utilizes the Google Speech-to-Text API to analyze the audio signal and output the audio as text data. The input is digital audio data, and the output is text information generated by speech recognition.

[0475] Step 4:

[0476] The server analyzes the text information obtained through speech recognition using natural language processing techniques. Specifically, it uses NLTK and SpaCy to extract important keywords and phrases from the text and tags them for highlighting. The input is text information, and the output is text information with highlighting tags applied.

[0477] Step 5:

[0478] The server automatically summarizes the converted and parsed text if it exceeds a certain length. In this process, an automatic summarization algorithm understands the context, extracts the main points, and shortens the text. The input is parsed long text information, and the output is summarized text information.

[0479] Step 6:

[0480] The server sends the final processed information to the terminal. During this process, the analysis results are updated in real time. Specifically, textual information, including highlighted points and summaries, is prepared for visualization through the interface. The input is the final processed textual information, and the output is the data sent to the terminal.

[0481] Step 7:

[0482] Users view information sent from the server through a user interface on the computing device. The screen is updated in real time, and important information is highlighted. Specifically, users can operate the interface on the terminal, scrolling through information and taking notes as needed. Input is the data sent to the terminal, and output is the information displayed on the user interface.

[0483] (Application Example 1)

[0484] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] In meetings and seminars, there is a problem in that participants often have difficulty grasping important information in real time and effectively. In particular, when the amount of information presented is vast, it is difficult to quickly determine which information is important. Furthermore, because individual participants perceive information from different perspectives, it is difficult to achieve a consistent understanding. In such environments, there is a need for technological means to facilitate the smooth acquisition and sharing of important information.

[0486] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0487] In this invention, the server includes means for converting audio data into text data, means for identifying speakers, and means for highlighting important information using natural language processing techniques. This allows participants to grasp the key points of a meeting in real time. In particular, by extracting and visually highlighting text entities, information is organized in a way that is easy for users to understand, supporting rapid decision-making.

[0488] "Audio data" refers to digital data that records audio, and is an information resource used when analyzing the content of meetings and conversations.

[0489] "Hardware" refers to physical devices used to acquire and process audio data, such as terminals and microphones.

[0490] A "processing unit" is a device or system that performs calculations to analyze transmitted audio data and convert it into text data.

[0491] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data, and includes the process of converting the content of speech into text.

[0492] "Text data" refers to text information converted by speech recognition technology, which encodes the content of a speech.

[0493] "Means for identifying speakers" refers to technologies that analyze the voice characteristics of multiple speakers and have the function of identifying each speaker.

[0494] "Natural language processing technology" is a technique for analyzing text data and extracting important information and concepts, and is particularly used to process human language on computers.

[0495] "Means of highlighting" refer to methods used to visually make important information or keywords stand out, and include techniques such as changing the color or style of the text.

[0496] "Automatic summarization" is a process that shortens long texts while retaining the main points, and is used to efficiently convey information.

[0497] "Interface" refers to the screen display and means of operation that allow users to visually view and manipulate data.

[0498] "Methods for extracting entities" refer to techniques that detect important elements such as specific words or phrases from text and classify the information.

[0499] The system implementing this invention performs the acquisition, analysis, and visualization of audio data in an integrated manner. Specifically, it uses the following hardware and software. The terminal uses a microphone to collect audio data from meetings and seminars. This enables real-time conversion of audio into digital data.

[0500] The terminal streams the collected audio data to the processing unit (server). The server uses speech recognition technology to convert the received audio data into text data. At this time, speaker identification technology can be used to accurately identify each speaker.

[0501] Next, the server analyzes the converted text data using natural language processing technology and extracts important information. This allows the server to highlight the information the user needs and present it in a visually easy-to-understand manner. In the case of long statements, an automatic summarization function is used to effectively aggregate the information.

[0502] The converted and analyzed information is then sent back to the terminal and visualized through the user interface. This allows users to recognize important information in real time and make appropriate decisions.

[0503] For example, if someone says in a meeting, "The deadline for the next project is very important," the server will convert this statement into text and highlight the key keywords, "deadline" and "important." Users can then instantly check this information on their devices and take appropriate action.

[0504] Natural language processing technology incorporating a generative AI model supports advanced analysis and enables the provision of highly accurate information. A concrete example of a prompt is, "Meeting content: Please highlight the key points about the next product." Thus, this development provides an effective information processing system that supports the rapid understanding and sharing of meeting content.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The terminal acquires meeting audio data via a microphone. The input is the meeting audio, and the output is digital audio data. This digitized audio data is captured in real time to prepare it for future processing.

[0508] Step 2:

[0509] The terminal streams the acquired digital audio data to the processing unit (server). The input is digital audio data, and the output is the audio data sent to the server. In this process, data is transmitted quickly and efficiently over the network.

[0510] Step 3:

[0511] The server uses speech recognition technology to convert transmitted audio data into text data. The input is streamed audio data, and the output is the converted text data. The server analyzes the features of the speech and maps the speech to text using an acoustic model.

[0512] Step 4:

[0513] The server identifies speakers within the text data. The input is text data obtained through speech recognition, and the output is text data with each speaker identified. Speaker identification technology is used to analyze the speech patterns of different speakers and assign speakers to them.

[0514] Step 5:

[0515] The server uses natural language processing techniques to extract important information from text data and add markup for highlighting. The input is identified text data, and the output is text data with the important information highlighted. A generative AI model is used to detect entities and identify important words and phrases.

[0516] Step 6:

[0517] The server automatically summarizes text data using a natural language processing model when the data exceeds a certain length. The input is long text data, and the output is text data containing only the summarized main points. Statistical analysis of the text selects the most important information and summarizes it concisely.

[0518] Step 7:

[0519] The server sends processed text data to the terminal and displays it visually through the user interface. The input is highlighted and summarized text data, and the output is a visual information display viewable by the user. Based on the displayed information, the user can instantly grasp the meeting content.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention is an information processing system that records audio in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. This system promotes a deeper understanding of information by combining an emotion engine at each stage of audio data acquisition, processing, and display.

[0522] First, the terminal acquires audio during the meeting and transmits the data to the server in real time. The audio data is transferred in digital format, and the server converts it into text data using speech recognition technology. Speaker identification is performed by analyzing the characteristics of the voice.

[0523] Simultaneously, the device captures emotional characteristics such as the user's facial expressions and tone of voice. This data is analyzed by an emotion engine to determine the user's emotional state. The server records this emotional information in combination with text data and assigns it to the text as an emotion tag.

[0524] Next, the server analyzes the text data and uses natural language processing techniques to extract and highlight important information. An automatic summarization function shortens long statements to display the main points. Furthermore, an emotion map generated by the emotion engine visualizes the flow of emotions within the meeting, allowing users to intuitively understand the atmosphere.

[0525] Users can view the processed audio content, highlighted information, and emotional flow through the device's user interface. For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this to text, and the emotion engine assigns an emotion tag such as "anxiety." The device then displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0526] Thus, the present invention aims to improve the quality of meetings by enabling participants to gain deeper insights through multi-layered data processing.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The device captures audio data using its microphone at the start of the meeting. The audio data is converted into a digital signal on the spot. In parallel, the device records the user's facial expressions and tone of voice through its camera and microphone, collecting emotion-related data.

[0530] Step 2:

[0531] The device transmits recorded audio and emotion data to the server at regular intervals. Audio data is transferred to the server in real time using a streaming protocol. Similarly, emotion data is also transmitted and processed in parallel.

[0532] Step 3:

[0533] The server converts the received audio data into text data using speech recognition technology. During this process, a speaker identification algorithm is applied to identify the speaker based on the characteristics of the voice. As a result, each statement is clearly recorded, indicating who spoke it and when.

[0534] Step 4:

[0535] The server uses an emotion analysis engine in parallel with processing the audio data to analyze the received emotion data. Based on the user's voice tone and facial expressions, it identifies their emotional state (e.g., joy, anger, anxiety, etc.). This emotion information is tagged in a way that corresponds to their speech.

[0536] Step 5:

[0537] The server further analyzes the converted text data and uses natural language processing techniques to extract important keywords and phrases. The extracted information is then enhanced with highlighting data and presented in a visually recognizable format.

[0538] Step 6:

[0539] The server applies an automated summarization algorithm to summarize statements exceeding a certain length. This summary is designed to retain the main points in a shortened form.

[0540] Step 7:

[0541] The server sends processed text data, sentiment tags, and summary information to the terminal. The terminal displays this information on the user interface and updates it in real time. This system allows users to instantly see the content of speech and the flow of sentiment, and quickly grasp the progress of the meeting.

[0542] Step 8:

[0543] Users can refer to the information provided on their devices, take notes during meetings, and record their reactions to agenda items. Since the information can also be viewed as a log after the meeting, meeting minutes can be reviewed and shared efficiently.

[0544] (Example 2)

[0545] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] In today's world, while many meetings and discussions take place, it's difficult for participants to accurately grasp all the content and understand the emotional nuances. In particular, simply receiving information without considering the emotional context or the speaker's emotional state can lead to misunderstandings and communication problems among participants. To solve these problems, it's necessary not only to convert audio into text data, but also to add emotional information so that the atmosphere of the meeting can be intuitively understood.

[0547] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0548] In this invention, the server includes means for converting acoustic information into text information using acoustic recognition technology and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for analyzing emotional characteristics, combining emotional information with text information, and generating an emotional map that shows the emotional state of the speaker. This makes it possible to improve the quality of meetings by deepening the understanding of the meeting content and providing emotional context to participants.

[0549] "Acoustic information" refers to data that represents audio signals acquired in settings such as meetings and discussions in a digital format.

[0550] "Equipment" refers to hardware and software used to acquire acoustic information during a meeting, including terminals and microphones.

[0551] A "computer" is a digital device used to receive and process acoustic information, and typically functions as a server.

[0552] "Acoustic recognition technology" is a technology for converting acoustic information into textual information, and it is a technology that uses a speech recognition engine to convert speech into text.

[0553] "Textual information" refers to text data converted from acoustic information using acoustic recognition technology.

[0554] "Means for identifying speakers" refers to analytical techniques for identifying a specific speaker from converted text information.

[0555] "Natural language processing technology" is a technology for analyzing and processing the meaning and importance of textual information.

[0556] "Important information" refers to the key points or conclusions that deserve particular attention within the meeting's content.

[0557] "Emotional characteristics" refer to elements that indicate a speaker's emotional state, such as facial expressions and tone of voice.

[0558] An "emotion map" is a diagram that visually represents the flow of a speaker's emotions during a meeting, and it shows the emotional state of the participants.

[0559] This invention is an information processing system that records acoustic information in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. The entire system mainly consists of a server and terminals.

[0560] The terminal acquires audio information during the meeting via a microphone. This terminal is equipped with high-performance audio filtering software, such as Audacity, to remove background noise and obtain clear audio. This audio information is transmitted to the server in real time via the network.

[0561] The server receives acoustic information and uses a speech recognition engine (e.g., Google Speech-to-Text API) based on acoustic recognition technology to convert the acoustic information into text. The server then identifies the speaker based on the characteristics of the voice. This ensures that who said what is recorded accurately.

[0562] Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and tone of voice, and performs emotion analysis based on this data. An emotion analysis engine (e.g., Microsoft Azure Emotion API) is used to determine the user's emotional state. The server combines this emotion information with textual data and assigns it as an emotion tag. This ensures that not only the content of what is said, but also the underlying emotional nuances are recorded.

[0563] The server analyzes textual information using natural language processing technologies (e.g., NLTK and spaCy), extracting and highlighting important information. An automatic summarization function can shorten long statements to only the essential points. Furthermore, based on the sentiment-tagged results, it generates an sentiment map showing the overall emotional flow of the meeting.

[0564] In this system embodiment, users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. The visually clear layout allows for an intuitive understanding of the meeting's atmosphere and the emotional state of the participants.

[0565] For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this into text, and sentiment analysis assigns the emotion tag "anxiety." As a result, the terminal displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0566] An example of a prompt for a generative AI model would be: "Please tell me how to add sentiment analysis to a real-time meeting recording system. Also, please suggest ways to make the sentiment map intuitively understandable to the user."

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The device acquires acoustic information in real time during meetings using a microphone. The input is ambient sound, and the output is digital acoustic information. During audio acquisition, audio filtering software is used to remove background noise. This process ensures that the meeting audio is captured clearly.

[0570] Step 2:

[0571] The terminal transmits the acquired acoustic information to the server via the network. The input is digital acoustic information, and the output is the acoustic information transferred to the server. During transmission, network bandwidth is managed to prevent data interruptions.

[0572] Step 3:

[0573] The server uses a speech recognition engine based on acoustic recognition technology to convert received acoustic information into text information. The input is acoustic information, and the output is text-based character information. During this process, the audio data is analyzed, and profiling is performed to identify a specific speaker.

[0574] Step 4:

[0575] The device uses a camera and microphone to capture the facial expressions and tone of voice of meeting participants. The input is the participants' video and audio, and the output is digital data based on emotional characteristics. This data is then analyzed by an emotion analysis engine, which performs data processing to identify the user's emotional state.

[0576] Step 5:

[0577] The server integrates textual information with sentiment information obtained through sentiment analysis and assigns sentiment tags to the textual information. The input is textual information and sentiment feature data, and the output is textual information with sentiment tags. This processing is performed to add emotional context to the meeting content.

[0578] Step 6:

[0579] The server uses natural language processing technology to analyze textual information, extracting and highlighting important information. The input is sentiment-tagged textual information, and the output is highlighted important information. Furthermore, if the textual information is long, an automatic summarization function is used to concisely summarize the key points.

[0580] Step 7:

[0581] The server analyzes the emotion tags assigned to all statements and generates an emotion map that visually represents the flow of emotions. The input is text information with emotion tags, and the output is an emotion map. This allows meeting participants to intuitively grasp the overall emotional changes.

[0582] Step 8:

[0583] Users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. Input is highlighted information and emotional maps sent from the server, while output is a visual display on the terminal. This allows users to track and understand the meeting content in detail.

[0584] (Application Example 2)

[0585] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0586] Traditional conferencing systems could acquire audio information and convert it to text, but they had the problem of not being able to grasp, analyze, and share the speaker's emotions in real time. As a result, meeting participants could not accurately understand the emotional changes of other participants, making it difficult to improve the quality of communication. In addition, the extraction of important information and automatic summarization were insufficient, which could lead to information being overlooked or misunderstood in long meetings. There is a need to solve these problems.

[0587] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0588] In this invention, the server includes means for transmitting audio information to a processing device, means for converting the audio information into text information using speech recognition technology and identifying the speaker, means for extracting and highlighting important information using information processing technology, and means for analyzing emotions and assigning emotion tags. This makes it possible to analyze and visualize the emotions of speakers during a meeting in real time, thereby improving the quality of communication among participants.

[0589] "Audio information" refers to audio data as an acoustic signal, which can be converted into textual information or emotional information through analysis.

[0590] A "processing device" refers to a computer system that receives, converts, and analyzes acquired audio information.

[0591] "Speech recognition technology" is a technology that analyzes speech and automatically converts it into corresponding text information.

[0592] "Textual information" refers to text data converted using speech recognition technology, which allows for the visual presentation of information.

[0593] "Speaker identification" is an analytical technique used to identify the person who made a statement within audio data.

[0594] "Information processing technology" refers to the technology of extracting important information from textual information and performing tasks such as highlighting and summarizing.

[0595] An "emotion tag" is a label that indicates the emotional state of the speaker and is added to textual information.

[0596] A "user interface" is an interface that provides processed information to the user visually and allows them to operate it.

[0597] The following system is a concrete example of how this invention can be implemented.

[0598] The server receives audio information in real time from multiple audio capture devices installed within the factory. This audio information is first quickly converted into text using the Google Speech-to-Text API. Then, sentiment analysis is performed using Microsoft Azure Text Analytics, and appropriate sentiment tags are assigned to each utterance. These sentiment tags are used to visualize the information as an sentiment map. Through this entire process, the audio information is stored not merely as text, but as data with emotional context.

[0599] The terminal receives text information and emotion tags from the server and displays them in real time on the user interface. Using data visualization libraries such as D3.js, the flow of emotions is visually represented, making it easy for users to understand changes in their emotions.

[0600] Based on the displayed information, users can grasp the progress of meetings and intuitively understand the emotions of the speakers. For example, if an employee says, "We've been having a lot of equipment breakdowns lately, and it's really bothering us," the audio information based on that statement will be recorded with the emotion tag "confused." This allows other participants to not only understand the content but also recognize the underlying emotional elements.

[0601] An example of a prompt might be, "Please show how to analyze and display the emotions expressed by employee A during their report." Through such prompts, the aim is to improve the accuracy of emotion analysis via the AI ​​model.

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The server receives audio information from an audio capture device installed within the factory. The audio capture device records the audio of meetings and conferences in real time and transmits the data to the server in digital format. The audio information is captured by the server as an unprocessed acoustic signal. At this stage, the input is a digital audio signal, and the output is audio data stored on the server.

[0605] Step 2:

[0606] The server converts received audio information into text using the Google Speech-to-Text API. This speech recognition process parses the audio data into corresponding text data. The input is the audio data stored on the server, and the output is the converted text data. This makes the audio information available in a format that can be visually processed.

[0607] Step 3:

[0608] The server performs sentiment analysis on the converted text data using Microsoft Azure Text Analytics. Sentiment analysis evaluates the emotional characteristics of each statement and assigns appropriate sentiment tags. The input is text data, and the output is text data with sentiment tags attached. This adds emotional context to the textual information.

[0609] Step 4:

[0610] The terminal displays text information and emotion tags received from the server in real time on the user interface. D3.js is used for this visualization, generating graphs and maps that visually represent the flow of emotions. The input is emotion-tagged text data sent from the server, and the output is visual information displayed on the terminal. This allows users to instantly understand the emotions associated with what they say.

[0611] Step 5:

[0612] Based on the information displayed on the device, the user monitors the progress of the meeting or conference and understands the emotions of the speakers. Through this monitoring process, the user effectively adjusts communication within the meeting and, if necessary, inputs prompt sentences into the AI ​​model to improve the accuracy of emotion analysis. The input is the displayed information, and the output is the user's understanding and actions.

[0613] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] As shown in Figure 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.

[0619] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0620] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0621] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0623] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0624] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0625] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0626] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0627] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0628] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0629] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] This invention is an information processing system that quickly and accurately records audio during meetings, converts it into text information in real time, and enables participants to immediately grasp important information. The system acquires audio data, performs advanced analysis processing via a server, and displays it visually on a terminal.

[0631] First, the terminal acquires the conference audio data through the microphone and converts it into digital data in real time. This data is streamed to the server at regular buffer intervals. The server converts the received audio data into text data using speech recognition technology. In this process, the server analyzes the voice characteristics of each speaker to identify who spoke.

[0632] Next, the converted text data is analyzed by the server using natural language processing technology. The server extracts important keywords and phrases from the spoken content and tags them in a highlighted format (e.g., red or bold). Furthermore, if the spoken content is long, an automatic summarization function is used to condense the content and present the main points. This allows users to quickly understand the important information of the meeting.

[0633] Users can view information sent from the server to their terminals through the user interface. The screen, which updates in real time, displays the content of each speaker's remarks, highlighting important points, thus supporting users in quickly grasping the main points of the meeting and taking appropriate action.

[0634] As a concrete example, if User A says "The deadline for the next project is very important" during a meeting, the terminal sends this audio to the server, which uses speech recognition to generate the text "User A: The deadline for the next project is very important." This text is further analyzed, and keywords such as "deadline" and "important" are highlighted in red. Finally, this information is displayed on the user's terminal, allowing the user to check it in real time.

[0635] Thus, the present invention aims to enable rapid recording of meeting contents and efficient provision of important information, thereby realizing the immediate information access that users require.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The terminal captures ambient sound during the meeting using an audio input device (microphone). The acquired audio is digitally converted and stored in a buffer as a data stream in real time.

[0639] Step 2:

[0640] The terminal sends the audio data stored in the buffer to the server at regular intervals using a streaming protocol. The connection to the server is made via a low-latency network to minimize the loss of audio data.

[0641] Step 3:

[0642] The server inputs the received audio data into the speech recognition engine and performs the process of converting the audio information of each utterance into text. The server also captures speaker-specific vocal characteristics from the audio data to identify who made the statement.

[0643] Step 4:

[0644] The server applies natural language processing techniques to the text generated by speech recognition to extract important keywords and phrases. The extracted information is then tagged for visual emphasis.

[0645] Step 5:

[0646] The server automatically summarizes slogans exceeding a certain length. Specifically, a natural language processing model extracts the main points of the text and reconstructs the original information concisely.

[0647] Step 6:

[0648] The server sends the processed text data to the terminal. The terminal displays this information on its user interface, allowing the user to see the updated content and highlights of the message in real time.

[0649] Step 7:

[0650] Based on the information displayed on their devices, users can grasp the meeting content in real time and take additional notes as needed. After the meeting, they can review the information saved on their devices and create meeting minutes or share information as necessary.

[0651] (Example 1)

[0652] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] This solution addresses the challenge of quickly and accurately recording and analyzing spoken information in large group settings such as meetings, making it difficult for participants to immediately grasp important information. Furthermore, accurately extracting key information from lengthy speeches is also challenging. This makes it difficult for participants to avoid overlooking or misunderstanding information and to conduct discussions efficiently.

[0654] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0655] In this invention, the server includes means for converting audio information into text information and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for automatically summarizing the text information if it exceeds a certain length. This makes it possible to convert audio information into text information in real time and to highlight and display important points.

[0656] A "computation device" is an information processing device that has the function of acquiring audio information and displaying the processing results via a user interface.

[0657] An "information processing device" is a server that analyzes audio information received from a computer and converts it into text information.

[0658] "Speech recognition technology" is a technology that analyzes speech information and converts it into text information, and has the function of identifying the speaker.

[0659] "Natural language processing technology" is a technique for extracting important information from textual data and visually highlighting the analyzed information.

[0660] "Automatic summarization" is a technology that shortens the content of a statement and extracts the main points when the text information exceeds a certain length.

[0661] A "graphical user interface" is a screen display method that allows users to visually access information and grasp it in real time.

[0662] This information processing system uses a computer and an information processing device to quickly and accurately convert audio information from meetings into text, enabling immediate access to important information. The computer has the function of acquiring audio information via a microphone and transmitting it as digital data to the information processing device in real time. The information processing device converts the received audio information into text using advanced speech recognition technology and identifies the speaker.

[0663] Highly accurate speech recognition software is used for speech recognition, such as the Google Speech-to-Text API. This converted text information is then analyzed using natural language processing techniques. The information processing device extracts important keywords and phrases from the text information and visually highlights them on a graphical user interface. Libraries such as NLTK and SpaCy are available for natural language processing.

[0664] Furthermore, if a meeting discussion is lengthy, automatic summarization technology can be applied to shorten and extract the main points. This technology allows users to understand the main points of the discussion in real time. The processed information is sent back to the computer and provided through the user interface.

[0665] As a concrete example, consider a scenario in a meeting where User A states, "The deadline for the next project is extremely important." The computer sends this statement to the information processing unit. The information processing unit uses the aforementioned speech recognition technology to generate the text, "User A: The deadline for the next project is extremely important." Natural language processing is then performed, emphasizing keywords such as "deadline" and "important." This processing result is returned to the computer and displayed for the user to quickly review.

[0666] Thus, the present invention enables rapid recording and analysis of spoken content, supporting users' immediate access to information. An example of a prompt sentence generated using the AI ​​model is, "Analyze the audio data from the meeting in real time, extract important keywords, and highlight them." This enables efficient processing and provision of information.

[0667] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0668] Step 1:

[0669] The terminal acquires audio information during a meeting via a microphone. The audio signal is converted into digital data. Specifically, it converts the analog signal into a digital signal using an A / D converter and generates sample data. The input is analog audio from the microphone, and the output is digital audio data.

[0670] Step 2:

[0671] The terminal streams digital audio data to the server at regular buffer time intervals. Specifically, this involves dividing the audio data into segments of a fixed duration (e.g., 5 seconds) and sending them to the server over the network. The input is digital audio data, and the output is the data stream sent to the server.

[0672] Step 3:

[0673] The server converts the received digital audio data into text information using speech recognition technology. This process utilizes the Google Speech-to-Text API to analyze the audio signal and output the audio as text data. The input is digital audio data, and the output is text information generated by speech recognition.

[0674] Step 4:

[0675] The server analyzes the text information obtained through speech recognition using natural language processing techniques. Specifically, it uses NLTK and SpaCy to extract important keywords and phrases from the text and tags them for highlighting. The input is text information, and the output is text information with highlighting tags applied.

[0676] Step 5:

[0677] The server automatically summarizes the converted and parsed text if it exceeds a certain length. In this process, an automatic summarization algorithm understands the context, extracts the main points, and shortens the text. The input is parsed long text information, and the output is summarized text information.

[0678] Step 6:

[0679] The server sends the final processed information to the terminal. During this process, the analysis results are updated in real time. Specifically, textual information, including highlighted points and summaries, is prepared for visualization through the interface. The input is the final processed textual information, and the output is the data sent to the terminal.

[0680] Step 7:

[0681] Users view information sent from the server through a user interface on the computing device. The screen is updated in real time, and important information is highlighted. Specifically, users can operate the interface on the terminal, scrolling through information and taking notes as needed. Input is the data sent to the terminal, and output is the information displayed on the user interface.

[0682] (Application Example 1)

[0683] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0684] In meetings and seminars, there is a problem in that participants often have difficulty grasping important information in real time and effectively. In particular, when the amount of information presented is vast, it is difficult to quickly determine which information is important. Furthermore, because individual participants perceive information from different perspectives, it is difficult to achieve a consistent understanding. In such environments, there is a need for technological means to facilitate the smooth acquisition and sharing of important information.

[0685] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0686] In this invention, the server includes means for converting audio data into text data, means for identifying speakers, and means for highlighting important information using natural language processing techniques. This allows participants to grasp the key points of a meeting in real time. In particular, by extracting and visually highlighting text entities, information is organized in a way that is easy for users to understand, supporting rapid decision-making.

[0687] "Audio data" refers to digital data that records audio, and is an information resource used when analyzing the content of meetings and conversations.

[0688] "Hardware" refers to physical devices used to acquire and process audio data, such as terminals and microphones.

[0689] A "processing unit" is a device or system that performs calculations to analyze transmitted audio data and convert it into text data.

[0690] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data, and includes the process of converting the content of speech into text.

[0691] "Text data" refers to text information converted by speech recognition technology, which encodes the content of a speech.

[0692] "Means for identifying speakers" refers to technologies that analyze the voice characteristics of multiple speakers and have the function of identifying each speaker.

[0693] "Natural language processing technology" is a technique for analyzing text data and extracting important information and concepts, and is particularly used to process human language on computers.

[0694] "Means of highlighting" refer to methods used to visually make important information or keywords stand out, and include techniques such as changing the color or style of the text.

[0695] "Automatic summarization" is a process that shortens long texts while retaining the main points, and is used to efficiently convey information.

[0696] "Interface" refers to the screen display and means of operation that allow users to visually view and manipulate data.

[0697] "Methods for extracting entities" refer to techniques that detect important elements such as specific words or phrases from text and classify the information.

[0698] The system implementing this invention performs the acquisition, analysis, and visualization of audio data in an integrated manner. Specifically, it uses the following hardware and software. The terminal uses a microphone to collect audio data from meetings and seminars. This enables real-time conversion of audio into digital data.

[0699] The terminal streams the collected audio data to the processing unit (server). The server uses speech recognition technology to convert the received audio data into text data. At this time, speaker identification technology can be used to accurately identify each speaker.

[0700] Next, the server analyzes the converted text data using natural language processing technology and extracts important information. This allows the server to highlight the information the user needs and present it in a visually easy-to-understand manner. In the case of long statements, an automatic summarization function is used to effectively aggregate the information.

[0701] The converted and analyzed information is then sent back to the terminal and visualized through the user interface. This allows users to recognize important information in real time and make appropriate decisions.

[0702] For example, if someone says in a meeting, "The deadline for the next project is very important," the server will convert this statement into text and highlight the key keywords, "deadline" and "important." Users can then instantly check this information on their devices and take appropriate action.

[0703] Natural language processing technology incorporating a generative AI model supports advanced analysis and enables the provision of highly accurate information. A concrete example of a prompt is, "Meeting content: Please highlight the key points about the next product." Thus, this development provides an effective information processing system that supports the rapid understanding and sharing of meeting content.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The terminal acquires meeting audio data via a microphone. The input is the meeting audio, and the output is digital audio data. This digitized audio data is captured in real time to prepare it for future processing.

[0707] Step 2:

[0708] The terminal streams the acquired digital audio data to the processing unit (server). The input is digital audio data, and the output is the audio data sent to the server. In this process, data is transmitted quickly and efficiently over the network.

[0709] Step 3:

[0710] The server uses speech recognition technology to convert transmitted audio data into text data. The input is streamed audio data, and the output is the converted text data. The server analyzes the features of the speech and maps the speech to text using an acoustic model.

[0711] Step 4:

[0712] The server identifies speakers within the text data. The input is text data obtained through speech recognition, and the output is text data with each speaker identified. Speaker identification technology is used to analyze the speech patterns of different speakers and assign speakers to them.

[0713] Step 5:

[0714] The server uses natural language processing techniques to extract important information from text data and add markup for highlighting. The input is identified text data, and the output is text data with the important information highlighted. A generative AI model is used to detect entities and identify important words and phrases.

[0715] Step 6:

[0716] The server automatically summarizes text data using a natural language processing model when the data exceeds a certain length. The input is long text data, and the output is text data containing only the summarized main points. Statistical analysis of the text selects the most important information and summarizes it concisely.

[0717] Step 7:

[0718] The server sends processed text data to the terminal and displays it visually through the user interface. The input is highlighted and summarized text data, and the output is a visual information display viewable by the user. Based on the displayed information, the user can instantly grasp the meeting content.

[0719] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0720] This invention is an information processing system that records audio in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. This system promotes a deeper understanding of information by combining an emotion engine at each stage of audio data acquisition, processing, and display.

[0721] First, the terminal acquires audio during the meeting and transmits the data to the server in real time. The audio data is transferred in digital format, and the server converts it into text data using speech recognition technology. Speaker identification is performed by analyzing the characteristics of the voice.

[0722] Simultaneously, the device captures emotional characteristics such as the user's facial expressions and tone of voice. This data is analyzed by an emotion engine to determine the user's emotional state. The server records this emotional information in combination with text data and assigns it to the text as an emotion tag.

[0723] Next, the server analyzes the text data and uses natural language processing techniques to extract and highlight important information. An automatic summarization function shortens long statements to display the main points. Furthermore, an emotion map generated by the emotion engine visualizes the flow of emotions within the meeting, allowing users to intuitively understand the atmosphere.

[0724] Users can view the processed audio content, highlighted information, and emotional flow through the device's user interface. For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this to text, and the emotion engine assigns an emotion tag such as "anxiety." The device then displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0725] Thus, the present invention aims to improve the quality of meetings by enabling participants to gain deeper insights through multi-layered data processing.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The device captures audio data using its microphone at the start of the meeting. The audio data is converted into a digital signal on the spot. In parallel, the device records the user's facial expressions and tone of voice through its camera and microphone, collecting emotion-related data.

[0729] Step 2:

[0730] The device transmits recorded audio and emotion data to the server at regular intervals. Audio data is transferred to the server in real time using a streaming protocol. Similarly, emotion data is also transmitted and processed in parallel.

[0731] Step 3:

[0732] The server converts the received audio data into text data using speech recognition technology. During this process, a speaker identification algorithm is applied to identify the speaker based on the characteristics of the voice. As a result, each statement is clearly recorded, indicating who spoke it and when.

[0733] Step 4:

[0734] The server uses an emotion analysis engine in parallel with processing the audio data to analyze the received emotion data. Based on the user's voice tone and facial expressions, it identifies their emotional state (e.g., joy, anger, anxiety, etc.). This emotion information is tagged in a way that corresponds to their speech.

[0735] Step 5:

[0736] The server further analyzes the converted text data and uses natural language processing techniques to extract important keywords and phrases. The extracted information is then enhanced with highlighting data and presented in a visually recognizable format.

[0737] Step 6:

[0738] The server applies an automated summarization algorithm to summarize statements exceeding a certain length. This summary is designed to retain the main points in a shortened form.

[0739] Step 7:

[0740] The server sends processed text data, sentiment tags, and summary information to the terminal. The terminal displays this information on the user interface and updates it in real time. This system allows users to instantly see the content of speech and the flow of sentiment, and quickly grasp the progress of the meeting.

[0741] Step 8:

[0742] Users can refer to the information provided on their devices, take notes during meetings, and record their reactions to agenda items. Since the information can also be viewed as a log after the meeting, meeting minutes can be reviewed and shared efficiently.

[0743] (Example 2)

[0744] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] In today's world, while many meetings and discussions take place, it's difficult for participants to accurately grasp all the content and understand the emotional nuances. In particular, simply receiving information without considering the emotional context or the speaker's emotional state can lead to misunderstandings and communication problems among participants. To solve these problems, it's necessary not only to convert audio into text data, but also to add emotional information so that the atmosphere of the meeting can be intuitively understood.

[0746] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0747] In this invention, the server includes means for converting acoustic information into text information using acoustic recognition technology and identifying the speaker, means for extracting and highlighting important information from the text information using natural language processing technology, and means for analyzing emotional characteristics, combining emotional information with text information, and generating an emotional map that shows the emotional state of the speaker. This makes it possible to improve the quality of meetings by deepening the understanding of the meeting content and providing emotional context to participants.

[0748] "Acoustic information" refers to data that represents audio signals acquired in settings such as meetings and discussions in a digital format.

[0749] "Equipment" refers to hardware and software used to acquire acoustic information during a meeting, including terminals and microphones.

[0750] A "computer" is a digital device used to receive and process acoustic information, and typically functions as a server.

[0751] "Acoustic recognition technology" is a technology for converting acoustic information into textual information, and it is a technology that uses a speech recognition engine to convert speech into text.

[0752] "Textual information" refers to text data converted from acoustic information using acoustic recognition technology.

[0753] "Means for identifying speakers" refers to analytical techniques for identifying a specific speaker from converted text information.

[0754] "Natural language processing technology" is a technology for analyzing and processing the meaning and importance of textual information.

[0755] "Important information" refers to the key points or conclusions that deserve particular attention within the meeting's content.

[0756] "Emotional characteristics" refer to elements that indicate a speaker's emotional state, such as facial expressions and tone of voice.

[0757] An "emotion map" is a diagram that visually represents the flow of a speaker's emotions during a meeting, and it shows the emotional state of the participants.

[0758] This invention is an information processing system that records acoustic information in a meeting in real time, identifies each speaker, analyzes their emotions, and adds emotional context to the meeting content. The entire system mainly consists of a server and terminals.

[0759] The terminal acquires audio information during the meeting via a microphone. This terminal is equipped with high-performance audio filtering software, such as Audacity, to remove background noise and obtain clear audio. This audio information is transmitted to the server in real time via the network.

[0760] The server receives acoustic information and uses a speech recognition engine (e.g., Google Speech-to-Text API) based on acoustic recognition technology to convert the acoustic information into text. The server then identifies the speaker based on the characteristics of the voice. This ensures that who said what is recorded accurately.

[0761] Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and tone of voice, and performs emotion analysis based on this data. An emotion analysis engine (e.g., Microsoft Azure Emotion API) is used to determine the user's emotional state. The server combines this emotion information with textual data and assigns it as an emotion tag. This ensures that not only the content of what is said, but also the underlying emotional nuances are recorded.

[0762] The server analyzes textual information using natural language processing technologies (e.g., NLTK and spaCy), extracting and highlighting important information. An automatic summarization function can shorten long statements to only the essential points. Furthermore, based on the sentiment-tagged results, it generates an sentiment map showing the overall emotional flow of the meeting.

[0763] In this system embodiment, users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. The visually clear layout allows for an intuitive understanding of the meeting's atmosphere and the emotional state of the participants.

[0764] For example, if User B says, "I'm worried about the project's progress," the speech recognition engine converts this into text, and sentiment analysis assigns the emotion tag "anxiety." As a result, the terminal displays "User B: I'm worried about the project's progress," and the emotion map indicating anxiety is updated.

[0765] An example of a prompt for a generative AI model would be: "Please tell me how to add sentiment analysis to a real-time meeting recording system. Also, please suggest ways to make the sentiment map intuitively understandable to the user."

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] The device acquires acoustic information in real time during meetings using a microphone. The input is ambient sound, and the output is digital acoustic information. During audio acquisition, audio filtering software is used to remove background noise. This process ensures that the meeting audio is captured clearly.

[0769] Step 2:

[0770] The terminal transmits the acquired acoustic information to the server via the network. The input is digital acoustic information, and the output is the acoustic information transferred to the server. During transmission, network bandwidth is managed to prevent data interruptions.

[0771] Step 3:

[0772] The server uses a speech recognition engine based on acoustic recognition technology to convert received acoustic information into text information. The input is acoustic information, and the output is text-based character information. During this process, the audio data is analyzed, and profiling is performed to identify a specific speaker.

[0773] Step 4:

[0774] The device uses a camera and microphone to capture the facial expressions and tone of voice of meeting participants. The input is the participants' video and audio, and the output is digital data based on emotional characteristics. This data is then analyzed by an emotion analysis engine, which performs data processing to identify the user's emotional state.

[0775] Step 5:

[0776] The server integrates textual information with sentiment information obtained through sentiment analysis and assigns sentiment tags to the textual information. The input is textual information and sentiment feature data, and the output is textual information with sentiment tags. This processing is performed to add emotional context to the meeting content.

[0777] Step 6:

[0778] The server uses natural language processing technology to analyze textual information, extracting and highlighting important information. The input is sentiment-tagged textual information, and the output is highlighted important information. Furthermore, if the textual information is long, an automatic summarization function is used to concisely summarize the key points.

[0779] Step 7:

[0780] The server analyzes the emotion tags assigned to all statements and generates an emotion map that visually represents the flow of emotions. The input is text information with emotion tags, and the output is an emotion map. This allows meeting participants to intuitively grasp the overall emotional changes.

[0781] Step 8:

[0782] Users can view processed audio content, highlighted information, and emotional flow through the terminal's user interface. Input is highlighted information and emotional maps sent from the server, while output is a visual display on the terminal. This allows users to track and understand the meeting content in detail.

[0783] (Application Example 2)

[0784] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0785] Traditional conferencing systems could acquire audio information and convert it to text, but they had the problem of not being able to grasp, analyze, and share the speaker's emotions in real time. As a result, meeting participants could not accurately understand the emotional changes of other participants, making it difficult to improve the quality of communication. In addition, the extraction of important information and automatic summarization were insufficient, which could lead to information being overlooked or misunderstood in long meetings. There is a need to solve these problems.

[0786] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0787] In this invention, the server includes means for transmitting audio information to a processing device, means for converting the audio information into text information using speech recognition technology and identifying the speaker, means for extracting and highlighting important information using information processing technology, and means for analyzing emotions and assigning emotion tags. This makes it possible to analyze and visualize the emotions of speakers during a meeting in real time, thereby improving the quality of communication among participants.

[0788] "Audio information" refers to audio data as an acoustic signal, which can be converted into textual information or emotional information through analysis.

[0789] A "processing device" refers to a computer system that receives, converts, and analyzes acquired audio information.

[0790] "Speech recognition technology" is a technology that analyzes speech and automatically converts it into corresponding text information.

[0791] "Textual information" refers to text data converted using speech recognition technology, which allows for the visual presentation of information.

[0792] "Speaker identification" is an analytical technique used to identify the person who made a statement within audio data.

[0793] "Information processing technology" refers to the technology of extracting important information from textual information and performing tasks such as highlighting and summarizing.

[0794] An "emotion tag" is a label that indicates the emotional state of the speaker and is added to textual information.

[0795] A "user interface" is an interface that provides processed information to the user visually and allows them to operate it.

[0796] The following system is a concrete example of how this invention can be implemented.

[0797] The server receives audio information in real time from multiple audio capture devices installed within the factory. This audio information is first quickly converted into text using the Google Speech-to-Text API. Then, sentiment analysis is performed using Microsoft Azure Text Analytics, and appropriate sentiment tags are assigned to each utterance. These sentiment tags are used to visualize the information as an sentiment map. Through this entire process, the audio information is stored not merely as text, but as data with emotional context.

[0798] The terminal receives text information and emotion tags from the server and displays them in real time on the user interface. Using data visualization libraries such as D3.js, the flow of emotions is visually represented, making it easy for users to understand changes in their emotions.

[0799] Based on the displayed information, users can grasp the progress of meetings and intuitively understand the emotions of the speakers. For example, if an employee says, "We've been having a lot of equipment breakdowns lately, and it's really bothering us," the audio information based on that statement will be recorded with the emotion tag "confused." This allows other participants to not only understand the content but also recognize the underlying emotional elements.

[0800] An example of a prompt might be, "Please show how to analyze and display the emotions expressed by employee A during their report." Through such prompts, the aim is to improve the accuracy of emotion analysis via the AI ​​model.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The server receives audio information from an audio capture device installed within the factory. The audio capture device records the audio of meetings and conferences in real time and transmits the data to the server in digital format. The audio information is captured by the server as an unprocessed acoustic signal. At this stage, the input is a digital audio signal, and the output is audio data stored on the server.

[0804] Step 2:

[0805] The server converts received audio information into text using the Google Speech-to-Text API. This speech recognition process parses the audio data into corresponding text data. The input is the audio data stored on the server, and the output is the converted text data. This makes the audio information available in a format that can be visually processed.

[0806] Step 3:

[0807] The server performs sentiment analysis on the converted text data using Microsoft Azure Text Analytics. Sentiment analysis evaluates the emotional characteristics of each statement and assigns appropriate sentiment tags. The input is text data, and the output is text data with sentiment tags attached. This adds emotional context to the textual information.

[0808] Step 4:

[0809] The terminal displays text information and emotion tags received from the server in real time on the user interface. D3.js is used for this visualization, generating graphs and maps that visually represent the flow of emotions. The input is emotion-tagged text data sent from the server, and the output is visual information displayed on the terminal. This allows users to instantly understand the emotions associated with what they say.

[0810] Step 5:

[0811] Based on the information displayed on the device, the user monitors the progress of the meeting or conference and understands the emotions of the speakers. Through this monitoring process, the user effectively adjusts communication within the meeting and, if necessary, inputs prompt sentences into the AI ​​model to improve the accuracy of emotion analysis. The input is the displayed information, and the output is the user's understanding and actions.

[0812] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0813] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0814] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0815] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0816] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0817] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0818] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0819] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0820] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0821] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0822] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0823] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0824] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0825] 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.

[0826] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0827] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0828] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0829] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0830] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0831] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0832] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A method using a terminal to acquire audio data,

[0836] Means for transmitting the aforementioned audio data to a server,

[0837] The server includes means for converting the voice data into text data using speech recognition technology and for identifying the speaker,

[0838] A means for extracting and highlighting important information from the aforementioned text data using natural language processing technology,

[0839] A means for automatically summarizing the aforementioned character data when it exceeds a certain length,

[0840] means for transmitting the processed character data to the terminal and displaying it,

[0841] An information processing system that includes this.

[0842] (Claim 2)

[0843] The information processing system according to claim 1, further comprising means for displaying the processed character data in real time via a user interface in the terminal, and for making important information visually recognizable.

[0844] (Claim 3)

[0845] The information processing system according to claim 1, wherein the means for performing the automatic summarization includes means for using a natural language processing model to analyze the context of a statement and extract and shorten key points.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A means for acquiring audio information in a computing device,

[0849] means for transmitting the aforementioned audio information to an information processing device,

[0850] The information processing device includes means for converting the speech information into text information using speech recognition technology and for identifying the speaker,

[0851] A means for extracting and highlighting important information from the aforementioned textual information using natural language processing technology,

[0852] A means for automatically summarizing the text information when it exceeds a certain length,

[0853] means for transmitting the processed character information to the computing device and displaying it,

[0854] A screen display method that updates in real time,

[0855] Means of visually highlighting important parts,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for presenting the processed character information in real time via a graphical user interface and making important information visually recognizable in the computing device.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein the means for performing the automatic summarization includes means for using a natural language processing model to analyze the context of a statement and extract and shorten key points.

[0861] "Application Example 1"

[0862] (Claim 1)

[0863] A means of using hardware to acquire audio data,

[0864] Means for transmitting the aforementioned audio data to a computing device,

[0865] The aforementioned computing device includes means for converting the speech data into text data using speech recognition technology and for identifying the speaker,

[0866] A means for extracting and highlighting important information from the aforementioned text data using natural language processing technology,

[0867] A means for automatically summarizing the aforementioned character data when it exceeds a certain length,

[0868] means for transmitting the processed character data to the hardware and displaying it,

[0869] A means of extracting text entities using a natural language processing model for display on a terminal, and visually highlighting important entities,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for displaying the processed character data in real time via an interface in the terminal, and for making important information visually recognizable.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein the means for performing the automatic summarization includes means for using a natural language processing model to analyze the context of a statement and extract and shorten key points.

[0875] "Example 2 of combining an emotion engine"

[0876] (Claim 1)

[0877] A means of using a device for acquiring acoustic information,

[0878] Means for transmitting the aforementioned acoustic information to a computer,

[0879] The computer includes means for converting the acoustic information into textual information using acoustic recognition technology and for identifying the speaker,

[0880] A means for extracting and highlighting important information from the aforementioned textual information using natural language processing technology,

[0881] A means for automatically summarizing the text information when it exceeds a certain length,

[0882] means for transmitting the processed character information to the device and displaying it,

[0883] Furthermore, methods for analyzing emotional characteristics such as facial expressions and tone of voice, and combining emotional information with textual information,

[0884] A means for generating an emotional map that shows the emotional state of the speaker,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising means for displaying the processed text information and emotional information in real time via a user interface, thereby enabling visual recognition of the flow of important information and emotions.

[0888] (Claim 3)

[0889] The system according to claim 1, wherein the means for performing the automatic summarization includes means for using a natural language processing model to analyze the context and emotional context of a statement and extract and shorten key points.

[0890] "Application example 2 when combining with an emotional engine"

[0891] (Claim 1)

[0892] A means of using a device for acquiring audio information,

[0893] means for transmitting the aforementioned audio information to a processing device,

[0894] The processing apparatus includes means for converting the speech information into text information using speech recognition technology and for identifying the speaker,

[0895] A means for extracting and highlighting important information from the aforementioned textual information using information processing technology,

[0896] A means for automatically summarizing the text information when it exceeds a limited length,

[0897] A means for analyzing emotions from the aforementioned information and assigning emotion tags,

[0898] Means for transmitting and displaying the processed character information and emotion tags to the device,

[0899] An information processing system that includes this.

[0900] (Claim 2)

[0901] The information processing system according to claim 1, further comprising means for displaying the processed text information and emotional flow in real time via a user interface and for making important information visually recognizable.

[0902] (Claim 3)

[0903] The information processing system according to claim 1, wherein the means for performing the automatic summarization includes means for analyzing the context of a statement and using an information processing model for extracting and shortening key points. [Explanation of Symbols]

[0904] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method using a terminal to acquire audio data, Means for transmitting the aforementioned audio data to a server, The server includes means for converting the voice data into text data using speech recognition technology and for identifying the speaker, A means for extracting and highlighting important information from the aforementioned text data using natural language processing technology, A means for automatically summarizing the aforementioned character data when it exceeds a certain length, means for transmitting the processed character data to the terminal and displaying it, An information processing system that includes this.

2. The information processing system according to claim 1, further comprising means for displaying the processed character data in real time via a user interface in the terminal, and for making important information visually recognizable.

3. The information processing system according to claim 1, wherein the means for performing the automatic summarization includes means for using a natural language processing model to analyze the context of a statement and extract and shorten the main points.

Images