system

Abstract

We provide the system. [Solution] Means for receiving data from user equipment, A means of converting audio to text from received data, An artificial intelligence method for extracting important information from text, A means of storing the extracted information in a structured data format, A means of sending notifications to users, A means of collecting feedback and updating artificial intelligence tools, A 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 method for controlling a persona chatbot, which is 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a current situation where various forms of data, such as meeting minutes accumulated within a company and data of online meetings, are not fully utilized. As a result, there are problems such as an increase in communication load related to information sharing and decision-making, and difficulty in quickly obtaining necessary information. This has led to a particular problem of stagnant information sharing between different departments and a decline in work efficiency.

Means for Solving the Problems

[0005] This invention provides a means for receiving data from a user device and converting speech to text from the received data. Furthermore, it includes an artificial intelligence means for extracting important information from the text and constructs a system that allows for easy searching and use of the extracted information later by storing it in a structured data format. It also has a means for sending notifications to the user and is characterized by updating the artificial intelligence means by collecting feedback to improve the accuracy and effectiveness of information extraction. This makes it possible to efficiently utilize data within a company and optimize the information sharing process.

[0006] A "user device" refers to a computer or mobile device used by a user to upload data to the system.

[0007] "Data" refers to files or stream information that include information such as meeting minutes or online meeting videos.

[0008] "Means of receiving" refers to a process or mechanism that captures data transmitted from a user device and puts it into a processing-ready state.

[0009] "Means of converting speech to text" refers to the process or algorithm of converting speech data into text information using speech recognition technology.

[0010] "Artificial intelligence methods for extracting important information from text" refers to AI technology that uses natural language processing to identify specific information from text data.

[0011] A "structured data format" is a method of representing data in which information is organized and arranged in a regular format so that computers can easily search and analyze it.

[0012] "Means of sending notifications" refers to methods of sending messages to users via email or notification applications to inform them of the completion of processing results.

[0013] "Means of collecting feedback and updating artificial intelligence tools" refers to the process of receiving evaluations and opinions from users and using them to improve and adjust AI models. [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), and the like.

[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] The present invention provides a platform for users to efficiently utilize meeting records and online meeting videos within a company. Specifically, users upload meeting data to the system from their own devices. In this process, the data entered by the user is transmitted to the server via the device. The server analyzes the received data and converts the audio data into text using a speech recognition module.

[0036] Next, the server uses artificial intelligence to analyze the transcribed data and extract key information. This AI model is pre-trained and capable of identifying specified information such as customer names, proposals, and action items. The extracted information is stored in a database as structured data, allowing users to easily search for the information they need later.

[0037] Furthermore, the server sends a notification to the user upon completion of processing. The notification is delivered to the user via email or a dedicated app and includes a link to view the processing results. This notification feature allows users to quickly obtain necessary information and incorporate it into their work.

[0038] Furthermore, users can submit feedback on the information provided by the server. The server collects this feedback and continuously updates its AI model to improve the accuracy of information extraction. This process allows the system to provide information that is better suited to the user's needs.

[0039] For example, consider a case where a sales team leader uploads a video of their weekly meeting to the system. In this process, the server transcribes the meeting content into text, and artificial intelligence extracts information such as the next proposal date and customer requests. The entire sales team is notified of the processing results, and each member can easily check the information necessary for their work. In this way, information sharing between different departments is facilitated, and work efficiency is improved. Thus, the present invention provides a practical system form for realizing the effective use of data in corporate activities.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user selects meeting minutes or online meeting video files from their device and initiates the upload to the system. The device then prepares to send the selected data to the server in the specified format.

[0043] Step 2:

[0044] The server receives the uploaded data. The received data is temporarily stored in storage and kept there until it is ready for processing.

[0045] Step 3:

[0046] The server invokes a speech recognition engine to convert the audio contained in the video data into text data. This engine analyzes the audio data and converts it into text information while taking the context into consideration.

[0047] Step 4:

[0048] The server passes the obtained text data to an AI model, which uses natural language processing to extract important information. The AI ​​model is pre-trained and identifies customer names, proposals, and action items.

[0049] Step 5:

[0050] The server converts the extracted information into a structured data format and stores it in a database for efficient storage. This makes it easy to search and access later.

[0051] Step 6:

[0052] The server notifies the user that all data processing is complete. This notification is sent via email or application notification and includes a link to access the information.

[0053] Step 7:

[0054] Users verify the information provided by the server and provide feedback to improve the system's performance. This feedback is used to improve the AI ​​model.

[0055] Step 8:

[0056] The server uses accumulated feedback to retrain the AI ​​model, aiming to improve the accuracy of subsequent processes. This continuous improvement enhances the system's information extraction capabilities.

[0057] (Example 1)

[0058] 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."

[0059] In today's information-management-driven business environment, efficiently utilizing meeting records and online meeting data to quickly extract crucial information is a challenge across many industries. Conventional methods struggle to effectively organize and analyze vast amounts of meeting data, requiring significant time and effort, thus failing to leverage the data's true value. This invention aims to solve these problems.

[0060] 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.

[0061] In this invention, the server includes means for receiving information from an information processing device, means for converting speech to text from the received information, and intelligent processing means for extracting important content from the text. This makes it possible to quickly and accurately extract important information from meeting and online meeting data, and to store and use it in a structured format.

[0062] An "information processing device" is a device, such as a user's terminal or computer, that transmits data.

[0063] "Methods for converting speech to text" refer to technologies and algorithms that analyze speech data and convert it into meaningful text data.

[0064] "Intelligent processing means" refers to technologies that use machine learning or artificial intelligence to identify and extract important information from text data.

[0065] A "structured information format" is a data format in which extracted information is organized and stored using methods such as categorization and tagging, allowing for efficient reference later on.

[0066] "Means of collecting opinions and updating intelligent processing systems" refers to the process of improving accuracy and performance by receiving feedback from users and retraining intelligent processing systems.

[0067] A "speaker tag" is identification information assigned to audio data to distinguish between different speakers.

[0068] This invention provides an information processing system for efficiently utilizing meeting records and online meeting information within a company. The system mainly consists of a server and user terminals, and extracts information through data transmission, analysis, storage, and notification.

[0069] Users upload meeting audio or video data to the server using their devices. The devices utilize a dedicated application or web interface to verify the data format before sending it to the server. The server then converts the received data into text using speech recognition software, such as Google® Cloud Speech-to-Text API.

[0070] The transcribed data is analyzed by a server using a generative AI model. The generative AI model operates according to pre-configured prompts, such as "Extract customer names, proposals, and action items from the meeting content." The AI ​​model is trained using machine learning algorithms and improves its accuracy by incorporating user feedback.

[0071] The extracted key information is stored in a database in a structured format to facilitate later searching and referencing. The server sends a notification to the user when processing is complete, allowing them to review the meeting record. This notification is sent via email or business communication applications, such as Slack or Microsoft Teams®.

[0072] For example, if a sales team leader uploads a video of their weekly meeting to the system, the server interprets the video data and compiles important information such as the next proposal schedule and customer feedback. The results are shared throughout the department, allowing each member to quickly access the necessary information and incorporate it into their work. This facilitates smooth information sharing between departments and improves overall company efficiency.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user selects the audio or video file of the meeting using their own device and uploads it to the system. The input is the audio or video data of the meeting. The device checks the file format and data size and sends the data to the server over the network. This is how the data is delivered to the server.

[0076] Step 2:

[0077] The server receives data sent from the terminal. The input is audio or video data sent from the terminal. The server checks the format of this data, extracts the audio portion as needed, and converts it to a parseable format (e.g., MP3). The output here is audio data in a speech recognition-readable format.

[0078] Step 3:

[0079] The server converts audio data into text using speech recognition technology. The input is audio data in a speech-recognizable format. A speech recognition module (e.g., Google Cloud Speech-to-Text API) analyzes the audio data and generates text data with speaker tags for each speaker. This process aims to accurately record speech, and the output is text data with speaker tags.

[0080] Step 4:

[0081] The server uses a generative AI model to extract important information from text data. The input is text data tagged with speaker tags. In this process, the AI ​​model performs analysis according to pre-configured prompts (e.g., "Extract customer name, proposal content, and action items"). The output is important information such as the extracted customer name and proposal content.

[0082] Step 5:

[0083] The server stores the extracted critical information in a structured data format in the database. The input is the critical information. The server organizes the information by category and tags it to allow for easy future searching. The output of this step is the critical information properly stored in the database.

[0084] Step 6:

[0085] The server sends a completion notification to the user based on the processing results. The input is the information indicating processing completion and saved data. The notification includes a link to view the processing results and is delivered to the user via email or a dedicated app. The output is the notification to the user.

[0086] Step 7:

[0087] The user submits feedback about the information provided. The input is the user's feedback. The server receives this feedback and retrains the generating AI model to improve the accuracy of information extraction. The output is the AI ​​model with updated accuracy.

[0088] (Application Example 1)

[0089] 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."

[0090] In factory operations, the inability to quickly and accurately utilize meeting minutes and online meeting content leads to delays in implementing decided action items and improvements. This can result in decreased productivity and delayed maintenance. To address this, there is a need for a system that effectively utilizes meeting data and allows work equipment to autonomously optimize operations.

[0091] 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.

[0092] In this invention, the server includes means for receiving data from user devices, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This allows the machine to autonomously adjust its work procedures by utilizing the received conference and meeting data, thereby improving productivity and streamlining maintenance work.

[0093] "User device" refers to a terminal device used by a user to input or transmit information, and includes computers, smartphones, and other similar devices.

[0094] "Means of receiving data" refers to the functions and protocols used to retrieve information transmitted from user devices.

[0095] "Methods for converting speech to text" refers to technologies that analyze speech information and convert it into corresponding textual information.

[0096] "Artificial intelligence tools" are systems that use machine learning and natural language processing to analyze information and extract important data based on specific rules.

[0097] "Means of storing data in a structured data format" refers to databases and storage systems that record extracted information in a format that allows for efficient management and retrieval.

[0098] "Means of sending notifications" refers to communication functions used to inform users of processing results or important information, and includes email and app notifications.

[0099] "Means of collecting feedback and updating artificial intelligence means" refers to the process of gathering opinions and improvement requests from users and using them to improve the performance of artificial intelligence.

[0100] "Means by which a machine autonomously adjusts its work procedures based on information it receives" refers to a mechanism that allows a machine to improve and optimize its own operation and processes based on analyzed information.

[0101] The server uses artificial intelligence technology to receive data from user devices, convert speech data into text data using speech recognition, and extract important information from the converted text data. This involves using the speech recognition library speech_recognition and transformers for natural language processing. When users upload meeting or conference data, the server analyzes it and stores important information such as identification information, suggested items, and work instructions as structured data in the database.

[0102] Furthermore, the server sends emails to users to notify them of the processing results, using smtplib. This feature allows users to quickly obtain important information that they should apply to their work. In addition, a system is in place to continuously improve the accuracy of information extraction by collecting user feedback and using it to update the AI ​​model.

[0103] As a concrete example, records of factory production meetings can be uploaded to the system, and the server can use AI analysis to extract improvement items and necessary maintenance instructions for the next meeting, and specific tasks can be set to be executed autonomously. This results in improved operational efficiency and reduced errors.

[0104] By utilizing a generative AI model, sharp analytical capabilities can be demonstrated, and instructions can be given in the form of, for example, "Extract the key points from the meeting and create an action list until the next production meeting."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The user uploads audio data of a meeting or conference from their device to the server. The input is audio data, which is sent to the server. The server receives this audio data and temporarily stores it for the next step.

[0108] Step 2:

[0109] The server converts received audio data into text data using the speech_recognition library. The input is audio data, and the output is text data that transcribes the content of the audio. In this process, the audio waveform is analyzed, and the corresponding language structure is generated as text.

[0110] Step 3:

[0111] The server analyzes the generated text data using a generative AI model from the transformers library. The input is text data, and the output is a list of important information such as identification information, suggested items, and work instructions. The AI ​​model extracts important phrases and syntax from the text through natural language processing.

[0112] Step 4:

[0113] The server stores the extracted key information as structured data in a database. The input is a list of key information, and the output is the structured data stored in the database. This makes subsequent searching and use easier.

[0114] Step 5:

[0115] The server sends the extracted results via email to the user to notify them of the processing results. The input is structured data, and the output is a notification email to the user. The email includes a summary of the processed information and a link.

[0116] Step 6:

[0117] The user sends feedback to the server based on the notification they receive. The input is the user's feedback data, and the output is the server that receives it. The server updates its AI model through this feedback, improving the accuracy of subsequent processing.

[0118] 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.

[0119] The present invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further leverage emotional information. This system allows users to upload various data generated within their company via a terminal. The data provided from the terminal is reliably received and stored by the server, after which processing begins.

[0120] The server converts audio data from the video into text using speech recognition technology. This process transforms the audio content into a format usable as text information, enabling analysis by AI models. Next, the server applies natural language processing technology to automatically extract important information from the text, such as customer names, proposal details, and action items. The extracted information is stored in a database in a structured data format, ready for subsequent searches and analyses.

[0121] Furthermore, by integrating an emotion engine, this system recognizes potential emotions from user voice and text data. For example, it can estimate what emotions a speaker is feeling based on the tone of their voice and the content of their speech during a meeting. The server extracts this emotion information and stores it in a structured data format along with other important information. This emotion information provides valuable insights in specific communication situations, enabling strategic support for decision-making and customer service activities.

[0122] Based on the processing results, the server sends a notification to the user. This notification is sent via email or application notification and includes a link that allows the user to quickly view the necessary information and sentiment analysis results. The user can also evaluate the provided information and sentiment results and provide feedback to the system. This feedback is used as training data for the AI ​​model, improving the system's accuracy and performance.

[0123] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts the proposal content and next sales actions from this video, but also recognizes the emotional state of the marketing personnel and customers who spoke. The emotion engine analyzes whether the customer's reaction to the discussed proposal was positive or negative, and notifies the person in charge of the results. In this way, the user can use this as concrete information to determine how well the proposal was received and what approach should be taken next.

[0124] This embodiment of the invention allows users to effectively utilize data and conduct business and customer interactions more strategically.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] Users select and upload meeting recordings and online meeting videos on a dedicated platform. The device converts the files to the required format and prepares to send the user's data to the server.

[0128] Step 2:

[0129] The server receives data sent by the user. This data is stored in temporary storage and organized to ensure security and efficiency.

[0130] Step 3:

[0131] The server starts the speech recognition engine to convert the audio data into text. The speech recognition engine extracts audio from the video and converts it into contextually appropriate text data.

[0132] Step 4:

[0133] The server passes the converted text data to an AI model, which extracts important information using natural language processing technology. The AI ​​model identifies customer names, proposals, action items, and other relevant information according to predefined criteria.

[0134] Step 5:

[0135] Simultaneously, the server activates an emotion engine to recognize emotions from text and speech features. The emotion engine analyzes what emotions the speaker is expressing and outputs the results.

[0136] Step 6:

[0137] The server converts the extracted critical and emotional information into a structured data format and stores it in a database. This prepares the information for later reference and analysis.

[0138] Step 7:

[0139] The server notifies the user when processing is complete. This notification includes a link to access the processing results, allowing the user to view them.

[0140] Step 8:

[0141] Users can review the provided information and sentiment analysis results and provide feedback to the system. The server collects this feedback and uses it as data to improve the AI ​​model and sentiment engine.

[0142] (Example 2)

[0143] 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".

[0144] While a large amount of information is shared in in-house and online meetings, there is a lack of effective means to organize and utilize this information for decision-making. Furthermore, it is difficult to identify the emotions of speakers and participants during meetings and use this information to make strategic decisions.

[0145] 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.

[0146] In this invention, the server includes means for receiving information from a user device, means for converting audio from the received information into documents, and processing means for extracting important information from the documents. This enables efficient organization of information generated within a company and facilitates strategic decision-making by performing sentiment analysis of speakers and participants.

[0147] A "user device" is a terminal device used by a user to transmit data.

[0148] "Means for receiving information" refers to the function that allows a server to receive data transmitted from a user device.

[0149] "Means of converting speech to text" refers to technologies for converting received speech data into text information.

[0150] "Processing means for extracting important information" refers to technology that automatically selects specific information from a converted document.

[0151] "Methods for saving in a structured format" refer to techniques for storing extracted information in a database in a specific format.

[0152] "Means of recognizing emotions" refers to technologies that analyze and identify emotions from user statements and text data.

[0153] "Means of sending notifications" refers to a function that sends a message to inform the user that processed information has been completed.

[0154] "Methods for collecting feedback and updating learning models" refer to technologies for collecting user evaluations and using them to improve AI models.

[0155] This invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further leverage emotional information. Users first upload meeting audio and video data generated within their company to a server using a device. The devices used in this process include personal computers and smartphones.

[0156] The server converts the received audio data into a document. For speech recognition, it uses speech recognition technologies such as the Google Cloud Speech-to-Text API. The audio data is analyzed using natural language processing libraries (e.g., SpaCy, BERT) to extract important information from the text. This process yields information such as customer names, proposals, and action items in a refined format. Furthermore, the server uses an emotion engine to identify the potential emotional states of the people who participated in the meeting from their audio and text data.

[0157] For example, emotions such as joy, surprise, and anger can be determined based on the tone and speed of a speaker's voice during a meeting, or keywords in the text. This emotional information, along with other important extracted information, is stored as structured data in a database and used for immediate or later analysis.

[0158] Based on the processing results, the server sends an email or application notification to the user. This notification includes a link to view the processed information and sentiment analysis results, making it immediately accessible to the user. The user can evaluate the provided information and sentiment results and provide feedback. This feedback is used to improve the AI ​​model and contribute to providing more accurate results.

[0159] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts information such as the proposal content and next sales actions from this video, but also recognizes the emotions of the participating marketing personnel and customers. The emotion engine analyzes the customers' reactions to the proposal and notifies the person in charge of the results. This allows the user to judge how the proposal was received and uses it as concrete information to decide on the next approach.

[0160] An example of input to the generative AI model is the prompt, "Summarize the proposals made in the meeting, analyze the speakers' emotions, and generate a report."

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] Users upload meeting audio and video data from their devices to the server. This is done by the user using a dedicated upload interface to select files from their device and send them to the server. The input is an audio or video file, and the output is saved on the server.

[0164] Step 2:

[0165] The server converts the received audio data into text using speech recognition technology. Specifically, it analyzes the audio file using the Google Cloud Speech-to-Text API and other tools, and converts the spoken content into a document format. The input to this process is audio data, and the output is the corresponding text data.

[0166] Step 3:

[0167] The server analyzes the converted text data using natural language processing techniques to extract key information such as customer names, proposal details, and action items. At this stage, the server analyzes the parts of speech in the text and identifies key information based on specific keywords. The input is text data, and the output is a set of extracted key information.

[0168] Step 4:

[0169] The server uses an emotion engine to analyze emotional information from text and audio data. Specifically, it estimates emotions based on the speaker's tone and speed of voice, as well as expressions in the text. In this step, text and audio data are taken as input, and emotional information is obtained as output.

[0170] Step 5:

[0171] The server stores the extracted important and sentiment information as structured data in a database. It then formats the data into JSON format and inserts it into the database to prepare for subsequent searches and analyses. The input is a collection of organized information, and the output is structured data in the database.

[0172] Step 6:

[0173] The server sends an email or application notification to the user based on the processing results. This notification includes a link to access the information and sentiment analysis results. The input is the processing results, and the output is the notification message.

[0174] Step 7:

[0175] Users evaluate the provided information and send feedback to the system. The server collects this feedback and improves the system's accuracy by updating the generated AI model. The input is the user's feedback, and the output is the data from the updated AI model.

[0176] (Application Example 2)

[0177] 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".

[0178] In brick-and-mortar stores, customer service requires staff to instantly grasp customer emotions and respond appropriately based on those emotions. However, conventional systems make it difficult to perform emotion analysis on the spot, potentially leading to missed opportunities to improve customer satisfaction. To solve this problem, real-time emotion analysis and feedback functions for rapid responses are essential.

[0179] 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.

[0180] In this invention, the server includes means for receiving data from a user device, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This makes it possible to analyze speech data in real time and provide feedback tailored to the customer's emotional state.

[0181] A "user device" is a device used to acquire data and send it to a server.

[0182] "Means of receiving data" refers to the function of receiving information provided by user devices within the server.

[0183] "Methods for converting speech to text" refer to the process of using speech recognition technology to convert speech data into a format that can be used as written information.

[0184] "Artificial intelligence methods" refer to technologies for extracting essential information from received data.

[0185] "Structured data format" refers to a standardized data format used to organize information and facilitate its later use and retrieval.

[0186] "Means of sending notifications" refers to a messaging function used to inform users of the analysis results.

[0187] "Methods for collecting feedback" refer to methods for compiling user opinions and reactions and using them to improve the system.

[0188] "Methods for analyzing emotions from audio data" refers to technologies for estimating a speaker's emotions based on their tone of voice and content.

[0189] "A means of providing real-time feedback tailored to the customer's emotional state" refers to a function that evaluates the customer's current emotions and immediately provides the most appropriate response.

[0190] This invention is a system for improving customer service in physical stores. First, the user interacts with the customer using a user device such as smart glasses. The audio data of this interaction is transmitted in real time from the user device to a server. The server uses a speech recognition engine to instantly convert this audio data into text. The speech recognition technology used is "Google Cloud Speech-to-Text".

[0191] The transcribed data is analyzed using natural language processing techniques to extract important information. For example, the names of products the customer is interested in and the content of the suggestions are extracted. Simultaneously, a sentiment analysis engine (e.g., "IBM Watson® Tone Analyzer") operates to analyze the customer's emotions from the tone and content of their voice.

[0192] The analysis results are notified in real time from the server to the user's smart glasses. This feedback allows the user to take immediate action based on the customer's emotional state. For example, if the customer is showing positive emotions, feedback is presented to the staff prompting them to "suggest related products."

[0193] For example, when a customer says, "This product has a great design," the server analyzes this positive response and notifies the user's device, "Next, please suggest additional products that would go well with this product."

[0194] This prompt text is generated by utilizing a generative AI model. A concrete example of a prompt text is, "Please enter the text of your conversation about the product." In this way, the present invention enhances customer interaction and improves service in physical stores.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user device captures customer interactions as audio data and sends it to the server. The input is customer voice, and the output is digital audio data. The user device transmits this audio data to the server in real time.

[0198] Step 2:

[0199] The server converts the received audio data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). The input is digital audio data, and the output is text data. When the server converts the sound waveform into text, it uses the speech recognition engine's algorithm to perform accurate transcription.

[0200] Step 3:

[0201] The server extracts important information from text data using natural language processing techniques. The input is text data, and the output is structured data containing the extracted information. The server uses algorithms to identify user-friendly information, such as product names and suggested content.

[0202] Step 4:

[0203] The server analyzes emotions from text data using an emotion analysis engine (e.g., IBM Watson Tone Analyzer). The input is text data, and the output is emotion data indicating the emotional state. The server determines the speaker's emotions by analyzing the tone of the text and keywords related to emotions.

[0204] Step 5:

[0205] The server notifies the user's smart glasses of feedback in real time. Inputs are structured data and sentiment data, and output is feedback information provided to the user. The server sends information suggesting specific actions to the user via the notification system.

[0206] Step 6:

[0207] The user continues the conversation with the customer based on feedback received from the server. The input is feedback information from the server, and the output is suggestions and responses to the customer. The user decides on the next action and responds appropriately to the customer while looking at the display on their smart glasses.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] The present invention provides a platform for users to efficiently utilize meeting records and online meeting videos within a company. Specifically, users upload meeting data to the system from their own devices. In this process, the data entered by the user is transmitted to the server via the device. The server analyzes the received data and converts the audio data into text using a speech recognition module.

[0225] Next, the server uses artificial intelligence to analyze the transcribed data and extract key information. This AI model is pre-trained and capable of identifying specified information such as customer names, proposals, and action items. The extracted information is stored in a database as structured data, allowing users to easily search for the information they need later.

[0226] Furthermore, the server sends a notification to the user upon completion of processing. The notification is delivered to the user via email or a dedicated app and includes a link to view the processing results. This notification feature allows users to quickly obtain necessary information and incorporate it into their work.

[0227] Furthermore, users can submit feedback on the information provided by the server. The server collects this feedback and continuously updates its AI model to improve the accuracy of information extraction. This process allows the system to provide information that is better suited to the user's needs.

[0228] For example, consider a case where a sales team leader uploads a video of their weekly meeting to the system. In this process, the server transcribes the meeting content into text, and artificial intelligence extracts information such as the next proposal date and customer requests. The entire sales team is notified of the processing results, and each member can easily check the information necessary for their work. In this way, information sharing between different departments is facilitated, and work efficiency is improved. Thus, the present invention provides a practical system form for realizing the effective use of data in corporate activities.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The user selects meeting minutes or online meeting video files from their device and initiates the upload to the system. The device then prepares to send the selected data to the server in the specified format.

[0232] Step 2:

[0233] The server receives the uploaded data. The received data is temporarily stored in storage and kept there until it is ready for processing.

[0234] Step 3:

[0235] The server invokes a speech recognition engine to convert the audio contained in the video data into text data. This engine analyzes the audio data and converts it into text information while taking the context into consideration.

[0236] Step 4:

[0237] The server passes the obtained text data to an AI model, which uses natural language processing to extract important information. The AI ​​model is pre-trained and identifies customer names, proposals, and action items.

[0238] Step 5:

[0239] The server converts the extracted information into a structured data format and stores it in a database for efficient storage. This makes it easy to search and access later.

[0240] Step 6:

[0241] The server notifies the user that all data processing is complete. This notification is sent via email or application notification and includes a link to access the information.

[0242] Step 7:

[0243] Users verify the information provided by the server and provide feedback to improve the system's performance. This feedback is used to improve the AI ​​model.

[0244] Step 8:

[0245] The server uses accumulated feedback to retrain the AI ​​model, aiming to improve the accuracy of subsequent processes. This continuous improvement enhances the system's information extraction capabilities.

[0246] (Example 1)

[0247] 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."

[0248] In today's information-management-driven business environment, efficiently utilizing meeting records and online meeting data to quickly extract crucial information is a challenge across many industries. Conventional methods struggle to effectively organize and analyze vast amounts of meeting data, requiring significant time and effort, thus failing to leverage the data's true value. This invention aims to solve these problems.

[0249] 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.

[0250] In this invention, the server includes means for receiving information from an information processing device, means for converting speech to text from the received information, and intelligent processing means for extracting important content from the text. This makes it possible to quickly and accurately extract important information from meeting and online meeting data, and to store and use it in a structured format.

[0251] An "information processing device" is a device, such as a user's terminal or computer, that transmits data.

[0252] "Methods for converting speech to text" refer to technologies and algorithms that analyze speech data and convert it into meaningful text data.

[0253] "Intelligent processing means" refers to technologies that use machine learning or artificial intelligence to identify and extract important information from text data.

[0254] A "structured information format" is a data format in which extracted information is organized and stored using methods such as categorization and tagging, allowing for efficient reference later on.

[0255] "Means of collecting opinions and updating intelligent processing systems" refers to the process of improving accuracy and performance by receiving feedback from users and retraining intelligent processing systems.

[0256] A "speaker tag" is identification information assigned to audio data to distinguish between different speakers.

[0257] This invention provides an information processing system for efficiently utilizing meeting records and online meeting information within a company. The system mainly consists of a server and user terminals, and extracts information through data transmission, analysis, storage, and notification.

[0258] Users upload meeting audio or video data to the server using their devices. The devices use a dedicated application or web interface to verify the correctness of the data format before sending it to the server. The server converts the received data into text using speech recognition software, such as the Google Cloud Speech-to-Text API.

[0259] The transcribed data is analyzed by a server using a generative AI model. The generative AI model operates according to pre-configured prompts, such as "Extract customer names, proposals, and action items from the meeting content." The AI ​​model is trained using machine learning algorithms and improves its accuracy by incorporating user feedback.

[0260] The extracted key information is stored in a database in a structured format to facilitate later searching and referencing. The server sends a notification to the user when processing is complete, allowing them to review the meeting record. This notification is sent via email or business communication applications, such as Slack or Microsoft Teams.

[0261] For example, if a sales team leader uploads a video of their weekly meeting to the system, the server interprets the video data and compiles important information such as the next proposal schedule and customer feedback. The results are shared throughout the department, allowing each member to quickly access the necessary information and incorporate it into their work. This facilitates smooth information sharing between departments and improves overall company efficiency.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user selects the audio or video file of the meeting using their own device and uploads it to the system. The input is the audio or video data of the meeting. The device checks the file format and data size and sends the data to the server over the network. This is how the data is delivered to the server.

[0265] Step 2:

[0266] The server receives data sent from the terminal. The input is audio or video data sent from the terminal. The server checks the format of this data, extracts the audio portion as needed, and converts it to a parseable format (e.g., MP3). The output here is audio data in a speech recognition-readable format.

[0267] Step 3:

[0268] The server converts audio data into text using speech recognition technology. The input is audio data in a speech-recognizable format. A speech recognition module (e.g., Google Cloud Speech-to-Text API) analyzes the audio data and generates text data with speaker tags for each speaker. This process aims to accurately record speech, and the output is text data with speaker tags.

[0269] Step 4:

[0270] The server uses a generative AI model to extract important information from text data. The input is text data tagged with speaker tags. In this process, the AI ​​model performs analysis according to pre-configured prompts (e.g., "Extract customer name, proposal content, and action items"). The output is important information such as the extracted customer name and proposal content.

[0271] Step 5:

[0272] The server stores the extracted critical information in a structured data format in the database. The input is the critical information. The server organizes the information by category and tags it to allow for easy future searching. The output of this step is the critical information properly stored in the database.

[0273] Step 6:

[0274] The server sends a completion notification to the user based on the processing results. The input is the information indicating processing completion and saved data. The notification includes a link to view the processing results and is delivered to the user via email or a dedicated app. The output is the notification to the user.

[0275] Step 7:

[0276] The user submits feedback about the information provided. The input is the user's feedback. The server receives this feedback and retrains the generating AI model to improve the accuracy of information extraction. The output is the AI ​​model with updated accuracy.

[0277] (Application Example 1)

[0278] 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 glasses 214 will be referred to as the "terminal."

[0279] In factory operations, the inability to quickly and accurately utilize meeting minutes and online meeting content leads to delays in implementing decided action items and improvements. This can result in decreased productivity and delayed maintenance. To address this, there is a need for a system that effectively utilizes meeting data and allows work equipment to autonomously optimize operations.

[0280] 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.

[0281] In this invention, the server includes means for receiving data from user devices, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This allows the machine to autonomously adjust its work procedures by utilizing the received conference and meeting data, thereby improving productivity and streamlining maintenance work.

[0282] The "user device" refers to a terminal device used by a user to input or transmit information, including computers, smartphones, etc.

[0283] The "means for receiving data" refers to functions or protocols for incorporating information transmitted from a user device.

[0284] The "means for converting voice to text" refers to a technology that analyzes voice information and performs a process of converting it into corresponding character information.

[0285] The "artificial intelligence means" is a system for analyzing information using machine learning and natural language processing and extracting important data based on specific rules.

[0286] The "means for storing in a structured data format" refers to a database or storage system for recording the extracted information in an efficiently manageable and searchable format.

[0287] The "means for sending notifications" refers to a communication function used to inform a user of processing results or important information, including emails, app notifications, etc.

[0288] The "means for collecting feedback and updating the artificial intelligence means" refers to a process of collecting opinions and improvement requests from users and improving the performance of artificial intelligence based on them.

[0289] The "means for autonomously adjusting business procedures based on information received by a mechanical device" refers to a mechanism for a machine to improve and optimize its own operations and processes based on the analyzed information.

[0290] The server uses artificial intelligence technology to receive data from user devices, convert speech data into text data using speech recognition, and extract important information from the converted text data. This involves using the speech recognition library speech_recognition and transformers for natural language processing. When users upload meeting or conference data, the server analyzes it and stores important information such as identification information, suggested items, and work instructions as structured data in the database.

[0291] Furthermore, the server sends emails to users to notify them of the processing results, using smtplib. This feature allows users to quickly obtain important information that they should apply to their work. In addition, a system is in place to continuously improve the accuracy of information extraction by collecting user feedback and using it to update the AI ​​model.

[0292] As a concrete example, records of factory production meetings can be uploaded to the system, and the server can use AI analysis to extract improvement items and necessary maintenance instructions for the next meeting, and specific tasks can be set to be executed autonomously. This results in improved operational efficiency and reduced errors.

[0293] By utilizing a generative AI model, sharp analytical capabilities can be demonstrated, and instructions can be given in the form of, for example, "Extract the key points from the meeting and create an action list until the next production meeting."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user uploads audio data of a meeting or conference from their device to the server. The input is audio data, which is sent to the server. The server receives this audio data and temporarily stores it for the next step.

[0297] Step 2:

[0298] The server converts received audio data into text data using the speech_recognition library. The input is audio data, and the output is text data that transcribes the content of the audio. In this process, the audio waveform is analyzed, and the corresponding language structure is generated as text.

[0299] Step 3:

[0300] The server analyzes the generated text data using a generative AI model from the transformers library. The input is text data, and the output is a list of important information such as identification information, suggested items, and work instructions. The AI ​​model extracts important phrases and syntax from the text through natural language processing.

[0301] Step 4:

[0302] The server stores the extracted key information as structured data in a database. The input is a list of key information, and the output is the structured data stored in the database. This makes subsequent searching and use easier.

[0303] Step 5:

[0304] The server sends the extracted results via email to the user to notify them of the processing results. The input is structured data, and the output is a notification email to the user. The email includes a summary of the processed information and a link.

[0305] Step 6:

[0306] The user sends feedback to the server based on the notification they receive. The input is the user's feedback data, and the output is the server that receives it. The server updates its AI model through this feedback, improving the accuracy of subsequent processing.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0308] The system of the present invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further utilize emotion information. With this system, users can upload various data generated within the company to the system through a terminal. The data provided from the terminal is reliably received by the server, stored, and then processing is started.

[0309] The server converts the audio data from the video into text using speech recognition technology. Through this process, the content of the audio is converted into a form that can be utilized as character information, enabling analysis by an AI model. Next, the server applies natural language processing technology to automatically extract important information such as customer names, proposal contents, and action items from the text. The extracted information is stored in a database in a structured data format for subsequent search and analysis.

[0310] Furthermore, by integrating an emotion engine, this system recognizes potential emotions from the user's voice and text data. For example, it is possible to estimate what emotions the speaker has based on the tone and content of the speaker's voice during a meeting. The server extracts this emotion information and stores it in a structured data format together with the important information. The emotion information provides useful insights in specific communication situations and enables strategic support for decision-making and customer response activities.

[0311] Based on the processing results, the server sends a notification to the user. This notification is sent via email or application notification and includes a link that allows the user to quickly view the necessary information and sentiment analysis results. The user can also evaluate the provided information and sentiment results and provide feedback to the system. This feedback is used as training data for the AI ​​model, improving the system's accuracy and performance.

[0312] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts the proposal content and next sales actions from this video, but also recognizes the emotional state of the marketing personnel and customers who spoke. The emotion engine analyzes whether the customer's reaction to the discussed proposal was positive or negative, and notifies the person in charge of the results. In this way, the user can use this as concrete information to determine how well the proposal was received and what approach should be taken next.

[0313] This embodiment of the invention allows users to effectively utilize data and conduct business and customer interactions more strategically.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] Users select and upload meeting recordings and online meeting videos on a dedicated platform. The device converts the files to the required format and prepares to send the user's data to the server.

[0317] Step 2:

[0318] The server receives data sent by the user. This data is stored in temporary storage and organized to ensure security and efficiency.

[0319] Step 3:

[0320] The server starts the speech recognition engine to convert the audio data into text. The speech recognition engine extracts audio from the video and converts it into contextually appropriate text data.

[0321] Step 4:

[0322] The server passes the converted text data to an AI model, which extracts important information using natural language processing technology. The AI ​​model identifies customer names, proposals, action items, and other relevant information according to predefined criteria.

[0323] Step 5:

[0324] Simultaneously, the server activates an emotion engine to recognize emotions from text and speech features. The emotion engine analyzes what emotions the speaker is expressing and outputs the results.

[0325] Step 6:

[0326] The server converts the extracted critical and emotional information into a structured data format and stores it in a database. This prepares the information for later reference and analysis.

[0327] Step 7:

[0328] The server notifies the user when processing is complete. This notification includes a link to access the processing results, allowing the user to view them.

[0329] Step 8:

[0330] Users can review the provided information and sentiment analysis results and provide feedback to the system. The server collects this feedback and uses it as data to improve the AI ​​model and sentiment engine.

[0331] (Example 2)

[0332] 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".

[0333] While a large amount of information is shared in in-house and online meetings, there is a lack of effective means to organize and utilize this information for decision-making. Furthermore, it is difficult to identify the emotions of speakers and participants during meetings and use this information to make strategic decisions.

[0334] 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.

[0335] In this invention, the server includes means for receiving information from a user device, means for converting audio from the received information into documents, and processing means for extracting important information from the documents. This enables efficient organization of information generated within a company and facilitates strategic decision-making by performing sentiment analysis of speakers and participants.

[0336] A "user device" is a terminal device used by a user to transmit data.

[0337] "Means for receiving information" refers to the function that allows a server to receive data transmitted from a user device.

[0338] "Means of converting speech to text" refers to technologies for converting received speech data into text information.

[0339] "Processing means for extracting important information" refers to technology that automatically selects specific information from a converted document.

[0340] "Methods for saving in a structured format" refer to techniques for storing extracted information in a database in a specific format.

[0341] "Means of recognizing emotions" refers to technologies that analyze and identify emotions from user statements and text data.

[0342] "Means of sending notifications" refers to a function that sends a message to inform the user that processed information has been completed.

[0343] "Methods for collecting feedback and updating learning models" refer to technologies for collecting user evaluations and using them to improve AI models.

[0344] This invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further leverage emotional information. Users first upload meeting audio and video data generated within their company to a server using a device. The devices used in this process include personal computers and smartphones.

[0345] The server converts the received audio data into a document. For speech recognition, it uses speech recognition technologies such as the Google Cloud Speech-to-Text API. The audio data is analyzed using natural language processing libraries (e.g., SpaCy, BERT) to extract important information from the text. This process yields information such as customer names, proposals, and action items in a refined format. Furthermore, the server uses an emotion engine to identify the potential emotional states of the people who participated in the meeting from their audio and text data.

[0346] For example, emotions such as joy, surprise, and anger can be determined based on the tone and speed of a speaker's voice during a meeting, or keywords in the text. This emotional information, along with other important extracted information, is stored as structured data in a database and used for immediate or later analysis.

[0347] Based on the processing results, the server sends an email or application notification to the user. This notification includes a link to view the processed information and sentiment analysis results, making it immediately accessible to the user. The user can evaluate the provided information and sentiment results and provide feedback. This feedback is used to improve the AI ​​model and contribute to providing more accurate results.

[0348] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts information such as the proposal content and next sales actions from this video, but also recognizes the emotions of the participating marketing personnel and customers. The emotion engine analyzes the customers' reactions to the proposal and notifies the person in charge of the results. This allows the user to judge how the proposal was received and uses it as concrete information to decide on the next approach.

[0349] An example of input to the generative AI model is the prompt, "Summarize the proposals made in the meeting, analyze the speakers' emotions, and generate a report."

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] Users upload meeting audio and video data from their devices to the server. This is done by the user using a dedicated upload interface to select files from their device and send them to the server. The input is an audio or video file, and the output is saved on the server.

[0353] Step 2:

[0354] The server converts the received audio data into text using speech recognition technology. Specifically, it analyzes the audio file using the Google Cloud Speech-to-Text API and other tools, and converts the spoken content into a document format. The input to this process is audio data, and the output is the corresponding text data.

[0355] Step 3:

[0356] The server analyzes the converted text data using natural language processing techniques to extract key information such as customer names, proposal details, and action items. At this stage, the server analyzes the parts of speech in the text and identifies key information based on specific keywords. The input is text data, and the output is a set of extracted key information.

[0357] Step 4:

[0358] The server uses an emotion engine to analyze emotional information from text and audio data. Specifically, it estimates emotions based on the speaker's tone and speed of voice, as well as expressions in the text. In this step, text and audio data are taken as input, and emotional information is obtained as output.

[0359] Step 5:

[0360] The server stores the extracted important and sentiment information as structured data in a database. It then formats the data into JSON format and inserts it into the database to prepare for subsequent searches and analyses. The input is a collection of organized information, and the output is structured data in the database.

[0361] Step 6:

[0362] The server sends an email or application notification to the user based on the processing results. This notification includes a link to access the information and sentiment analysis results. The input is the processing results, and the output is the notification message.

[0363] Step 7:

[0364] Users evaluate the provided information and send feedback to the system. The server collects this feedback and improves the system's accuracy by updating the generated AI model. The input is the user's feedback, and the output is the data from the updated AI model.

[0365] (Application Example 2)

[0366] 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."

[0367] In brick-and-mortar stores, customer service requires staff to instantly grasp customer emotions and respond appropriately based on those emotions. However, conventional systems make it difficult to perform emotion analysis on the spot, potentially leading to missed opportunities to improve customer satisfaction. To solve this problem, real-time emotion analysis and feedback functions for rapid responses are essential.

[0368] 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.

[0369] In this invention, the server includes means for receiving data from a user device, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This makes it possible to analyze speech data in real time and provide feedback tailored to the customer's emotional state.

[0370] A "user device" is a device used to acquire data and send it to a server.

[0371] "Means of receiving data" refers to the function of receiving information provided by user devices within the server.

[0372] "Methods for converting speech to text" refer to the process of using speech recognition technology to convert speech data into a format that can be used as written information.

[0373] "Artificial intelligence methods" refer to technologies for extracting essential information from received data.

[0374] "Structured data format" refers to a standardized data format used to organize information and facilitate its later use and retrieval.

[0375] "Means of sending notifications" refers to a messaging function used to inform users of the analysis results.

[0376] "Methods for collecting feedback" refer to methods for compiling user opinions and reactions and using them to improve the system.

[0377] "Methods for analyzing emotions from audio data" refers to technologies for estimating a speaker's emotions based on their tone of voice and content.

[0378] "A means of providing real-time feedback tailored to the customer's emotional state" refers to a function that evaluates the customer's current emotions and immediately provides the most appropriate response.

[0379] This invention is a system for improving customer service in physical stores. First, the user interacts with the customer using a user device such as smart glasses. The audio data of this interaction is transmitted in real time from the user device to a server. The server uses a speech recognition engine to instantly convert this audio data into text. The speech recognition technology used is "Google Cloud Speech-to-Text".

[0380] The transcribed data is analyzed using natural language processing techniques to extract important information. For example, the names of products the customer is interested in and the suggested products are extracted. Simultaneously, a sentiment analysis engine (such as "IBM Watson Tone Analyzer") operates to analyze the customer's emotions from the tone and content of their voice.

[0381] The analysis results are notified in real time from the server to the user's smart glasses. This feedback allows the user to take immediate action based on the customer's emotional state. For example, if the customer is showing positive emotions, feedback is presented to the staff prompting them to "suggest related products."

[0382] For example, when a customer says, "This product has a great design," the server analyzes this positive response and notifies the user's device, "Next, please suggest additional products that would go well with this product."

[0383] This prompt text is generated by utilizing a generative AI model. A concrete example of a prompt text is, "Please enter the text of your conversation about the product." In this way, the present invention enhances customer interaction and improves service in physical stores.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The user device captures customer interactions as audio data and sends it to the server. The input is customer voice, and the output is digital audio data. The user device transmits this audio data to the server in real time.

[0387] Step 2:

[0388] The server converts the received audio data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). The input is digital audio data, and the output is text data. When the server converts the sound waveform into text, it uses the speech recognition engine's algorithm to perform accurate transcription.

[0389] Step 3:

[0390] The server extracts important information from text data using natural language processing techniques. The input is text data, and the output is structured data containing the extracted information. The server uses algorithms to identify user-friendly information, such as product names and suggested content.

[0391] Step 4:

[0392] The server analyzes emotions from text data using an emotion analysis engine (e.g., IBM Watson Tone Analyzer). The input is text data, and the output is emotion data indicating the emotional state. The server determines the speaker's emotions by analyzing the tone of the text and keywords related to emotions.

[0393] Step 5:

[0394] The server notifies the user's smart glasses of feedback in real time. Inputs are structured data and sentiment data, and output is feedback information provided to the user. The server sends information suggesting specific actions to the user via the notification system.

[0395] Step 6:

[0396] The user continues the conversation with the customer based on feedback received from the server. The input is feedback information from the server, and the output is suggestions and responses to the customer. The user decides on the next action and responds appropriately to the customer while looking at the display on their smart glasses.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] The present invention provides a platform for users to efficiently utilize meeting records and online meeting videos within a company. Specifically, users upload meeting data to the system from their own devices. In this process, the data entered by the user is transmitted to the server via the device. The server analyzes the received data and converts the audio data into text using a speech recognition module.

[0414] Next, the server uses artificial intelligence to analyze the transcribed data and extract key information. This AI model is pre-trained and capable of identifying specified information such as customer names, proposals, and action items. The extracted information is stored in a database as structured data, allowing users to easily search for the information they need later.

[0415] Furthermore, the server sends a notification to the user upon completion of processing. The notification is delivered to the user via email or a dedicated app and includes a link to view the processing results. This notification feature allows users to quickly obtain necessary information and incorporate it into their work.

[0416] Furthermore, users can submit feedback on the information provided by the server. The server collects this feedback and continuously updates its AI model to improve the accuracy of information extraction. This process allows the system to provide information that is better suited to the user's needs.

[0417] For example, consider a case where a sales team leader uploads a video of their weekly meeting to the system. In this process, the server transcribes the meeting content into text, and artificial intelligence extracts information such as the next proposal date and customer requests. The entire sales team is notified of the processing results, and each member can easily check the information necessary for their work. In this way, information sharing between different departments is facilitated, and work efficiency is improved. Thus, the present invention provides a practical system form for realizing the effective use of data in corporate activities.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The user selects meeting minutes or online meeting video files from their device and initiates the upload to the system. The device then prepares to send the selected data to the server in the specified format.

[0421] Step 2:

[0422] The server receives the uploaded data. The received data is temporarily stored in storage and kept there until it is ready for processing.

[0423] Step 3:

[0424] The server invokes a speech recognition engine to convert the audio contained in the video data into text data. This engine analyzes the audio data and converts it into text information while taking the context into consideration.

[0425] Step 4:

[0426] The server passes the obtained text data to an AI model, which uses natural language processing to extract important information. The AI ​​model is pre-trained and identifies customer names, proposals, and action items.

[0427] Step 5:

[0428] The server converts the extracted information into a structured data format and stores it in a database for efficient storage. This makes it easy to search and access later.

[0429] Step 6:

[0430] The server notifies the user that all data processing is complete. This notification is sent via email or application notification and includes a link to access the information.

[0431] Step 7:

[0432] Users verify the information provided by the server and provide feedback to improve the system's performance. This feedback is used to improve the AI ​​model.

[0433] Step 8:

[0434] The server uses accumulated feedback to retrain the AI ​​model, aiming to improve the accuracy of subsequent processes. This continuous improvement enhances the system's information extraction capabilities.

[0435] (Example 1)

[0436] 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."

[0437] In today's information-management-driven business environment, efficiently utilizing meeting records and online meeting data to quickly extract crucial information is a challenge across many industries. Conventional methods struggle to effectively organize and analyze vast amounts of meeting data, requiring significant time and effort, thus failing to leverage the data's true value. This invention aims to solve these problems.

[0438] 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.

[0439] In this invention, the server includes means for receiving information from an information processing device, means for converting speech to text from the received information, and intelligent processing means for extracting important content from the text. This makes it possible to quickly and accurately extract important information from meeting and online meeting data, and to store and use it in a structured format.

[0440] An "information processing device" is a device, such as a user's terminal or computer, that transmits data.

[0441] "Methods for converting speech to text" refer to technologies and algorithms that analyze speech data and convert it into meaningful text data.

[0442] "Intelligent processing means" refers to technologies that use machine learning or artificial intelligence to identify and extract important information from text data.

[0443] A "structured information format" is a data format in which extracted information is organized and stored using methods such as categorization and tagging, allowing for efficient reference later on.

[0444] "Means of collecting opinions and updating intelligent processing systems" refers to the process of improving accuracy and performance by receiving feedback from users and retraining intelligent processing systems.

[0445] A "speaker tag" is identification information assigned to audio data to distinguish between different speakers.

[0446] This invention provides an information processing system for efficiently utilizing meeting records and online meeting information within a company. The system mainly consists of a server and user terminals, and extracts information through data transmission, analysis, storage, and notification.

[0447] Users upload meeting audio or video data to the server using their devices. The devices use a dedicated application or web interface to verify the correctness of the data format before sending it to the server. The server converts the received data into text using speech recognition software, such as the Google Cloud Speech-to-Text API.

[0448] The transcribed data is analyzed by a server using a generative AI model. The generative AI model operates according to pre-configured prompts, such as "Extract customer names, proposals, and action items from the meeting content." The AI ​​model is trained using machine learning algorithms and improves its accuracy by incorporating user feedback.

[0449] The extracted key information is stored in a database in a structured format to facilitate later searching and referencing. The server sends a notification to the user when processing is complete, allowing them to review the meeting record. This notification is sent via email or business communication applications, such as Slack or Microsoft Teams.

[0450] For example, if a sales team leader uploads a video of their weekly meeting to the system, the server interprets the video data and compiles important information such as the next proposal schedule and customer feedback. The results are shared throughout the department, allowing each member to quickly access the necessary information and incorporate it into their work. This facilitates smooth information sharing between departments and improves overall company efficiency.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The user selects the audio or video file of the meeting using their own device and uploads it to the system. The input is the audio or video data of the meeting. The device checks the file format and data size and sends the data to the server over the network. This is how the data is delivered to the server.

[0454] Step 2:

[0455] The server receives data sent from the terminal. The input is audio or video data sent from the terminal. The server checks the format of this data, extracts the audio portion as needed, and converts it to a parseable format (e.g., MP3). The output here is audio data in a speech recognition-readable format.

[0456] Step 3:

[0457] The server converts audio data into text using speech recognition technology. The input is audio data in a speech-recognizable format. A speech recognition module (e.g., Google Cloud Speech-to-Text API) analyzes the audio data and generates text data with speaker tags for each speaker. This process aims to accurately record speech, and the output is text data with speaker tags.

[0458] Step 4:

[0459] The server uses a generative AI model to extract important information from text data. The input is text data tagged with speaker tags. In this process, the AI ​​model performs analysis according to pre-configured prompts (e.g., "Extract customer name, proposal content, and action items"). The output is important information such as the extracted customer name and proposal content.

[0460] Step 5:

[0461] The server stores the extracted critical information in a structured data format in the database. The input is the critical information. The server organizes the information by category and tags it to allow for easy future searching. The output of this step is the critical information properly stored in the database.

[0462] Step 6:

[0463] The server sends a completion notification to the user based on the processing results. The input is the information indicating processing completion and saved data. The notification includes a link to view the processing results and is delivered to the user via email or a dedicated app. The output is the notification to the user.

[0464] Step 7:

[0465] The user submits feedback about the information provided. The input is the user's feedback. The server receives this feedback and retrains the generating AI model to improve the accuracy of information extraction. The output is the AI ​​model with updated accuracy.

[0466] (Application Example 1)

[0467] 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."

[0468] In factory operations, the inability to quickly and accurately utilize meeting minutes and online meeting content leads to delays in implementing decided action items and improvements. This can result in decreased productivity and delayed maintenance. To address this, there is a need for a system that effectively utilizes meeting data and allows work equipment to autonomously optimize operations.

[0469] 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.

[0470] In this invention, the server includes means for receiving data from user devices, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This allows the machine to autonomously adjust its work procedures by utilizing the received conference and meeting data, thereby improving productivity and streamlining maintenance work.

[0471] "User device" refers to a terminal device used by a user to input or transmit information, and includes computers, smartphones, and other similar devices.

[0472] "Means of receiving data" refers to the functions and protocols used to retrieve information transmitted from user devices.

[0473] "Methods for converting speech to text" refers to technologies that analyze speech information and convert it into corresponding textual information.

[0474] "Artificial intelligence tools" are systems that use machine learning and natural language processing to analyze information and extract important data based on specific rules.

[0475] "Means of storing data in a structured data format" refers to databases and storage systems that record extracted information in a format that allows for efficient management and retrieval.

[0476] "Means of sending notifications" refers to communication functions used to inform users of processing results or important information, and includes email and app notifications.

[0477] "Means of collecting feedback and updating artificial intelligence means" refers to the process of gathering opinions and improvement requests from users and using them to improve the performance of artificial intelligence.

[0478] "Means by which a machine autonomously adjusts its work procedures based on information it receives" refers to a mechanism that allows a machine to improve and optimize its own operation and processes based on analyzed information.

[0479] The server uses artificial intelligence technology to receive data from user devices, convert speech data into text data using speech recognition, and extract important information from the converted text data. This involves using the speech recognition library speech_recognition and transformers for natural language processing. When users upload meeting or conference data, the server analyzes it and stores important information such as identification information, suggested items, and work instructions as structured data in the database.

[0480] Furthermore, the server sends emails to users to notify them of the processing results, using smtplib. This feature allows users to quickly obtain important information that they should apply to their work. In addition, a system is in place to continuously improve the accuracy of information extraction by collecting user feedback and using it to update the AI ​​model.

[0481] As a concrete example, records of factory production meetings can be uploaded to the system, and the server can use AI analysis to extract improvement items and necessary maintenance instructions for the next meeting, and specific tasks can be set to be executed autonomously. This results in improved operational efficiency and reduced errors.

[0482] By utilizing a generative AI model, sharp analytical capabilities can be demonstrated, and instructions can be given in the form of, for example, "Extract the key points from the meeting and create an action list until the next production meeting."

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] The user uploads audio data of a meeting or conference from their device to the server. The input is audio data, which is sent to the server. The server receives this audio data and temporarily stores it for the next step.

[0486] Step 2:

[0487] The server converts received audio data into text data using the speech_recognition library. The input is audio data, and the output is text data that transcribes the content of the audio. In this process, the audio waveform is analyzed, and the corresponding language structure is generated as text.

[0488] Step 3:

[0489] The server analyzes the generated text data using a generative AI model from the transformers library. The input is text data, and the output is a list of important information such as identification information, suggested items, and work instructions. The AI ​​model extracts important phrases and syntax from the text through natural language processing.

[0490] Step 4:

[0491] The server stores the extracted key information as structured data in a database. The input is a list of key information, and the output is the structured data stored in the database. This makes subsequent searching and use easier.

[0492] Step 5:

[0493] The server sends the extracted results via email to the user to notify them of the processing results. The input is structured data, and the output is a notification email to the user. The email includes a summary of the processed information and a link.

[0494] Step 6:

[0495] The user sends feedback to the server based on the notification they receive. The input is the user's feedback data, and the output is the server that receives it. The server updates its AI model through this feedback, improving the accuracy of subsequent processing.

[0496] 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.

[0497] The present invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further leverage emotional information. This system allows users to upload various data generated within their company via a terminal. The data provided from the terminal is reliably received and stored by the server, after which processing begins.

[0498] The server converts audio data from the video into text using speech recognition technology. This process transforms the audio content into a format usable as text information, enabling analysis by AI models. Next, the server applies natural language processing technology to automatically extract important information from the text, such as customer names, proposal details, and action items. The extracted information is stored in a database in a structured data format, ready for subsequent searches and analyses.

[0499] Furthermore, by integrating an emotion engine, this system recognizes potential emotions from user voice and text data. For example, it can estimate what emotions a speaker is feeling based on the tone of their voice and the content of their speech during a meeting. The server extracts this emotion information and stores it in a structured data format along with other important information. This emotion information provides valuable insights in specific communication situations, enabling strategic support for decision-making and customer service activities.

[0500] Based on the processing results, the server sends a notification to the user. This notification is sent via email or application notification and includes a link that allows the user to quickly view the necessary information and sentiment analysis results. The user can also evaluate the provided information and sentiment results and provide feedback to the system. This feedback is used as training data for the AI ​​model, improving the system's accuracy and performance.

[0501] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts the proposal content and next sales actions from this video, but also recognizes the emotional state of the marketing personnel and customers who spoke. The emotion engine analyzes whether the customer's reaction to the discussed proposal was positive or negative, and notifies the person in charge of the results. In this way, the user can use this as concrete information to determine how well the proposal was received and what approach should be taken next.

[0502] This embodiment of the invention allows users to effectively utilize data and conduct business and customer interactions more strategically.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] Users select and upload meeting recordings and online meeting videos on a dedicated platform. The device converts the files to the required format and prepares to send the user's data to the server.

[0506] Step 2:

[0507] The server receives data sent by the user. This data is stored in temporary storage and organized to ensure security and efficiency.

[0508] Step 3:

[0509] The server starts the speech recognition engine to convert the audio data into text. The speech recognition engine extracts audio from the video and converts it into contextually appropriate text data.

[0510] Step 4:

[0511] The server passes the converted text data to an AI model, which extracts important information using natural language processing technology. The AI ​​model identifies customer names, proposals, action items, and other relevant information according to predefined criteria.

[0512] Step 5:

[0513] Simultaneously, the server activates an emotion engine to recognize emotions from text and speech features. The emotion engine analyzes what emotions the speaker is expressing and outputs the results.

[0514] Step 6:

[0515] The server converts the extracted critical and emotional information into a structured data format and stores it in a database. This prepares the information for later reference and analysis.

[0516] Step 7:

[0517] The server notifies the user when processing is complete. This notification includes a link to access the processing results, allowing the user to view them.

[0518] Step 8:

[0519] Users can review the provided information and sentiment analysis results and provide feedback to the system. The server collects this feedback and uses it as data to improve the AI ​​model and sentiment engine.

[0520] (Example 2)

[0521] 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."

[0522] While a large amount of information is shared in in-house and online meetings, there is a lack of effective means to organize and utilize this information for decision-making. Furthermore, it is difficult to identify the emotions of speakers and participants during meetings and use this information to make strategic decisions.

[0523] 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.

[0524] In this invention, the server includes means for receiving information from a user device, means for converting audio from the received information into documents, and processing means for extracting important information from the documents. This enables efficient organization of information generated within a company and facilitates strategic decision-making by performing sentiment analysis of speakers and participants.

[0525] A "user device" is a terminal device used by a user to transmit data.

[0526] "Means for receiving information" refers to the function that allows a server to receive data transmitted from a user device.

[0527] "Means of converting speech to text" refers to technologies for converting received speech data into text information.

[0528] "Processing means for extracting important information" refers to technology that automatically selects specific information from a converted document.

[0529] "Methods for saving in a structured format" refer to techniques for storing extracted information in a database in a specific format.

[0530] "Means of recognizing emotions" refers to technologies that analyze and identify emotions from user statements and text data.

[0531] "Means of sending notifications" refers to a function that sends a message to inform the user that processed information has been completed.

[0532] "Methods for collecting feedback and updating learning models" refer to technologies for collecting user evaluations and using them to improve AI models.

[0533] This invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further leverage emotional information. Users first upload meeting audio and video data generated within their company to a server using a device. The devices used in this process include personal computers and smartphones.

[0534] The server converts the received audio data into a document. For speech recognition, it uses speech recognition technologies such as the Google Cloud Speech-to-Text API. The audio data is analyzed using natural language processing libraries (e.g., SpaCy, BERT) to extract important information from the text. This process yields information such as customer names, proposals, and action items in a refined format. Furthermore, the server uses an emotion engine to identify the potential emotional states of the people who participated in the meeting from their audio and text data.

[0535] For example, emotions such as joy, surprise, and anger can be determined based on the tone and speed of a speaker's voice during a meeting, or keywords in the text. This emotional information, along with other important extracted information, is stored as structured data in a database and used for immediate or later analysis.

[0536] Based on the processing results, the server sends an email or application notification to the user. This notification includes a link to view the processed information and sentiment analysis results, making it immediately accessible to the user. The user can evaluate the provided information and sentiment results and provide feedback. This feedback is used to improve the AI ​​model and contribute to providing more accurate results.

[0537] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts information such as the proposal content and next sales actions from this video, but also recognizes the emotions of the participating marketing personnel and customers. The emotion engine analyzes the customers' reactions to the proposal and notifies the person in charge of the results. This allows the user to judge how the proposal was received and uses it as concrete information to decide on the next approach.

[0538] An example of input to the generative AI model is the prompt, "Summarize the proposals made in the meeting, analyze the speakers' emotions, and generate a report."

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] Users upload meeting audio and video data from their devices to the server. This is done by the user using a dedicated upload interface to select files from their device and send them to the server. The input is an audio or video file, and the output is saved on the server.

[0542] Step 2:

[0543] The server converts the received audio data into text using speech recognition technology. Specifically, it analyzes the audio file using the Google Cloud Speech-to-Text API and other tools, and converts the spoken content into a document format. The input to this process is audio data, and the output is the corresponding text data.

[0544] Step 3:

[0545] The server analyzes the converted text data using natural language processing techniques to extract key information such as customer names, proposal details, and action items. At this stage, the server analyzes the parts of speech in the text and identifies key information based on specific keywords. The input is text data, and the output is a set of extracted key information.

[0546] Step 4:

[0547] The server uses an emotion engine to analyze emotional information from text and audio data. Specifically, it estimates emotions based on the speaker's tone and speed of voice, as well as expressions in the text. In this step, text and audio data are taken as input, and emotional information is obtained as output.

[0548] Step 5:

[0549] The server stores the extracted important and sentiment information as structured data in a database. It then formats the data into JSON format and inserts it into the database to prepare for subsequent searches and analyses. The input is a collection of organized information, and the output is structured data in the database.

[0550] Step 6:

[0551] The server sends an email or application notification to the user based on the processing results. This notification includes a link to access the information and sentiment analysis results. The input is the processing results, and the output is the notification message.

[0552] Step 7:

[0553] Users evaluate the provided information and send feedback to the system. The server collects this feedback and improves the system's accuracy by updating the generated AI model. The input is the user's feedback, and the output is the data from the updated AI model.

[0554] (Application Example 2)

[0555] 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."

[0556] In brick-and-mortar stores, customer service requires staff to instantly grasp customer emotions and respond appropriately based on those emotions. However, conventional systems make it difficult to perform emotion analysis on the spot, potentially leading to missed opportunities to improve customer satisfaction. To solve this problem, real-time emotion analysis and feedback functions for rapid responses are essential.

[0557] 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.

[0558] In this invention, the server includes means for receiving data from a user device, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This makes it possible to analyze speech data in real time and provide feedback tailored to the customer's emotional state.

[0559] A "user device" is a device used to acquire data and send it to a server.

[0560] "Means of receiving data" refers to the function of receiving information provided by user devices within the server.

[0561] "Methods for converting speech to text" refer to the process of using speech recognition technology to convert speech data into a format that can be used as written information.

[0562] "Artificial intelligence methods" refer to technologies for extracting essential information from received data.

[0563] "Structured data format" refers to a standardized data format used to organize information and facilitate its later use and retrieval.

[0564] "Means of sending notifications" refers to a messaging function used to inform users of the analysis results.

[0565] "Methods for collecting feedback" refer to methods for compiling user opinions and reactions and using them to improve the system.

[0566] "Methods for analyzing emotions from audio data" refers to technologies for estimating a speaker's emotions based on their tone of voice and content.

[0567] "A means of providing real-time feedback tailored to the customer's emotional state" refers to a function that evaluates the customer's current emotions and immediately provides the most appropriate response.

[0568] This invention is a system for improving customer service in physical stores. First, the user interacts with the customer using a user device such as smart glasses. The audio data of this interaction is transmitted in real time from the user device to a server. The server uses a speech recognition engine to instantly convert this audio data into text. The speech recognition technology used is "Google Cloud Speech-to-Text".

[0569] The transcribed data is analyzed using natural language processing techniques to extract important information. For example, the names of products the customer is interested in and the suggested products are extracted. Simultaneously, a sentiment analysis engine (such as "IBM Watson Tone Analyzer") operates to analyze the customer's emotions from the tone and content of their voice.

[0570] The analysis results are notified in real time from the server to the user's smart glasses. This feedback allows the user to take immediate action based on the customer's emotional state. For example, if the customer is showing positive emotions, feedback is presented to the staff prompting them to "suggest related products."

[0571] For example, when a customer says, "This product has a great design," the server analyzes this positive response and notifies the user's device, "Next, please suggest additional products that would go well with this product."

[0572] This prompt text is generated by utilizing a generative AI model. A concrete example of a prompt text is, "Please enter the text of your conversation about the product." In this way, the present invention enhances customer interaction and improves service in physical stores.

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The user device captures customer interactions as audio data and sends it to the server. The input is customer voice, and the output is digital audio data. The user device transmits this audio data to the server in real time.

[0576] Step 2:

[0577] The server converts the received audio data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). The input is digital audio data, and the output is text data. When the server converts the sound waveform into text, it uses the speech recognition engine's algorithm to perform accurate transcription.

[0578] Step 3:

[0579] The server extracts important information from text data using natural language processing techniques. The input is text data, and the output is structured data containing the extracted information. The server uses algorithms to identify user-friendly information, such as product names and suggested content.

[0580] Step 4:

[0581] The server analyzes emotions from text data using an emotion analysis engine (e.g., IBM Watson Tone Analyzer). The input is text data, and the output is emotion data indicating the emotional state. The server determines the speaker's emotions by analyzing the tone of the text and keywords related to emotions.

[0582] Step 5:

[0583] The server notifies the user's smart glasses of feedback in real time. Inputs are structured data and sentiment data, and output is feedback information provided to the user. The server sends information suggesting specific actions to the user via the notification system.

[0584] Step 6:

[0585] The user continues the conversation with the customer based on feedback received from the server. The input is feedback information from the server, and the output is suggestions and responses to the customer. The user decides on the next action and responds appropriately to the customer while looking at the display on their smart glasses.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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".

[0603] The present invention provides a platform for users to efficiently utilize meeting records and online meeting videos within a company. Specifically, users upload meeting data to the system from their own devices. In this process, the data entered by the user is transmitted to the server via the device. The server analyzes the received data and converts the audio data into text using a speech recognition module.

[0604] Next, the server uses artificial intelligence to analyze the transcribed data and extract key information. This AI model is pre-trained and capable of identifying specified information such as customer names, proposals, and action items. The extracted information is stored in a database as structured data, allowing users to easily search for the information they need later.

[0605] Furthermore, the server sends a notification to the user upon completion of processing. The notification is delivered to the user via email or a dedicated app and includes a link to view the processing results. This notification feature allows users to quickly obtain necessary information and incorporate it into their work.

[0606] Furthermore, users can submit feedback on the information provided by the server. The server collects this feedback and continuously updates its AI model to improve the accuracy of information extraction. This process allows the system to provide information that is better suited to the user's needs.

[0607] For example, consider a case where a sales team leader uploads a video of their weekly meeting to the system. In this process, the server transcribes the meeting content into text, and artificial intelligence extracts information such as the next proposal date and customer requests. The entire sales team is notified of the processing results, and each member can easily check the information necessary for their work. In this way, information sharing between different departments is facilitated, and work efficiency is improved. Thus, the present invention provides a practical system form for realizing the effective use of data in corporate activities.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The user selects meeting minutes or online meeting video files from their device and initiates the upload to the system. The device then prepares to send the selected data to the server in the specified format.

[0611] Step 2:

[0612] The server receives the uploaded data. The received data is temporarily stored in storage and kept there until it is ready for processing.

[0613] Step 3:

[0614] The server invokes a speech recognition engine to convert the audio contained in the video data into text data. This engine analyzes the audio data and converts it into text information while taking the context into consideration.

[0615] Step 4:

[0616] The server passes the obtained text data to an AI model, which uses natural language processing to extract important information. The AI ​​model is pre-trained and identifies customer names, proposals, and action items.

[0617] Step 5:

[0618] The server converts the extracted information into a structured data format and stores it in a database for efficient storage. This makes it easy to search and access later.

[0619] Step 6:

[0620] The server notifies the user that all data processing is complete. This notification is sent via email or application notification and includes a link to access the information.

[0621] Step 7:

[0622] Users verify the information provided by the server and provide feedback to improve the system's performance. This feedback is used to improve the AI ​​model.

[0623] Step 8:

[0624] The server uses accumulated feedback to retrain the AI ​​model, aiming to improve the accuracy of subsequent processes. This continuous improvement enhances the system's information extraction capabilities.

[0625] (Example 1)

[0626] 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".

[0627] In today's information-management-driven business environment, efficiently utilizing meeting records and online meeting data to quickly extract crucial information is a challenge across many industries. Conventional methods struggle to effectively organize and analyze vast amounts of meeting data, requiring significant time and effort, thus failing to leverage the data's true value. This invention aims to solve these problems.

[0628] 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.

[0629] In this invention, the server includes means for receiving information from an information processing device, means for converting speech to text from the received information, and intelligent processing means for extracting important content from the text. This makes it possible to quickly and accurately extract important information from meeting and online meeting data, and to store and use it in a structured format.

[0630] An "information processing device" is a device, such as a user's terminal or computer, that transmits data.

[0631] "Methods for converting speech to text" refer to technologies and algorithms that analyze speech data and convert it into meaningful text data.

[0632] "Intelligent processing means" refers to technologies that use machine learning or artificial intelligence to identify and extract important information from text data.

[0633] A "structured information format" is a data format in which extracted information is organized and stored using methods such as categorization and tagging, allowing for efficient reference later on.

[0634] "Means of collecting opinions and updating intelligent processing systems" refers to the process of improving accuracy and performance by receiving feedback from users and retraining intelligent processing systems.

[0635] A "speaker tag" is identification information assigned to audio data to distinguish between different speakers.

[0636] This invention provides an information processing system for efficiently utilizing meeting records and online meeting information within a company. The system mainly consists of a server and user terminals, and extracts information through data transmission, analysis, storage, and notification.

[0637] Users upload meeting audio or video data to the server using their devices. The devices use a dedicated application or web interface to verify the correctness of the data format before sending it to the server. The server converts the received data into text using speech recognition software, such as the Google Cloud Speech-to-Text API.

[0638] The transcribed data is analyzed by a server using a generative AI model. The generative AI model operates according to pre-configured prompts, such as "Extract customer names, proposals, and action items from the meeting content." The AI ​​model is trained using machine learning algorithms and improves its accuracy by incorporating user feedback.

[0639] The extracted key information is stored in a database in a structured format to facilitate later searching and referencing. The server sends a notification to the user when processing is complete, allowing them to review the meeting record. This notification is sent via email or business communication applications, such as Slack or Microsoft Teams.

[0640] For example, if a sales team leader uploads a video of their weekly meeting to the system, the server interprets the video data and compiles important information such as the next proposal schedule and customer feedback. The results are shared throughout the department, allowing each member to quickly access the necessary information and incorporate it into their work. This facilitates smooth information sharing between departments and improves overall company efficiency.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The user selects the audio or video file of the meeting using their own device and uploads it to the system. The input is the audio or video data of the meeting. The device checks the file format and data size and sends the data to the server over the network. This is how the data is delivered to the server.

[0644] Step 2:

[0645] The server receives data sent from the terminal. The input is audio or video data sent from the terminal. The server checks the format of this data, extracts the audio portion as needed, and converts it to a parseable format (e.g., MP3). The output here is audio data in a speech recognition-readable format.

[0646] Step 3:

[0647] The server converts audio data into text using speech recognition technology. The input is audio data in a speech-recognizable format. A speech recognition module (e.g., Google Cloud Speech-to-Text API) analyzes the audio data and generates text data with speaker tags for each speaker. This process aims to accurately record speech, and the output is text data with speaker tags.

[0648] Step 4:

[0649] The server uses a generative AI model to extract important information from text data. The input is text data tagged with speaker tags. In this process, the AI ​​model performs analysis according to pre-configured prompts (e.g., "Extract customer name, proposal content, and action items"). The output is important information such as the extracted customer name and proposal content.

[0650] Step 5:

[0651] The server stores the extracted critical information in a structured data format in the database. The input is the critical information. The server organizes the information by category and tags it to allow for easy future searching. The output of this step is the critical information properly stored in the database.

[0652] Step 6:

[0653] The server sends a completion notification to the user based on the processing results. The input is the information indicating processing completion and saved data. The notification includes a link to view the processing results and is delivered to the user via email or a dedicated app. The output is the notification to the user.

[0654] Step 7:

[0655] The user submits feedback about the information provided. The input is the user's feedback. The server receives this feedback and retrains the generating AI model to improve the accuracy of information extraction. The output is the AI ​​model with updated accuracy.

[0656] (Application Example 1)

[0657] 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".

[0658] In factory operations, the inability to quickly and accurately utilize meeting minutes and online meeting content leads to delays in implementing decided action items and improvements. This can result in decreased productivity and delayed maintenance. To address this, there is a need for a system that effectively utilizes meeting data and allows work equipment to autonomously optimize operations.

[0659] 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.

[0660] In this invention, the server includes means for receiving data from user devices, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This allows the machine to autonomously adjust its work procedures by utilizing the received conference and meeting data, thereby improving productivity and streamlining maintenance work.

[0661] "User device" refers to a terminal device used by a user to input or transmit information, and includes computers, smartphones, and other similar devices.

[0662] "Means of receiving data" refers to the functions and protocols used to retrieve information transmitted from user devices.

[0663] "Methods for converting speech to text" refers to technologies that analyze speech information and convert it into corresponding textual information.

[0664] "Artificial intelligence tools" are systems that use machine learning and natural language processing to analyze information and extract important data based on specific rules.

[0665] "Means of storing data in a structured data format" refers to databases and storage systems that record extracted information in a format that allows for efficient management and retrieval.

[0666] "Means of sending notifications" refers to communication functions used to inform users of processing results or important information, and includes email and app notifications.

[0667] "Means of collecting feedback and updating artificial intelligence means" refers to the process of gathering opinions and improvement requests from users and using them to improve the performance of artificial intelligence.

[0668] "Means by which a machine autonomously adjusts its work procedures based on information it receives" refers to a mechanism that allows a machine to improve and optimize its own operation and processes based on analyzed information.

[0669] The server uses artificial intelligence technology to receive data from user devices, convert speech data into text data using speech recognition, and extract important information from the converted text data. This involves using the speech recognition library speech_recognition and transformers for natural language processing. When users upload meeting or conference data, the server analyzes it and stores important information such as identification information, suggested items, and work instructions as structured data in the database.

[0670] Furthermore, the server sends emails to users to notify them of the processing results, using smtplib. This feature allows users to quickly obtain important information that they should apply to their work. In addition, a system is in place to continuously improve the accuracy of information extraction by collecting user feedback and using it to update the AI ​​model.

[0671] As a concrete example, records of factory production meetings can be uploaded to the system, and the server can use AI analysis to extract improvement items and necessary maintenance instructions for the next meeting, and specific tasks can be set to be executed autonomously. This results in improved operational efficiency and reduced errors.

[0672] By utilizing a generative AI model, sharp analytical capabilities can be demonstrated, and instructions can be given in the form of, for example, "Extract the key points from the meeting and create an action list until the next production meeting."

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The user uploads audio data of a meeting or conference from their device to the server. The input is audio data, which is sent to the server. The server receives this audio data and temporarily stores it for the next step.

[0676] Step 2:

[0677] The server converts received audio data into text data using the speech_recognition library. The input is audio data, and the output is text data that transcribes the content of the audio. In this process, the audio waveform is analyzed, and the corresponding language structure is generated as text.

[0678] Step 3:

[0679] The server analyzes the generated text data using a generative AI model from the transformers library. The input is text data, and the output is a list of important information such as identification information, suggested items, and work instructions. The AI ​​model extracts important phrases and syntax from the text through natural language processing.

[0680] Step 4:

[0681] The server stores the extracted key information as structured data in a database. The input is a list of key information, and the output is the structured data stored in the database. This makes subsequent searching and use easier.

[0682] Step 5:

[0683] The server sends the extracted results via email to the user to notify them of the processing results. The input is structured data, and the output is a notification email to the user. The email includes a summary of the processed information and a link.

[0684] Step 6:

[0685] The user sends feedback to the server based on the notification they receive. The input is the user's feedback data, and the output is the server that receives it. The server updates its AI model through this feedback, improving the accuracy of subsequent processing.

[0686] 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.

[0687] The present invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further leverage emotional information. This system allows users to upload various data generated within their company via a terminal. The data provided from the terminal is reliably received and stored by the server, after which processing begins.

[0688] The server converts audio data from the video into text using speech recognition technology. This process transforms the audio content into a format usable as text information, enabling analysis by AI models. Next, the server applies natural language processing technology to automatically extract important information from the text, such as customer names, proposal details, and action items. The extracted information is stored in a database in a structured data format, ready for subsequent searches and analyses.

[0689] Furthermore, by integrating an emotion engine, this system recognizes potential emotions from user voice and text data. For example, it can estimate what emotions a speaker is feeling based on the tone of their voice and the content of their speech during a meeting. The server extracts this emotion information and stores it in a structured data format along with other important information. This emotion information provides valuable insights in specific communication situations, enabling strategic support for decision-making and customer service activities.

[0690] Based on the processing results, the server sends a notification to the user. This notification is sent via email or application notification and includes a link that allows the user to quickly view the necessary information and sentiment analysis results. The user can also evaluate the provided information and sentiment results and provide feedback to the system. This feedback is used as training data for the AI ​​model, improving the system's accuracy and performance.

[0691] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts the proposal content and next sales actions from this video, but also recognizes the emotional state of the marketing personnel and customers who spoke. The emotion engine analyzes whether the customer's reaction to the discussed proposal was positive or negative, and notifies the person in charge of the results. In this way, the user can use this as concrete information to determine how well the proposal was received and what approach should be taken next.

[0692] This embodiment of the invention allows users to effectively utilize data and conduct business and customer interactions more strategically.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] Users select and upload meeting recordings and online meeting videos on a dedicated platform. The device converts the files to the required format and prepares to send the user's data to the server.

[0696] Step 2:

[0697] The server receives data sent by the user. This data is stored in temporary storage and organized to ensure security and efficiency.

[0698] Step 3:

[0699] The server starts the speech recognition engine to convert the audio data into text. The speech recognition engine extracts audio from the video and converts it into contextually appropriate text data.

[0700] Step 4:

[0701] The server passes the converted text data to an AI model, which extracts important information using natural language processing technology. The AI ​​model identifies customer names, proposals, action items, and other relevant information according to predefined criteria.

[0702] Step 5:

[0703] Simultaneously, the server activates an emotion engine to recognize emotions from text and speech features. The emotion engine analyzes what emotions the speaker is expressing and outputs the results.

[0704] Step 6:

[0705] The server converts the extracted critical and emotional information into a structured data format and stores it in a database. This prepares the information for later reference and analysis.

[0706] Step 7:

[0707] The server notifies the user when processing is complete. This notification includes a link to access the processing results, allowing the user to view them.

[0708] Step 8:

[0709] Users can review the provided information and sentiment analysis results and provide feedback to the system. The server collects this feedback and uses it as data to improve the AI ​​model and sentiment engine.

[0710] (Example 2)

[0711] 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".

[0712] While a large amount of information is shared in in-house and online meetings, there is a lack of effective means to organize and utilize this information for decision-making. Furthermore, it is difficult to identify the emotions of speakers and participants during meetings and use this information to make strategic decisions.

[0713] 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.

[0714] In this invention, the server includes means for receiving information from a user device, means for converting audio from the received information into documents, and processing means for extracting important information from the documents. This enables efficient organization of information generated within a company and facilitates strategic decision-making by performing sentiment analysis of speakers and participants.

[0715] A "user device" is a terminal device used by a user to transmit data.

[0716] "Means for receiving information" refers to the function that allows a server to receive data transmitted from a user device.

[0717] "Means of converting speech to text" refers to technologies for converting received speech data into text information.

[0718] "Processing means for extracting important information" refers to technology that automatically selects specific information from a converted document.

[0719] "Methods for saving in a structured format" refer to techniques for storing extracted information in a database in a specific format.

[0720] "Means of recognizing emotions" refers to technologies that analyze and identify emotions from user statements and text data.

[0721] "Means of sending notifications" refers to a function that sends a message to inform the user that processed information has been completed.

[0722] "Methods for collecting feedback and updating learning models" refer to technologies for collecting user evaluations and using them to improve AI models.

[0723] This invention provides an innovative platform for users to efficiently utilize meeting minutes and online meeting videos, and further leverage emotional information. Users first upload meeting audio and video data generated within their company to a server using a device. The devices used in this process include personal computers and smartphones.

[0724] The server converts the received audio data into a document. For speech recognition, it uses speech recognition technologies such as the Google Cloud Speech-to-Text API. The audio data is analyzed using natural language processing libraries (e.g., SpaCy, BERT) to extract important information from the text. This process yields information such as customer names, proposals, and action items in a refined format. Furthermore, the server uses an emotion engine to identify the potential emotional states of the people who participated in the meeting from their audio and text data.

[0725] For example, emotions such as joy, surprise, and anger can be determined based on the tone and speed of a speaker's voice during a meeting, or keywords in the text. This emotional information, along with other important extracted information, is stored as structured data in a database and used for immediate or later analysis.

[0726] Based on the processing results, the server sends an email or application notification to the user. This notification includes a link to view the processed information and sentiment analysis results, making it immediately accessible to the user. The user can evaluate the provided information and sentiment results and provide feedback. This feedback is used to improve the AI ​​model and contribute to providing more accurate results.

[0727] As a concrete example, consider a scenario where a user in the marketing department uploads a video of a product proposal meeting to the system. The server not only extracts information such as the proposal content and next sales actions from this video, but also recognizes the emotions of the participating marketing personnel and customers. The emotion engine analyzes the customers' reactions to the proposal and notifies the person in charge of the results. This allows the user to judge how the proposal was received and uses it as concrete information to decide on the next approach.

[0728] An example of input to the generative AI model is the prompt, "Summarize the proposals made in the meeting, analyze the speakers' emotions, and generate a report."

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] Users upload meeting audio and video data from their devices to the server. This is done by the user using a dedicated upload interface to select files from their device and send them to the server. The input is an audio or video file, and the output is saved on the server.

[0732] Step 2:

[0733] The server converts the received audio data into text using speech recognition technology. Specifically, it analyzes the audio file using the Google Cloud Speech-to-Text API and other tools, and converts the spoken content into a document format. The input to this process is audio data, and the output is the corresponding text data.

[0734] Step 3:

[0735] The server analyzes the converted text data using natural language processing techniques to extract key information such as customer names, proposal details, and action items. At this stage, the server analyzes the parts of speech in the text and identifies key information based on specific keywords. The input is text data, and the output is a set of extracted key information.

[0736] Step 4:

[0737] The server uses an emotion engine to analyze emotional information from text and audio data. Specifically, it estimates emotions based on the speaker's tone and speed of voice, as well as expressions in the text. In this step, text and audio data are taken as input, and emotional information is obtained as output.

[0738] Step 5:

[0739] The server stores the extracted important and sentiment information as structured data in a database. It then formats the data into JSON format and inserts it into the database to prepare for subsequent searches and analyses. The input is a collection of organized information, and the output is structured data in the database.

[0740] Step 6:

[0741] The server sends an email or application notification to the user based on the processing results. This notification includes a link to access the information and sentiment analysis results. The input is the processing results, and the output is the notification message.

[0742] Step 7:

[0743] Users evaluate the provided information and send feedback to the system. The server collects this feedback and improves the system's accuracy by updating the generated AI model. The input is the user's feedback, and the output is the data from the updated AI model.

[0744] (Application Example 2)

[0745] 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".

[0746] In brick-and-mortar stores, customer service requires staff to instantly grasp customer emotions and respond appropriately based on those emotions. However, conventional systems make it difficult to perform emotion analysis on the spot, potentially leading to missed opportunities to improve customer satisfaction. To solve this problem, real-time emotion analysis and feedback functions for rapid responses are essential.

[0747] 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.

[0748] In this invention, the server includes means for receiving data from a user device, means for converting speech to text from the received data, and artificial intelligence means for extracting important information from the text. This makes it possible to analyze speech data in real time and provide feedback tailored to the customer's emotional state.

[0749] A "user device" is a device used to acquire data and send it to a server.

[0750] "Means of receiving data" refers to the function of receiving information provided by user devices within the server.

[0751] "Methods for converting speech to text" refer to the process of using speech recognition technology to convert speech data into a format that can be used as written information.

[0752] "Artificial intelligence methods" refer to technologies for extracting essential information from received data.

[0753] "Structured data format" refers to a standardized data format used to organize information and facilitate its later use and retrieval.

[0754] "Means of sending notifications" refers to a messaging function used to inform users of the analysis results.

[0755] "Methods for collecting feedback" refer to methods for compiling user opinions and reactions and using them to improve the system.

[0756] "Methods for analyzing emotions from audio data" refers to technologies for estimating a speaker's emotions based on their tone of voice and content.

[0757] "A means of providing real-time feedback tailored to the customer's emotional state" refers to a function that evaluates the customer's current emotions and immediately provides the most appropriate response.

[0758] This invention is a system for improving customer service in physical stores. First, the user interacts with the customer using a user device such as smart glasses. The audio data of this interaction is transmitted in real time from the user device to a server. The server uses a speech recognition engine to instantly convert this audio data into text. The speech recognition technology used is "Google Cloud Speech-to-Text".

[0759] The transcribed data is analyzed using natural language processing techniques to extract important information. For example, the names of products the customer is interested in and the suggested products are extracted. Simultaneously, a sentiment analysis engine (such as "IBM Watson Tone Analyzer") operates to analyze the customer's emotions from the tone and content of their voice.

[0760] The analysis results are notified in real time from the server to the user's smart glasses. This feedback allows the user to take immediate action based on the customer's emotional state. For example, if the customer is showing positive emotions, feedback is presented to the staff prompting them to "suggest related products."

[0761] For example, when a customer says, "This product has a great design," the server analyzes this positive response and notifies the user's device, "Next, please suggest additional products that would go well with this product."

[0762] This prompt text is generated by utilizing a generative AI model. A concrete example of a prompt text is, "Please enter the text of your conversation about the product." In this way, the present invention enhances customer interaction and improves service in physical stores.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The user device captures customer interactions as audio data and sends it to the server. The input is customer voice, and the output is digital audio data. The user device transmits this audio data to the server in real time.

[0766] Step 2:

[0767] The server converts the received audio data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). The input is digital audio data, and the output is text data. When the server converts the sound waveform into text, it uses the speech recognition engine's algorithm to perform accurate transcription.

[0768] Step 3:

[0769] The server extracts important information from text data using natural language processing techniques. The input is text data, and the output is structured data containing the extracted information. The server uses algorithms to identify user-friendly information, such as product names and suggested content.

[0770] Step 4:

[0771] The server analyzes emotions from text data using an emotion analysis engine (e.g., IBM Watson Tone Analyzer). The input is text data, and the output is emotion data indicating the emotional state. The server determines the speaker's emotions by analyzing the tone of the text and keywords related to emotions.

[0772] Step 5:

[0773] The server notifies the user's smart glasses of feedback in real time. Inputs are structured data and sentiment data, and output is feedback information provided to the user. The server sends information suggesting specific actions to the user via the notification system.

[0774] Step 6:

[0775] The user continues the conversation with the customer based on feedback received from the server. The input is feedback information from the server, and the output is suggestions and responses to the customer. The user decides on the next action and responds appropriately to the customer while looking at the display on their smart glasses.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] Means for receiving data from user equipment,

[0800] A means of converting audio to text from received data,

[0801] An artificial intelligence method for extracting important information from text,

[0802] A means of storing the extracted information in a structured data format,

[0803] A means of sending notifications to users,

[0804] A means of collecting feedback and updating artificial intelligence tools,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, comprising means for converting data into text using speech recognition technology.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the extracted information includes customer name, proposed content, and action item.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A means for receiving information from an information processing device,

[0813] A means of converting received information into text,

[0814] An intelligent processing method for extracting important content from text,

[0815] A means for saving the extracted content in a structured information format,

[0816] A means of sending notifications to users,

[0817] A means of collecting opinions and updating intelligent processing systems,

[0818] A method for attaching speaker tags to received audio data,

[0819] A means of retraining intelligent processing systems based on feedback,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, comprising means for converting information into text using speech recognition technology.

[0823] (Claim 3)

[0824] The system according to claim 1, wherein the extracted content includes customer identification information, proposed content, and action items.

[0825] "Application Example 1"

[0826] (Claim 1)

[0827] Means for receiving data from user equipment,

[0828] A means of converting audio to text from received data,

[0829] An artificial intelligence method for extracting important information from text,

[0830] A means of storing the extracted information in a structured data format,

[0831] A means of sending notifications to users,

[0832] A means of collecting feedback and updating artificial intelligence tools,

[0833] A means by which a machine or device autonomously adjusts its work procedures based on the information it receives,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for converting data into text using speech recognition technology.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein the extracted information includes identification information, suggested items, and work instructions.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] Means for receiving information from user equipment,

[0842] A means of converting audio into document from received information,

[0843] A processing method for extracting important information from documents,

[0844] A means for storing extracted information and recognized emotional information in a structured format,

[0845] A means of recognizing emotions from the user's way of speaking and the content of documents,

[0846] A means of sending notifications to users,

[0847] A means of collecting feedback and updating the learning model,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising means for documenting information using speech recognition technology.

[0851] (Claim 3)

[0852] The system according to claim 1, wherein the extracted information includes personal names, proposal details, and business items.

[0853] "Application example 2 when combining with an emotional engine"

[0854] (Claim 1)

[0855] Means for receiving data from user equipment,

[0856] A means of converting audio to text from received data,

[0857] An artificial intelligence method for extracting important information from text,

[0858] A means of storing the extracted information in a structured data format,

[0859] A means of sending notifications to users,

[0860] A means of collecting feedback and updating artificial intelligence tools,

[0861] Methods for analyzing emotions from audio data,

[0862] A means of providing real-time feedback tailored to the customer's emotional state,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, comprising means for converting data into text using speech recognition technology.

[0866] (Claim 3)

[0867] The system according to claim 1, wherein the extracted information includes customer identification information, proposed content, and action plan. [Explanation of Symbols]

[0868] 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. Means for receiving data from user equipment, A means of converting audio to text from received data, An artificial intelligence method for extracting important information from text, A means of storing the extracted information in a structured data format, A means of sending notifications to users, A means of collecting feedback and updating artificial intelligence tools, A system that includes this.

2. The system according to claim 1, comprising means for converting data into text using speech recognition technology.

3. The system according to claim 1, wherein the extracted information includes customer name, proposed content, and action item.

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