system

Abstract

We provide the system. [Solution] A sound recording means for recording sounds in an environment such as a classroom, A means for transmitting recorded audio data to a server, A voice analysis means on a server that analyzes voice data and converts it into text data, A report generation method that automatically generates reports based on text data, A report editing method that sends the generated report to the terminal in a format that the user can edit, A report transmission means for resending the edited report to the server, A notification mechanism to send the edited final report to parents and relevant users, 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In educational institutions, the communication between teachers and parents plays an important role, but the work for this imposes a great burden on teachers. In particular, it takes time and effort to create a detailed report on sudden events such as fights among children and explain it to parents. In order to reduce the burden on teachers regarding such communication tasks, an efficient system is required. Furthermore, it is necessary to realize more rapid and accurate information sharing by automating the reporting tasks.

Means for Solving the Problems

[0005] The present invention solves the above-mentioned problems with a system that includes: an audio recording means for recording audio in an environment such as a classroom; an audio data transmission means for transmitting the recorded audio data to a server; an audio analysis means for analyzing the audio data on the server and converting it into text data; a report generation means for automatically generating a report based on the text data; a report editing means for transmitting the generated report to a terminal in a user-editable format; a report transmission means for retransmitting the edited report to the server; and a notification means for transmitting the final edited report to parents and relevant users. This system enables automatic analysis of audio data and report generation, allowing information to be provided quickly and accurately to parents and other teachers. This significantly reduces the burden on teachers.

[0006] "Voice recording means" refers to a device or function for recording sound in an environment such as a classroom.

[0007] "Voice data transmission means" refers to a device or function for transmitting recorded voice data to an external device such as a server.

[0008] "Speech analysis means" refers to a device or function that analyzes speech data received by a server and converts it into text data.

[0009] "Report generation means" refers to a device or function for automatically generating reports based on text data.

[0010] "Report editing means" refers to a device or function for sending a generated report to a terminal in a format that can be edited by the user.

[0011] "Report transmission means" refers to a device or function for resending a user-edited report to the server.

[0012] "Notification means" refers to a device or function for notifying parents and relevant users of the edited final report.

[0013] A "server" is a central control unit that receives and analyzes voice data, and generates and manages reports.

[0014] A "terminal" refers to a device used by teachers or parents that is equipped with means for recording audio and editing reports.

[0015] A "user" is someone such as a teacher who operates the system or a parent who receives reports. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[0038] System Examples

[0039] 1. Recording and transmitting audio data

[0040] Device (Teacher's device)

[0041] Teachers record classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[0042] Specific example:

[0043] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[0044] 2. Analysis and transcription of audio data

[0045] server

[0046] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. A machine learning model is used for speech recognition, applying a model that is strong in everyday conversation and specific terminology. The server then analyzes this audio data and converts it back into text data.

[0047] Specific example:

[0048] When the server receives an audio file, it automatically starts the speech recognition engine. The engine analyzes the audio and generates the corresponding text. For example, the audio "The fight that just happened was between A and B" is transcribed as "The fight that just happened was between A and B."

[0049] 3. Report generation and editing

[0050] server

[0051] The system automatically generates a report by applying text data to a report template. The generated report is then sent to the teacher's device.

[0052] User (Teacher)

[0053] Teachers can review the report and edit the necessary parts. For example, they can add specific details about the situation or additional information. The edited report is then sent back to the server.

[0054] Specific example:

[0055] The server embeds text data into a report template and generates a report stating, "Today, XX and YY had a fight. The cause was XX and YY." This report is sent to the teacher's terminal, where the teacher can add additional comments.

[0056] 4. Sending reports and notifications to parents / guardians

[0057] server

[0058] The edited final report is sent to the parents. The report sent will include a push notification so that parents are immediately notified when a new report is received.

[0059] Specific example:

[0060] The server sends the edited report to the parent's app and simultaneously sends a push notification. When the parent opens the app, the report will appear in the "Today's Announcements" section.

[0061] 5. Sharing among teachers

[0062] server

[0063] Share the report with other relevant teachers as needed. For example, send the report to the homeroom teacher, school nurse, grade level head, etc., to facilitate information sharing among teachers.

[0064] Specific example:

[0065] If the server is configured to share the same report with other relevant faculty members, the report will be automatically sent to each of their devices.

[0066] The above is a specific embodiment of the present invention, a system that streamlines communication between teachers and parents and reduces the burden on both parties by seamlessly performing a series of steps from recording audio data to automatically generating, editing, and sending reports.

[0067] The following describes the processing flow.

[0068] Step 1:

[0069] User (Teacher)

[0070] The teacher launches the dedicated app and selects the recording function. Tapping the record button prompts the app to request access to the microphone, and recording begins. The teacher records the necessary scenes and then taps the end-of-recording button.

[0071] Step 2:

[0072] Device (Teacher's device)

[0073] Once recording is complete, the audio data is temporarily stored on the device. The stored audio data is then automatically uploaded to a server via the internet.

[0074] Step 3:

[0075] server

[0076] The server receives the audio data. Upon receiving the audio data, the speech recognition engine within the server starts up and converts the audio data into text data. The speech recognition technology employs a machine learning model to analyze and convert the data with high accuracy.

[0077] Step 4:

[0078] server

[0079] Based on the converted text data, the automated report generation module generates a report. The text data is embedded in a pre-configured report template, and the initial report is created.

[0080] Step 5:

[0081] server

[0082] The initial generated report is sent to the teacher's device. A notification function is used to inform the teacher so they can receive and review the report.

[0083] Step 6:

[0084] User (Teacher)

[0085] The teacher reviews the report and edits it as needed. For example, they might add specific details or additional information. Once editing is complete, the teacher saves the edited report and resubmits it to the server.

[0086] Step 7:

[0087] Device (Teacher's device)

[0088] Upload the edited report data to the server.

[0089] Step 8:

[0090] server

[0091] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[0092] Step 9:

[0093] User (Parent / Guardian)

[0094] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[0095] Step 10:

[0096] server

[0097] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[0098] (Example 1)

[0099] 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."

[0100] Traditional methods for recording classroom events and creating reports involved a great deal of manual work, placing a significant burden on both teachers and parents. Furthermore, manual recording and report creation were time-consuming, and the accuracy of the information could not always be guaranteed. Moreover, in today's world, where efficiency and speed in reporting are crucial, existing systems have failed to adequately meet these needs. Therefore, this invention provides a system for automatically analyzing audio data and automatically generating reports to solve these problems.

[0101] 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.

[0102] In this invention, the server includes a speech recognition engine control means that, after receiving audio data, activates a speech recognition engine and performs a process of converting the audio data into text data; an automatic report generation means that applies the text data based on the audio data to a report template; and a report sharing means that shares the generated report with other faculty and staff. This eliminates the need to manually convert audio data to text, enabling the rapid and accurate generation and sharing of reports.

[0103] "Audio recording means" refers to devices or functions for recording audio in real time in environments such as classrooms.

[0104] "Voice data transmission means" refers to a function for transmitting voice data obtained through recording to a server via the internet.

[0105] "Speech analysis means" refers to a function that performs the process of analyzing the received speech data on the server and converting it into text data.

[0106] The "report generation method" is a function that automatically generates a report by applying the analyzed text data to a report template.

[0107] The "report editing means" is a function that sends the generated report to the terminal in a format that the user can edit.

[0108] The "report transmission method" refers to the function of resending the edited report to the server.

[0109] "Notification method" refers to a function that sends the edited final report to parents and relevant users and notifies them of its receipt.

[0110] The "speech recognition engine control means" is a function that activates the speech recognition engine after receiving speech data and executes the process of converting the speech data into text data.

[0111] The "automatic report generation method" is a function that automatically generates a report by applying text data to a report template based on audio data.

[0112] "Report sharing means" refers to a function for sharing generated reports with other faculty and staff members.

[0113] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The aim of this system is to streamline communication between teachers and parents and reduce the burden on both parties.

[0114] Hardware and software to be used

[0115] Device (Teacher's device)

[0116] This system uses devices such as smartphones and tablets as terminals. These devices have dedicated apps installed that provide functions such as recording, saving, and transmitting audio.

[0117] server

[0118] Many processes, such as receiving, analyzing, storing, generating reports on, and sharing audio data, are performed by the server. The server has high processing power and runs multiple software components, including a speech recognition engine and a database.

[0119] Speech recognition engine

[0120] To convert audio data into text data, a machine learning-based speech recognition engine (e.g., Google® Cloud Speech-to-Text API) is used.

[0121] Report generation template

[0122] When generating reports based on text data, predefined templates are used. This makes it easy to create reports in a consistent format.

[0123] Data processing and calculation

[0124] Voice recording

[0125] The user (teacher) operates the audio recording device through a dedicated app to record the sound in the classroom. When recording begins, the device's microphone activates, recording ambient sounds in real time.

[0126] Storing and transmitting audio data

[0127] When the teacher's device finishes recording audio, the audio data is temporarily stored in local storage and then uploaded to the server via the internet.

[0128] Start the speech recognition engine

[0129] When the server receives audio data, it uses the speech recognition engine control means to activate the speech recognition engine and convert the audio data into text data.

[0130] Automatic report generation

[0131] Based on the converted text data, the automated report generation system embeds the data into a template and automatically generates the report.

[0132] Sharing of reports

[0133] The server not only sends the generated reports to the teacher's device but also shares them with other faculty members' devices as needed.

[0134] Specific example

[0135] Specific examples of audio data recording:

[0136] When the teacher taps the record button in the app, the device activates the microphone to record the classroom environment and the children's conversations.

[0137] Specific examples of sending audio data:

[0138] After recording is complete, the device uploads the audio data to a server via the internet. An HTTP POST request is used for the upload.

[0139] Specific examples of audio data analysis and transcription:

[0140] When the server receives an audio file, it automatically starts the speech recognition engine. For example, the audio "The fight that just happened was between A and B" is converted to text "The fight that just happened was between A and B."

[0141] Example of a prompt

[0142] Example of a prompt:

[0143] "I want to create an app that records events that occur in the classroom and saves them as audio data. Please explain what kind of mechanism is needed to implement a system that sends the recorded audio data to a server and converts the audio data into text. Furthermore, please explain in detail the process of automatically generating, editing, and sending the generated text data as a report."

[0144] The system described above allows for a seamless workflow from recording audio data to automatically generating, editing, and sending reports, thereby streamlining communication between teachers and parents.

[0145] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0146] Step 1:

[0147] The user taps the record button in the dedicated app.

[0148] Input: User tap operation

[0149] Operation: When the user taps the app's record button, the device's microphone is activated, and ambient sounds are recorded in real time. At the time recording begins, metadata (start time, location information, etc.) is also recorded.

[0150] Output: Audio files and metadata stored on the device

[0151] Step 2:

[0152] After recording is complete, the device temporarily saves the audio data to local storage.

[0153] Input: Action to end recording

[0154] Operation: When recording ends, the audio data is saved to the device's local storage. Important metadata (start time, end time, device ID, etc.) is also saved at this time.

[0155] Output: Audio files and metadata saved to local storage

[0156] Step 3:

[0157] The device uploads the stored audio data to the server via the internet.

[0158] Input: Audio files and metadata stored in local storage

[0159] Operation: The terminal sends audio data to the server using an HTTP POST request. In this process, a request containing the audio file and associated metadata is created.

[0160] Output: Audio files and metadata sent to the server

[0161] Step 4:

[0162] The server receives the audio data and starts the speech recognition engine.

[0163] Input: Audio files and metadata sent to the server

[0164] Operation: When the server receives an HTTP POST request, it saves the audio file to a temporary directory and starts a speech recognition engine (e.g., Google Cloud Speech-to-Text API).

[0165] Output: Speech recognition engine is operational.

[0166] Step 5:

[0167] The server uses a speech recognition engine to convert the audio data into text data.

[0168] Input: Audio files saved in a temporary directory

[0169] Operation: The speech recognition engine analyzes the audio data and generates corresponding text data. In this process, specific keywords and phrases are converted into text data.

[0170] Output: Generated text data

[0171] Step 6:

[0172] The server applies the generated text data to the report template to automatically generate the report.

[0173] Input: Generated text data

[0174] Operation: The server automatically generates a report by embedding text data into a predefined report template. Important information (date, time, location, stakeholders, etc.) is included in the report.

[0175] Output: Automated report

[0176] Step 7:

[0177] The server sends the generated report to the teacher's terminal.

[0178] Input: Automated report

[0179] Operation: The server uses email and push notifications to send the generated report to the teacher's device. Along with the report, an editing link and an in-app notification are sent.

[0180] Output: Report sent to the teacher's terminal

[0181] Step 8:

[0182] The user (teacher) reviews the report and edits it as needed.

[0183] Input: Report sent to the teacher's terminal

[0184] Operation: The user opens the report in the app and reviews its contents. They can add comments or make edits as needed. The edited report is then saved.

[0185] Output: Edited report

[0186] Step 9:

[0187] The terminal resends the edited report to the server.

[0188] Input: Edited report

[0189] Operation: Once editing is complete, the report is sent back to the server using an HTTP POST request.

[0190] Output: Edited report sent to the server

[0191] Step 10:

[0192] The server sends the edited final report to the parents and sends a push notification.

[0193] Input: Edited report sent to the server

[0194] Operation: The server sends the final report to the parent's device and simultaneously sends a push notification. This immediately notifies the parent that a new report has been received.

[0195] Output: Final report and push notification sent to the parent's device.

[0196] Step 11:

[0197] The server shares the generated report with other faculty and staff members.

[0198] Input: Automated or edited report

[0199] Operation: The server also sends the report to other relevant faculty members (e.g., homeroom teacher, school nurse, grade level head). This transmission also uses HTTP POST requests or email.

[0200] Output: Report sent to other faculty members' terminals

[0201] (Application Example 1)

[0202] 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."

[0203] In current classroom and retail environments, recording audio data and creating reports based on it is time-consuming and labor-intensive. Furthermore, the process of sharing these reports among stakeholders is cumbersome, making it particularly difficult to quickly share troubleshooting information. This increases the burden on teachers and technicians, hindering efficient information dissemination.

[0204] 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.

[0205] In this invention, the server includes: an audio recording means for recording audio in an environment such as a classroom; an audio data transmission means for transmitting the recorded audio data to the server; an audio analysis means for analyzing the audio data on the server and converting it into text data; a report generation means for automatically generating a report based on the text data; a report editing means for transmitting the generated report to a terminal in a user-editable format; a report transmission means for retransmitting the edited report to the server; a notification means for sending the final edited report to parents and relevant users; a technician support means for recording audio data in a physical store and generating a troubleshooting report based on it; and a technician information sharing means for sharing the generated report with relevant technicians. This enables efficient report creation and information sharing from audio data.

[0206] "Audio recording means" refers to devices or software used to record sounds that occur in environments such as classrooms or physical stores.

[0207] "Voice data transmission means" refers to a means for transmitting recorded voice data to a server via a network.

[0208] "Voice analysis means" refers to a technology that analyzes voice data sent to a server and converts it into text data.

[0209] The "report generation means" is a means of automatically creating a report using text data generated by the speech analysis means.

[0210] "Report editing means" refers to a means for sending the generated report to a terminal in a format that can be edited by the user.

[0211] "Report transmission means" refers to the means by which a user can resend an edited report to the server.

[0212] "Notification method" refers to the means of sending the edited final report to parents and relevant users for notification.

[0213] "Technical support methods" refer to methods for recording voice data in physical stores and generating troubleshooting reports based on that data.

[0214] A "means for sharing engineer information" refers to a method for sharing generated reports with relevant engineers.

[0215] This invention is a system that records audio in environments such as classrooms and physical stores, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The main components include audio recording means, audio data transmission means, audio analysis means, report generation means, report editing means, report transmission means, notification means, engineer support means, and engineer information sharing means.

[0216] This system uses the following hardware and software:

[0217] Smartphones and smart glasses are used as means of voice recording. This allows users to easily record conversations and ambient sounds.

[0218] As a means of speech analysis, the Google Web Speech API is used to convert speech data into text data.

[0219] A web application using the Django framework will be used as the means for generating and editing reports.

[0220] As a means of supporting engineers and sharing engineer information, the system enables the automatic generation of troubleshooting reports based on audio data and their sharing with relevant engineers.

[0221] Voice recording and transmission

[0222] Users (classroom teachers or store technicians) record audio through a dedicated app installed on their smartphones or smart glasses. When recording begins, the device's microphone activates and records ambient sounds. Once recording is complete, the audio data is temporarily stored on the device and then transmitted to a server via the internet.

[0223] Audio data analysis and transcription

[0224] When the server receives audio data, the speech recognition engine (Google Web Speech API) is activated and converts the audio data into text data. The converted text is then used for subsequent processing.

[0225] Report generation and editing

[0226] Based on the generated text data, the server automatically generates a report. A report template is used to allow users to easily fill in specific information. The generated report is sent to the user's terminal, where the user can enter additional information and edit the report as needed.

[0227] Sending reports to parents and relevant technicians

[0228] The edited final report is resent to the server. The server sends the final report to the parent or relevant technician and uses a notification system to inform them that a new report has been received. Furthermore, a technician information sharing system allows the report to be shared among relevant technicians, enabling rapid troubleshooting.

[0229] Specific example

[0230] For example, in a classroom, a recording might be made stating, "Today, Mr. Tanaka taught a math class. During the class, two students got into a fight." This recording is then converted into text data, generating a report like this: "Today, during Mr. Tanaka's math class, students A and B got into a fight. The cause was a dispute over seats."

[0231] Example of a prompt

[0232] Examples of prompts to input into a generative AI model are as follows:

[0233] "Based on this week's technical support report, please list the main troubleshooting issues in bullet points."

[0234] As a result, this system enables efficient report creation and information sharing from audio data, significantly reducing the burden on classrooms and physical stores.

[0235] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0236] Step 1:

[0237] The user (teacher or technician) launches a dedicated app installed on their smartphone or smart glasses and taps the record button. The input is voice, which is collected through the device's microphone. The recorded voice data is temporarily stored on the device.

[0238] Step 2:

[0239] The device transmits the recorded audio data to the server via the internet. The input is the recorded audio data, and the output is the audio data uploaded to the server.

[0240] Step 3:

[0241] The server analyzes the received audio data using speech analysis tools. Specifically, it uses the Google Web Speech API to convert the audio data into text data. The input is audio data, and the output is the analyzed text data.

[0242] Step 4:

[0243] The server automatically generates a report using a report generation device based on the text data obtained by the speech analysis device. The input is text data, and the output is an automatically generated report.

[0244] Step 5:

[0245] The server sends the generated report to the terminal, and the user reviews and edits the report through a dedicated app. The input is an automatically generated report, and the output is the edited report. The user can enter additional information and edit the report as needed.

[0246] Step 6:

[0247] The terminal resends the edited report to the server. The input is the edited report, and the output is the edited report resent to the server.

[0248] Step 7:

[0249] The server sends the edited final report to parents and relevant technicians and uses notification methods to inform them that the new report has been delivered. The input is the edited final report, and the output is the notification and report received by parents and relevant technicians.

[0250] Step 8:

[0251] The server uses a technical information sharing system to share the generated report with other relevant technical personnel. The input is the final report, and the output is the report sent to the relevant technical personnel's terminals.

[0252] Each step aims to efficiently generate and share reports automatically from audio data, thereby reducing the burden on users.

[0253] 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.

[0254] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by incorporating an emotion engine, this system also has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[0255] System Examples

[0256] 1. Recording and transmitting audio data

[0257] User (Teacher)

[0258] The teacher records classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[0259] Specific example:

[0260] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[0261] 2. Analysis and transcription of audio data

[0262] server

[0263] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. The speech recognition technology uses machine learning models to analyze and convert the data with high accuracy. The server then analyzes this audio data and converts it back into text data.

[0264] Emotional Engine

[0265] The server inputs the received audio data into the emotion engine, which recognizes the user's emotions from the voice. The emotion engine identifies emotions by analyzing voice tone, word choice, and other acoustic characteristics. The emotion data is added to the text data and used to create reports.

[0266] Specific example:

[0267] When the server receives an audio file, it automatically activates the speech recognition engine and emotion engine. The engines analyze the audio and generate corresponding text, while the emotion engine identifies emotions such as "anger" and "sadness" and adds emotion data to the text. For example, "The fight that just happened was between A and B" is converted to "The fight that just happened was between A and B (anger)."

[0268] 3. Report generation and editing

[0269] server

[0270] Based on the converted text data and sentiment data, the automated report generation module generates a report. The text data and sentiment data are embedded in a pre-configured report template, and the initial report is created.

[0271] User (Teacher)

[0272] Teachers can review the report and edit the necessary sections. Sentiment data is also reflected in the report, and they can enter specific situational descriptions and additional information. Once editing is complete, teachers save the edited report and resubmit it to the server.

[0273] Specific example:

[0274] The server embeds text data and sentiment data into the report template and generates a report stating, "Today, XX and XX had a fight (angry). The reason is XX and XX." This report is sent to the teacher's terminal, and the teacher fills in additional comments.

[0275] 4. Sending the report to the guardians and notification

[0276] Server

[0277] The edited final report is sent to the guardians. The report to be sent is accompanied by a push notification, and the guardians can immediately know that they have received a new report.

[0278] Specific example:

[0279] When the server sends the edited report to the guardian's app and at the same time sends a push notification. When the guardian opens the app, the report is displayed in the "Today's Notification" section, and sentiment information such as "Today, XX seemed to be suffering from sadness" is also included.

[0280] 5. Sharing among teachers

[0281] Server

[0282] The report is also shared with other relevant teachers as needed. For example, the report is sent to the class teacher, school nurse, grade head, etc. to achieve information sharing among teachers.

[0283] Specific example:

[0284] If the server is set to share the same report with other relevant teachers, the report will be automatically sent to each terminal.

[0285] The above are specific embodiments of the present invention. By seamlessly performing a series of processes from the recording of voice data to the automatic generation, editing, and transmission of reports, it is a system that improves the communication between teachers and guardians and reduces the burden on both sides. Additionally, by combining an emotion engine, it is possible to more precisely grasp the emotions of users and provide appropriate feedback.

[0286] The following will explain the process flow.

[0287] Step 1:

[0288] User (teacher)

[0289] The teacher launches the dedicated app and selects the recording function. When the recording button is tapped, the app requests access to the microphone and recording starts. The teacher records the necessary scenes and taps the recording stop button.

[0290] Step 2:

[0291] Terminal (teacher's device)

[0292] When recording ends, the voice data is temporarily saved in the terminal. The saved voice data is automatically uploaded to the server via the Internet.

[0293] Step 3:

[0294] Server

[0295] The server receives the voice data and activates the speech recognition engine. The voice data is converted into text data. As a result, the voice recording is output as a text.

[0296] Step 4:

[0297] Server

[0298] The server also inputs the received voice data into the emotion engine to recognize the user's emotion from the voice. The emotion engine analyzes the tone of voice, diction, and other acoustic characteristics to identify the emotion and adds the data to the text data.

[0299] Step 5:

[0300] Server

[0301] Based on the converted text data and emotion data, the automatic report generation module generates a report. The character data and emotion data are embedded in a pre-set report template to create an initial report.

[0302] Step 6:

[0303] Server

[0304] The generated initial report is sent to the teacher's terminal. A notification function is used to inform the teacher so that they can receive and check the report.

[0305] Step 7:

[0306] User (teacher)

[0307] The teacher checks the report and edits the necessary parts. Since the emotion data is also reflected in the report, the teacher inputs specific situation descriptions and additional information. After the editing is completed, the teacher saves the edited report and sends it back to the server.

[0308] Step 8:

[0309] Terminal (teacher's device)

[0310] Upload the edited report data to the server.

[0311] Step 9:

[0312] Server

[0313] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[0314] Step 10:

[0315] User (Parent / Guardian)

[0316] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[0317] Step 11:

[0318] server

[0319] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[0320] (Example 2)

[0321] 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".

[0322] Traditionally, recording and analyzing audio data in classrooms and other environments was often done manually, requiring considerable time and effort. Furthermore, report creation was primarily manual, leading to problems with accuracy and consistency. Additionally, there was a lack of technology to identify emotions from audio data and reflect them in reports, highlighting the need for more efficient communication between teachers and parents.

[0323] 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.

[0324] In this invention, the server includes a voice analysis means for analyzing voice data and converting it into text data, an emotion recognition means for extracting emotion data from the text data converted by the voice analysis means, and a report generation means for automatically generating a report based on the text data and emotion data. This allows the user to automatically perform the entire process from recording voice data to generating, editing, and sending reports, significantly improving the efficiency of communication between teachers and parents.

[0325] "Sound recording means" refers to a device or system for recording sounds within an environment.

[0326] "Audio data storage means" refers to a device or system for temporarily storing recorded audio data.

[0327] "Voice data transmission means" refers to a device or system for transmitting stored voice data to a server.

[0328] "Speech analysis means" refers to a device or system for analyzing received speech data and converting it into text data.

[0329] "Emotion recognition means" refers to a device or system for extracting a user's emotions from text data converted by speech analysis means.

[0330] A "report generation means" is a device or system for automatically generating reports based on text data and sentiment data.

[0331] "Report editing means" refers to a device or system for sending a generated report to a terminal in a format that can be edited by the user.

[0332] "Report transmission means" refers to a device or system for resending an edited report to a server.

[0333] "Notification means" refers to a device or system for sending the edited final report to parents and relevant users and notifying them of this fact.

[0334] "Information sharing means" refers to a device or system for sharing generated reports among other relevant users.

[0335] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by combining this system with an emotion recognition engine, it has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[0336] Hardware and software

[0337] User (Teacher)

[0338] Teachers use a dedicated application (e.g., a custom app for iOS or Android®) to record audio in the classroom. Recording is done using the microphone on a smartphone or tablet.

[0339] terminal

[0340] The device temporarily stores the recorded data in its internal storage and uploads the data to the server via an internet connection.

[0341] server

[0342] The server analyzes speech data using speech analysis and emotion recognition technologies such as Google Cloud Speech-to-Text API and IBM Watson® Tone Analyzer, and converts it into text data. Based on the converted text data and emotion data, it automatically generates a report using a text generation library written in Python and sends it to the user.

[0343] Specific explanation of system operation

[0344] 1. Recording audio data

[0345] The user (teacher) launches the dedicated app and taps the record button. This activates the device's microphone and records the audio in the classroom.

[0346] Specific example: When the user taps the "Start Recording" button in the app, the device turns on the microphone and begins recording audio data.

[0347] 2. Temporary storage and transmission of recorded data to the server

[0348] Once recording is complete, the device temporarily saves the audio data to its internal storage. It then uploads the audio data to the server via Wi-Fi or mobile data.

[0349] Specific example: After recording is complete, the device saves the audio file to its internal storage and displays a notification to the user stating, "Recording saved." The saved audio data is then uploaded to the server.

[0350] 3. Analysis and transcription of audio data

[0351] The server converts the received audio data into text data using the Google Cloud Speech-to-Text API.

[0352] Specific example: When the server receives an audio file, it automatically starts the speech recognition engine and converts the audio to text.

[0353] 4. Extraction of emotional data

[0354] The server uses IBM Watson Tone Analyzer to extract sentiment data from the converted text data and adds sentiment labels to the text data.

[0355] Specific example: An emotion recognition engine identifies emotions such as "anger" and "sadness," and adds emotion labels to the text data, such as "The argument earlier was between A and B (anger)."

[0356] 5. Automatic generation and user editing of reports

[0357] The server generates an initial report using a pre-configured report template based on text and sentiment data. The generated report is sent to the user's (teacher's) terminal and can be edited as needed. Once editing is complete, the teacher sends the edited report back to the server.

[0358] Specific example: The server embeds text and sentiment data into a report template and sends the report to the teacher's terminal. The teacher adds necessary comments, saves the edited report, and then resends it to the server.

[0359] 6. Sending reports to parents and relevant users.

[0360] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[0361] Specific example: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements."

[0362] Example of a prompt

[0363] 1. "Please record the audio from the classroom and send it to the server."

[0364] 2. "Convert the audio file to text and extract the sentiment data."

[0365] 3. "Generate a report based on the extracted text data and sentiment data."

[0366] 4. Edit the generated report and send it to the parents.

[0367] The above describes an embodiment of the present invention, which enables a seamless workflow from recording audio data to generating, editing, and transmitting reports. This workflow streamlines communication between teachers and parents, reducing the burden on both parties. Furthermore, by combining this with emotion recognition technology, it is possible to reflect the user's emotional information in the report, providing more detailed and useful information.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] Audio data recording

[0371] When the user (teacher) launches the dedicated app and taps the record button, the device's microphone activates and records the audio in the classroom.

[0372] Input: User's tap of the recording button

[0373] Data processing: The device's microphone picks up ambient sounds and generates audio data.

[0374] Output: Audio data (e.g., .wav format)

[0375] Specific operation: Recording begins when the user taps the "Start Recording" button in the app, and the recording time is displayed on the app screen.

[0376] Step 2:

[0377] Temporary storage of recording data

[0378] Once recording is complete, the device temporarily saves the recorded audio data to its internal storage.

[0379] Input: Recording termination operation (user taps the record button)

[0380] Data processing: The generated audio data is saved to the device's internal storage.

[0381] Output: Temporarily saved audio data

[0382] Specific action: Tapping the record button again will end the recording and a pop-up notification will appear stating, "Recording saved."

[0383] Step 3:

[0384] Sending audio data to the server

[0385] The device uploads the stored audio data to the server via an internet connection.

[0386] Input: Temporarily stored audio data

[0387] Data processing: Upload audio data to the server.

[0388] Output: Audio data sent to the server

[0389] Specific operation: The device uploads the audio file to the server using Wi-Fi or mobile data, and the progress is displayed with a progress bar. Once complete, a "Sending Complete" message is displayed.

[0390] Step 4:

[0391] Analysis of audio data

[0392] The server automatically launches a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to analyze the received audio data.

[0393] Input: Audio data sent to the server

[0394] Data processing: Analyze audio data and convert it into text data.

[0395] Output: Text data

[0396] Specific operation: The server receives the audio file and calls the Google Cloud Speech-to-Text API to begin analyzing the audio data. The processing status is displayed on the dashboard.

[0397] Step 5:

[0398] Convert to text data

[0399] The server converts the analyzed audio data into text data. The converted text data is stored in a database on the server.

[0400] Input: Audio data analyzed by a speech recognition engine

[0401] Data manipulation: Converting audio data to text.

[0402] Output: Text data (e.g., .txt format)

[0403] Specific operation: The server converts audio files into text data and saves that data to a database. A preview of the conversion results can be viewed on the dashboard.

[0404] Step 6:

[0405] Extraction of emotional data

[0406] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to extract the user's emotions based on the text data and adds emotion labels to the text data.

[0407] Input: Text data

[0408] Data processing: Identify emotions from text data and add emotion labels.

[0409] Output: Text data with emotion labels attached.

[0410] Specific operation: The server inputs text data into the emotion recognition engine and generates text data with emotion labels such as "anger" and "sadness" attached.

[0411] Step 7:

[0412] Automatic report generation

[0413] The server automatically generates a report based on text data and sentiment data. The generated report is sent to the user's (teacher's) terminal.

[0414] Input: Text data with emotion labels attached

[0415] Data processing: Generate reports using pre-configured report templates.

[0416] Output: Initial Report

[0417] Specific operation: The server uses a template to generate a report such as, "Today, XX and YY had a fight (anger). The cause was XX and YY," and sends it to the user's terminal.

[0418] Step 8:

[0419] Editing the report

[0420] The user (teacher) reviews the report using a dedicated app and edits the necessary parts. Once editing is complete, they resubmit the report to the server.

[0421] Input: Initial report

[0422] Data processing: Users edit reports and resend them to the server.

[0423] Output: Edited report

[0424] Specific actions: The user reviews the report, adds an "additional comment" to the input field, saves the edits, and then resends the report to the server.

[0425] Step 9:

[0426] Sending reports to parents and related users

[0427] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[0428] Input: Edited report

[0429] Data processing: Send the report to parents and relevant teachers.

[0430] Output: Sent reports and push notifications

[0431] Specific operation: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements".

[0432] Step 10:

[0433] Sharing among teachers

[0434] The server will also share the report with other relevant teachers (e.g., homeroom teachers, school nurses, grade level heads) as needed.

[0435] Input: Report data

[0436] Data processing: Automatically send reports to relevant faculty members.

[0437] Output: Shared report

[0438] Specific operation: The server sends the report to the homeroom teacher and school nurse, and sends a notification to each teacher's terminal stating, "A new report has been shared."

[0439] (Application Example 2)

[0440] 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".

[0441] In recent years, there has been a growing demand for improved education and customer service in environments such as classrooms and physical stores by utilizing voice recording and emotion recognition technologies. However, existing systems suffer from insufficient accuracy in transcribing voice data and recognizing emotions, and also lack the functionality to automatically generate reports using this data effectively. As a result, it is difficult for teachers and store managers to quickly and accurately grasp the necessary information, hindering improvements in operational efficiency.

[0442] 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. In this invention, the server includes a voice analysis means that analyzes voice data and converts it into text data, an emotion recognition means that recognizes emotions and generates emotion data, and a report generation means that automatically generates a report based on the text data and emotion data. This makes it possible to obtain highly accurate text data and emotion data from voice data and to automatically generate a report based on them.

[0443] "Voice recording means" refers to a device or system for recording sound in an environment such as a classroom or a physical store.

[0444] "Voice data transmission means" refers to a device or system for transmitting recorded voice data to a server.

[0445] "Voice analysis means" refers to a device or system that analyzes voice data received by a server and converts that voice data into text data.

[0446] "Emotion recognition means" refers to a device or system that recognizes a user's emotions from voice data and generates emotion data.

[0447] "Report generation means" refers to a device or system that automatically generates a report based on acquired text data and sentiment data.

[0448] "Report editing means" refers to a device or system that sends the generated report to a terminal in a format that can be edited by the user.

[0449] "Report transmission means" refers to a device or system for resending reports edited by users to a server.

[0450] "Notification means" refers to a device or system that transmits the edited final report to parents and relevant users.

[0451] "Text data" refers to data in text format obtained as a result of analyzing and converting audio data.

[0452] "Emotional data" refers to data that represents the user's emotions as recognized from voice data.

[0453] Modes for carrying out the invention

[0454] A system for carrying out this invention includes the following components:

[0455] 1. Audio recording means:

[0456] Users (teachers and store operators) record ambient sounds in classrooms and physical stores using smartphones or dedicated devices. For example, when a teacher wants to record students' comments or questions during class, they launch a dedicated application and tap the record button to start audio recording.

[0457] 2. Means for transmitting audio data:

[0458] Once recording is complete, the audio data is temporarily stored on the device and then sent to the server. This transmission requires an internet connection. For example, if you tap the "Send" button after recording is finished, the audio file will be automatically uploaded to the server.

[0459] 3. Voice analysis means:

[0460] The server analyzes the received audio data using speech recognition technology and converts it into text data. Machine learning models such as the Google Speech-to-Text API are used for this process. The analyzed text data then proceeds to the next processing step.

[0461] 4. Emotion recognition means:

[0462] On the server, emotion data is generated using emotion recognition technology based on text data. This is done by analyzing the tone of voice and the content of the speaker's speech. For example, if the word "thank you" is included, it will be recognized as "happy".

[0463] 5. Report generation means:

[0464] Using the analyzed text and sentiment data, a report is automatically generated. This report is created based on a pre-configured report template. For example, the report might be in the format, "Today, [Name] is experiencing sadness."

[0465] 6. Report editing methods:

[0466] The generated report is sent to the user's device in an editable format. The user can review the report and enter any necessary corrections or additional information. The edited report is then sent back to the server.

[0467] 7. Method of sending the report:

[0468] The server sends the edited report to parents and other relevant parties. A push notification feature is used to inform recipients that a new report has arrived. For example, tapping the "Notify Parents" button sends the report to the parent's app and displays a push notification.

[0469] 8. Means of notification:

[0470] Once the report has been submitted, the system sends a notification to the recipient using a notification method. For example, to make it easier for parents to check the report, they may be notified via push notification on their smartphone that a new report has arrived.

[0471] Hardware and software to be used

[0472] Hardware:

[0473] Smartphone, dedicated recording terminal, server

[0474] Software / Tools:

[0475] Voice recording application

[0476] Internet connection

[0477] Google Speech-to-Text API (speech recognition technology using machine learning models)

[0478] Emotion recognition engine (proprietary software)

[0479] Report generation and editing software

[0480] User application with push notification functionality

[0481] Examples of specific cases and prompt statements

[0482] For example, consider a scenario in a store where staff explain products to customers and express their gratitude. The system automatically analyzes this audio data and generates a report containing sentences like the following:

[0483] "This product is very popular. Let me explain how to use it... Thank you!"

[0484] Example of a report generated based on this:

[0485] Customer Feedback:

[0486] This product is very popular. Let me explain how to use it. ...Thank you!

[0487] Emotion: happy”

[0488] Example of a prompt

[0489] "Convert the following audio data to text and analyze the customer's emotions. If the conversation includes the word 'thank you,' output 'emotion: happy.'"

[0490] In this way, teachers and store managers can easily generate reports based on voice and sentiment data, and streamline communication with stakeholders.

[0491] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0492] Step 1:

[0493] Users record audio using their smartphones or dedicated devices. Specifically, teachers or store managers launch the application and tap the record button, which activates the device's microphone and records ambient sounds. The input is conversational audio from the classroom or store, and the output is a recorded audio data file.

[0494] Step 2:

[0495] The device temporarily stores the audio data and sends it to the server via the internet. Specifically, when you tap the "Send" button after recording is finished, the audio data file is uploaded to the server. The input is the recorded audio data file, and the output is the audio data sent to the server.

[0496] Step 3:

[0497] The server analyzes the received audio data and converts it into text data. Specifically, the audio data is converted into text data using speech recognition technology such as the Google Speech-to-Text API. The input is the audio data sent to the server, and the output is the converted text data.

[0498] Step 4:

[0499] The server inputs the converted text data into an emotion recognition engine to generate emotion data. Specifically, the text data is analyzed by the emotion recognition engine, and the user's emotion is identified. For example, if the text data contains "thank you," it will be recognized as "happy." The input is the text data obtained from speech analysis, and the output is the generated emotion data.

[0500] Step 5:

[0501] The server generates a report based on text data and sentiment data. Specifically, text data and sentiment data are embedded in a pre-configured report template. The input is text data and sentiment data, and the output is the generated report.

[0502] Step 6:

[0503] The server sends the generated report to the user's terminal, and the user edits the report. Specifically, the user can review the report content and enter corrections or additional information as needed. The input is the generated report, and the output is the report edited by the user.

[0504] Step 7:

[0505] The device resends the edited report to the server. Specifically, tapping the "Save" button uploads the edited report to the server. The input is the report edited by the user, and the output is the edited report sent to the server.

[0506] Step 8:

[0507] The server sends the edited report to parents and relevant users and sends notifications. Specifically, it uses a push notification function to inform recipients that a new report has arrived. The input is the edited report, and the output is the report sent to parents and relevant users, along with the push notification.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] [Second Embodiment]

[0512] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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).

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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".

[0524] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[0525] System Examples

[0526] 1. Recording and transmitting audio data

[0527] Device (Teacher's device)

[0528] Teachers record classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[0529] Specific example:

[0530] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[0531] 2. Analysis and transcription of audio data

[0532] server

[0533] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. A machine learning model is used for speech recognition, applying a model that is strong in everyday conversation and specific terminology. The server then analyzes this audio data and converts it back into text data.

[0534] Specific example:

[0535] When the server receives an audio file, it automatically starts the speech recognition engine. The engine analyzes the audio and generates the corresponding text. For example, the audio "The fight that just happened was between A and B" is transcribed as "The fight that just happened was between A and B."

[0536] 3. Report generation and editing

[0537] server

[0538] The system automatically generates a report by applying text data to a report template. The generated report is then sent to the teacher's device.

[0539] User (Teacher)

[0540] Teachers can review the report and edit the necessary parts. For example, they can add specific details about the situation or additional information. The edited report is then sent back to the server.

[0541] Specific example:

[0542] The server embeds text data into a report template and generates a report stating, "Today, XX and YY had a fight. The cause was XX and YY." This report is sent to the teacher's terminal, where the teacher can add additional comments.

[0543] 4. Sending reports and notifications to parents / guardians

[0544] server

[0545] The edited final report is sent to the parents. The report sent will include a push notification so that parents are immediately notified when a new report is received.

[0546] Specific example:

[0547] The server sends the edited report to the parent's app and simultaneously sends a push notification. When the parent opens the app, the report will appear in the "Today's Announcements" section.

[0548] 5. Sharing among teachers

[0549] server

[0550] Share the report with other relevant teachers as needed. For example, send the report to the homeroom teacher, school nurse, grade level head, etc., to facilitate information sharing among teachers.

[0551] Specific example:

[0552] If the server is configured to share the same report with other relevant faculty members, the report will be automatically sent to each of their devices.

[0553] The above is a specific embodiment of the present invention, a system that streamlines communication between teachers and parents and reduces the burden on both parties by seamlessly performing a series of steps from recording audio data to automatically generating, editing, and sending reports.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] User (Teacher)

[0557] The teacher launches the dedicated app and selects the recording function. Tapping the record button prompts the app to request access to the microphone, and recording begins. The teacher records the necessary scenes and then taps the end-of-recording button.

[0558] Step 2:

[0559] Device (Teacher's device)

[0560] Once recording is complete, the audio data is temporarily stored on the device. The stored audio data is then automatically uploaded to a server via the internet.

[0561] Step 3:

[0562] server

[0563] The server receives the audio data. Upon receiving the audio data, the speech recognition engine within the server starts up and converts the audio data into text data. The speech recognition technology employs a machine learning model to analyze and convert the data with high accuracy.

[0564] Step 4:

[0565] server

[0566] Based on the converted text data, the automated report generation module generates a report. The text data is embedded in a pre-configured report template, and the initial report is created.

[0567] Step 5:

[0568] server

[0569] The initial generated report is sent to the teacher's device. A notification function is used to inform the teacher so they can receive and review the report.

[0570] Step 6:

[0571] User (Teacher)

[0572] The teacher reviews the report and edits it as needed. For example, they might add specific details or additional information. Once editing is complete, the teacher saves the edited report and resubmits it to the server.

[0573] Step 7:

[0574] Device (Teacher's device)

[0575] Upload the edited report data to the server.

[0576] Step 8:

[0577] server

[0578] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[0579] Step 9:

[0580] User (Parent / Guardian)

[0581] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[0582] Step 10:

[0583] server

[0584] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[0585] (Example 1)

[0586] 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."

[0587] Traditional methods for recording classroom events and creating reports involved a great deal of manual work, placing a significant burden on both teachers and parents. Furthermore, manual recording and report creation were time-consuming, and the accuracy of the information could not always be guaranteed. Moreover, in today's world, where efficiency and speed in reporting are crucial, existing systems have failed to adequately meet these needs. Therefore, this invention provides a system for automatically analyzing audio data and automatically generating reports to solve these problems.

[0588] 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.

[0589] In this invention, the server includes a speech recognition engine control means that, after receiving audio data, activates a speech recognition engine and performs a process of converting the audio data into text data; an automatic report generation means that applies the text data based on the audio data to a report template; and a report sharing means that shares the generated report with other faculty and staff. This eliminates the need to manually convert audio data to text, enabling the rapid and accurate generation and sharing of reports.

[0590] "Audio recording means" refers to devices or functions for recording audio in real time in environments such as classrooms.

[0591] "Voice data transmission means" refers to a function for transmitting voice data obtained through recording to a server via the internet.

[0592] "Speech analysis means" refers to a function that performs the process of analyzing the received speech data on the server and converting it into text data.

[0593] The "report generation method" is a function that automatically generates a report by applying the analyzed text data to a report template.

[0594] The "report editing means" is a function that sends the generated report to the terminal in a format that the user can edit.

[0595] The "report transmission method" refers to the function of resending the edited report to the server.

[0596] "Notification method" refers to a function that sends the edited final report to parents and relevant users and notifies them of its receipt.

[0597] The "speech recognition engine control means" is a function that activates the speech recognition engine after receiving speech data and executes the process of converting the speech data into text data.

[0598] The "automatic report generation method" is a function that automatically generates a report by applying text data to a report template based on audio data.

[0599] "Report sharing means" refers to a function for sharing generated reports with other faculty and staff members.

[0600] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The aim of this system is to streamline communication between teachers and parents and reduce the burden on both parties.

[0601] Hardware and software to be used

[0602] Device (Teacher's device)

[0603] This system uses devices such as smartphones and tablets as terminals. These devices have dedicated apps installed that provide functions such as recording, saving, and transmitting audio.

[0604] server

[0605] Many processes, such as receiving, analyzing, storing, generating reports on, and sharing audio data, are performed by the server. The server has high processing power and runs multiple software components, including a speech recognition engine and a database.

[0606] Speech recognition engine

[0607] To convert audio data into text data, a machine learning-based speech recognition engine (e.g., Google Cloud Speech-to-Text API) is used.

[0608] Report generation template

[0609] When generating reports based on text data, predefined templates are used. This makes it easy to create reports in a consistent format.

[0610] Data processing and calculation

[0611] Voice recording

[0612] The user (teacher) operates the audio recording device through a dedicated app to record the sound in the classroom. When recording begins, the device's microphone activates, recording ambient sounds in real time.

[0613] Storing and transmitting audio data

[0614] When the teacher's device finishes recording audio, the audio data is temporarily stored in local storage and then uploaded to the server via the internet.

[0615] Start the speech recognition engine

[0616] When the server receives audio data, it uses the speech recognition engine control means to activate the speech recognition engine and convert the audio data into text data.

[0617] Automatic report generation

[0618] Based on the converted text data, the automated report generation system embeds the data into a template and automatically generates the report.

[0619] Sharing of reports

[0620] The server not only sends the generated reports to the teacher's device but also shares them with other faculty members' devices as needed.

[0621] Specific example

[0622] Specific examples of audio data recording:

[0623] When the teacher taps the record button in the app, the device activates the microphone to record the classroom environment and the children's conversations.

[0624] Specific examples of sending audio data:

[0625] After recording is complete, the device uploads the audio data to a server via the internet. An HTTP POST request is used for the upload.

[0626] Specific examples of audio data analysis and transcription:

[0627] When the server receives an audio file, it automatically starts the speech recognition engine. For example, the audio "The fight that just happened was between A and B" is converted to text "The fight that just happened was between A and B."

[0628] Example of a prompt

[0629] Example of a prompt:

[0630] "I want to create an app that records events that occur in the classroom and saves them as audio data. Please explain what kind of mechanism is needed to implement a system that sends the recorded audio data to a server and converts the audio data into text. Furthermore, please explain in detail the process of automatically generating, editing, and sending the generated text data as a report."

[0631] The system described above allows for a seamless workflow from recording audio data to automatically generating, editing, and sending reports, thereby streamlining communication between teachers and parents.

[0632] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0633] Step 1:

[0634] The user taps the record button in the dedicated app.

[0635] Input: User tap operation

[0636] Operation: When the user taps the app's record button, the device's microphone is activated, and ambient sounds are recorded in real time. At the time recording begins, metadata (start time, location information, etc.) is also recorded.

[0637] Output: Audio files and metadata stored on the device

[0638] Step 2:

[0639] After recording is complete, the device temporarily saves the audio data to local storage.

[0640] Input: Action to end recording

[0641] Operation: When recording ends, the audio data is saved to the device's local storage. Important metadata (start time, end time, device ID, etc.) is also saved at this time.

[0642] Output: Audio files and metadata saved to local storage

[0643] Step 3:

[0644] The device uploads the stored audio data to the server via the internet.

[0645] Input: Audio files and metadata stored in local storage

[0646] Operation: The terminal sends audio data to the server using an HTTP POST request. In this process, a request containing the audio file and associated metadata is created.

[0647] Output: Audio files and metadata sent to the server

[0648] Step 4:

[0649] The server receives the audio data and starts the speech recognition engine.

[0650] Input: Audio files and metadata sent to the server

[0651] Operation: When the server receives an HTTP POST request, it saves the audio file to a temporary directory and starts a speech recognition engine (e.g., Google Cloud Speech-to-Text API).

[0652] Output: Speech recognition engine is operational.

[0653] Step 5:

[0654] The server uses a speech recognition engine to convert the audio data into text data.

[0655] Input: Audio files saved in a temporary directory

[0656] Operation: The speech recognition engine analyzes the audio data and generates corresponding text data. In this process, specific keywords and phrases are converted into text data.

[0657] Output: Generated text data

[0658] Step 6:

[0659] The server applies the generated text data to the report template to automatically generate the report.

[0660] Input: Generated text data

[0661] Operation: The server automatically generates a report by embedding text data into a predefined report template. Important information (date, time, location, stakeholders, etc.) is included in the report.

[0662] Output: Automated report

[0663] Step 7:

[0664] The server sends the generated report to the teacher's terminal.

[0665] Input: Automated report

[0666] Operation: The server uses email and push notifications to send the generated report to the teacher's device. Along with the report, an editing link and an in-app notification are sent.

[0667] Output: Report sent to the teacher's terminal

[0668] Step 8:

[0669] The user (teacher) reviews the report and edits it as needed.

[0670] Input: Report sent to the teacher's terminal

[0671] Operation: The user opens the report in the app and reviews its contents. They can add comments or make edits as needed. The edited report is then saved.

[0672] Output: Edited report

[0673] Step 9:

[0674] The terminal resends the edited report to the server.

[0675] Input: Edited report

[0676] Operation: Once editing is complete, the report is sent back to the server using an HTTP POST request.

[0677] Output: Edited report sent to the server

[0678] Step 10:

[0679] The server sends the edited final report to the parents and sends a push notification.

[0680] Input: Edited report sent to the server

[0681] Operation: The server sends the final report to the parent's device and simultaneously sends a push notification. This immediately notifies the parent that a new report has been received.

[0682] Output: Final report and push notification sent to the parent's device.

[0683] Step 11:

[0684] The server shares the generated report with other faculty and staff members.

[0685] Input: Automated or edited report

[0686] Operation: The server also sends the report to other relevant faculty members (e.g., homeroom teacher, school nurse, grade level head). This transmission also uses HTTP POST requests or email.

[0687] Output: Report sent to other faculty members' terminals

[0688] (Application Example 1)

[0689] 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."

[0690] In current classroom and retail environments, recording audio data and creating reports based on it is time-consuming and labor-intensive. Furthermore, the process of sharing these reports among stakeholders is cumbersome, making it particularly difficult to quickly share troubleshooting information. This increases the burden on teachers and technicians, hindering efficient information dissemination.

[0691] 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.

[0692] In this invention, the server includes: an audio recording means for recording audio in an environment such as a classroom; an audio data transmission means for transmitting the recorded audio data to the server; an audio analysis means for analyzing the audio data on the server and converting it into text data; a report generation means for automatically generating a report based on the text data; a report editing means for transmitting the generated report to a terminal in a user-editable format; a report transmission means for retransmitting the edited report to the server; a notification means for sending the final edited report to parents and relevant users; a technician support means for recording audio data in a physical store and generating a troubleshooting report based on it; and a technician information sharing means for sharing the generated report with relevant technicians. This enables efficient report creation and information sharing from audio data.

[0693] "Audio recording means" refers to devices or software used to record sounds that occur in environments such as classrooms or physical stores.

[0694] "Voice data transmission means" refers to a means for transmitting recorded voice data to a server via a network.

[0695] "Voice analysis means" refers to a technology that analyzes voice data sent to a server and converts it into text data.

[0696] The "report generation means" is a means of automatically creating a report using text data generated by the speech analysis means.

[0697] "Report editing means" refers to a means for sending the generated report to a terminal in a format that can be edited by the user.

[0698] "Report transmission means" refers to the means by which a user can resend an edited report to the server.

[0699] "Notification method" refers to the means of sending the edited final report to parents and relevant users for notification.

[0700] "Technical support methods" refer to methods for recording voice data in physical stores and generating troubleshooting reports based on that data.

[0701] A "means for sharing engineer information" refers to a method for sharing generated reports with relevant engineers.

[0702] This invention is a system that records audio in environments such as classrooms and physical stores, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The main components include audio recording means, audio data transmission means, audio analysis means, report generation means, report editing means, report transmission means, notification means, engineer support means, and engineer information sharing means.

[0703] This system uses the following hardware and software:

[0704] Smartphones and smart glasses are used as means of voice recording. This allows users to easily record conversations and ambient sounds.

[0705] As a means of speech analysis, the Google Web Speech API is used to convert speech data into text data.

[0706] A web application using the Django framework will be used as the means for generating and editing reports.

[0707] As a means of supporting engineers and sharing engineer information, the system enables the automatic generation of troubleshooting reports based on audio data and their sharing with relevant engineers.

[0708] Voice recording and transmission

[0709] Users (classroom teachers or store technicians) record audio through a dedicated app installed on their smartphones or smart glasses. When recording begins, the device's microphone activates and records ambient sounds. Once recording is complete, the audio data is temporarily stored on the device and then transmitted to a server via the internet.

[0710] Audio data analysis and transcription

[0711] When the server receives audio data, the speech recognition engine (Google Web Speech API) is activated and converts the audio data into text data. The converted text is then used for subsequent processing.

[0712] Report generation and editing

[0713] Based on the generated text data, the server automatically generates a report. A report template is used to allow users to easily fill in specific information. The generated report is sent to the user's terminal, where the user can enter additional information and edit the report as needed.

[0714] Sending reports to parents and relevant technicians

[0715] The edited final report is resent to the server. The server sends the final report to the parent or relevant technician and uses a notification system to inform them that a new report has been received. Furthermore, a technician information sharing system allows the report to be shared among relevant technicians, enabling rapid troubleshooting.

[0716] Specific example

[0717] For example, in a classroom, a recording might be made stating, "Today, Mr. Tanaka taught a math class. During the class, two students got into a fight." This recording is then converted into text data, generating a report like this: "Today, during Mr. Tanaka's math class, students A and B got into a fight. The cause was a dispute over seats."

[0718] Example of a prompt

[0719] Examples of prompts to input into a generative AI model are as follows:

[0720] "Based on this week's technical support report, please list the main troubleshooting issues in bullet points."

[0721] As a result, this system enables efficient report creation and information sharing from audio data, significantly reducing the burden on classrooms and physical stores.

[0722] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0723] Step 1:

[0724] The user (teacher or technician) launches a dedicated app installed on their smartphone or smart glasses and taps the record button. The input is voice, which is collected through the device's microphone. The recorded voice data is temporarily stored on the device.

[0725] Step 2:

[0726] The device transmits the recorded audio data to the server via the internet. The input is the recorded audio data, and the output is the audio data uploaded to the server.

[0727] Step 3:

[0728] The server analyzes the received audio data using speech analysis tools. Specifically, it uses the Google Web Speech API to convert the audio data into text data. The input is audio data, and the output is the analyzed text data.

[0729] Step 4:

[0730] The server automatically generates a report using a report generation device based on the text data obtained by the speech analysis device. The input is text data, and the output is an automatically generated report.

[0731] Step 5:

[0732] The server sends the generated report to the terminal, and the user reviews and edits the report through a dedicated app. The input is an automatically generated report, and the output is the edited report. The user can enter additional information and edit the report as needed.

[0733] Step 6:

[0734] The terminal resends the edited report to the server. The input is the edited report, and the output is the edited report resent to the server.

[0735] Step 7:

[0736] The server sends the edited final report to parents and relevant technicians and uses notification methods to inform them that the new report has been delivered. The input is the edited final report, and the output is the notification and report received by parents and relevant technicians.

[0737] Step 8:

[0738] The server uses a technical information sharing system to share the generated report with other relevant technical personnel. The input is the final report, and the output is the report sent to the relevant technical personnel's terminals.

[0739] Each step aims to efficiently generate and share reports automatically from audio data, thereby reducing the burden on users.

[0740] 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.

[0741] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by incorporating an emotion engine, this system also has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[0742] System Examples

[0743] 1. Recording and transmitting audio data

[0744] User (Teacher)

[0745] The teacher records classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[0746] Specific example:

[0747] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[0748] 2. Analysis and transcription of audio data

[0749] server

[0750] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. The speech recognition technology uses machine learning models to analyze and convert the data with high accuracy. The server then analyzes this audio data and converts it back into text data.

[0751] Emotional Engine

[0752] The server inputs the received audio data into the emotion engine, which recognizes the user's emotions from the voice. The emotion engine identifies emotions by analyzing voice tone, word choice, and other acoustic characteristics. The emotion data is added to the text data and used to create reports.

[0753] Specific example:

[0754] When the server receives an audio file, it automatically activates the speech recognition engine and emotion engine. The engines analyze the audio and generate corresponding text, while the emotion engine identifies emotions such as "anger" and "sadness" and adds emotion data to the text. For example, "The fight that just happened was between A and B" is converted to "The fight that just happened was between A and B (anger)."

[0755] 3. Report generation and editing

[0756] server

[0757] Based on the converted text data and sentiment data, the automated report generation module generates a report. The text data and sentiment data are embedded in a pre-configured report template, and the initial report is created.

[0758] User (Teacher)

[0759] Teachers can review the report and edit the necessary sections. Sentiment data is also reflected in the report, and they can enter specific situational descriptions and additional information. Once editing is complete, teachers save the edited report and resubmit it to the server.

[0760] Specific example:

[0761] The server embeds text and sentiment data into a report template and generates a report that reads, "Today, XX and YY had a fight (anger). The cause was XX and YY." This report is sent to the teacher's terminal, where the teacher can add additional comments.

[0762] 4. Sending reports and notifications to parents / guardians

[0763] server

[0764] The edited final report is sent to the parents. The report sent will include a push notification so that parents are immediately notified when a new report is received.

[0765] Specific example:

[0766] The server sends the edited report to the parent's app and simultaneously sends a push notification. When the parent opens the app, the report appears in the "Today's Announcements" section, and includes emotional information such as, "It seemed like [child's name] was feeling sad today."

[0767] 5. Sharing among teachers

[0768] server

[0769] Share the report with other relevant teachers as needed. For example, send the report to the homeroom teacher, school nurse, grade level head, etc., to facilitate information sharing among teachers.

[0770] Specific example:

[0771] If the server is configured to share the same report with other relevant faculty members, the report will be automatically sent to each of their devices.

[0772] The above is a specific embodiment of the present invention, a system that streamlines communication between teachers and parents and reduces the burden on both parties by seamlessly performing a series of steps from recording audio data to automatically generating, editing, and sending reports. Furthermore, by combining it with an emotion engine, it is possible to understand the user's emotions in more detail and provide appropriate feedback.

[0773] The following describes the processing flow.

[0774] Step 1:

[0775] User (Teacher)

[0776] The teacher launches the dedicated app and selects the recording function. Tapping the record button prompts the app to request access to the microphone, and recording begins. The teacher records the necessary scenes and then taps the end-of-recording button.

[0777] Step 2:

[0778] Device (Teacher's device)

[0779] Once recording is complete, the audio data is temporarily stored on the device. The stored audio data is then automatically uploaded to a server via the internet.

[0780] Step 3:

[0781] server

[0782] The server receives the audio data and starts the speech recognition engine. The audio data is then converted into text data. This allows the audio recording to be output as text.

[0783] Step 4:

[0784] server

[0785] The server also inputs the received audio data into the emotion engine, which recognizes the user's emotions from the voice. The emotion engine analyzes the tone of voice, word choice, and other acoustic characteristics to identify emotions and adds that data to the text data.

[0786] Step 5:

[0787] server

[0788] Based on the converted text data and sentiment data, the automated report generation module generates a report. The text data and sentiment data are embedded in a pre-configured report template, and the initial report is created.

[0789] Step 6:

[0790] server

[0791] The initial generated report is sent to the teacher's device. A notification function is used to inform the teacher so they can receive and review the report.

[0792] Step 7:

[0793] User (Teacher)

[0794] The teacher reviews the report and edits the necessary parts. Since sentiment data is also reflected in the report, the teacher enters specific situational descriptions and additional information. Once editing is complete, the teacher saves the edited report and resubmits it to the server.

[0795] Step 8:

[0796] Device (Teacher's device)

[0797] Upload the edited report data to the server.

[0798] Step 9:

[0799] server

[0800] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[0801] Step 10:

[0802] User (Parent / Guardian)

[0803] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[0804] Step 11:

[0805] server

[0806] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[0807] (Example 2)

[0808] 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".

[0809] Traditionally, recording and analyzing audio data in classrooms and other environments was often done manually, requiring considerable time and effort. Furthermore, report creation was primarily manual, leading to problems with accuracy and consistency. Additionally, there was a lack of technology to identify emotions from audio data and reflect them in reports, highlighting the need for more efficient communication between teachers and parents.

[0810] 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.

[0811] In this invention, the server includes a voice analysis means for analyzing voice data and converting it into text data, an emotion recognition means for extracting emotion data from the text data converted by the voice analysis means, and a report generation means for automatically generating a report based on the text data and emotion data. This allows the user to automatically perform the entire process from recording voice data to generating, editing, and sending reports, significantly improving the efficiency of communication between teachers and parents.

[0812] "Sound recording means" refers to a device or system for recording sounds within an environment.

[0813] "Audio data storage means" refers to a device or system for temporarily storing recorded audio data.

[0814] "Voice data transmission means" refers to a device or system for transmitting stored voice data to a server.

[0815] "Speech analysis means" refers to a device or system for analyzing received speech data and converting it into text data.

[0816] "Emotion recognition means" refers to a device or system for extracting a user's emotions from text data converted by speech analysis means.

[0817] A "report generation means" is a device or system for automatically generating reports based on text data and sentiment data.

[0818] "Report editing means" refers to a device or system for sending a generated report to a terminal in a format that can be edited by the user.

[0819] "Report transmission means" refers to a device or system for resending an edited report to a server.

[0820] "Notification means" refers to a device or system for sending the edited final report to parents and relevant users and notifying them of this fact.

[0821] "Information sharing means" refers to a device or system for sharing generated reports among other relevant users.

[0822] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by combining this system with an emotion recognition engine, it has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[0823] Hardware and software

[0824] User (Teacher)

[0825] Teachers use a dedicated application (e.g., a custom app for iOS or Android) to record audio in the classroom. Recording is done using the microphone on a smartphone or tablet.

[0826] terminal

[0827] The device temporarily stores the recorded data in its internal storage and uploads the data to the server via an internet connection.

[0828] server

[0829] The server analyzes speech data using speech analysis and sentiment recognition technologies such as Google Cloud Speech-to-Text API and IBM Watson Tone Analyzer, and converts it into text data. Based on the converted text data and sentiment data, it automatically generates a report using a text generation library written in Python and sends it to the user.

[0830] Specific explanation of system operation

[0831] 1. Recording audio data

[0832] The user (teacher) launches the dedicated app and taps the record button. This activates the device's microphone and records the audio in the classroom.

[0833] Specific example: When the user taps the "Start Recording" button in the app, the device turns on the microphone and begins recording audio data.

[0834] 2. Temporary storage and transmission of recorded data to the server

[0835] Once recording is complete, the device temporarily saves the audio data to its internal storage. It then uploads the audio data to the server via Wi-Fi or mobile data.

[0836] Specific example: After recording is complete, the device saves the audio file to its internal storage and displays a notification to the user stating, "Recording saved." The saved audio data is then uploaded to the server.

[0837] 3. Analysis and transcription of audio data

[0838] The server converts the received audio data into text data using the Google Cloud Speech-to-Text API.

[0839] Specific example: When the server receives an audio file, it automatically starts the speech recognition engine and converts the audio to text.

[0840] 4. Extraction of emotional data

[0841] The server uses IBM Watson Tone Analyzer to extract sentiment data from the converted text data and adds sentiment labels to the text data.

[0842] Specific example: An emotion recognition engine identifies emotions such as "anger" and "sadness," and adds emotion labels to the text data, such as "The argument earlier was between A and B (anger)."

[0843] 5. Automatic generation and user editing of reports

[0844] The server generates an initial report using a pre-configured report template based on text and sentiment data. The generated report is sent to the user's (teacher's) terminal and can be edited as needed. Once editing is complete, the teacher sends the edited report back to the server.

[0845] Specific example: The server embeds text and sentiment data into a report template and sends the report to the teacher's terminal. The teacher adds necessary comments, saves the edited report, and then resends it to the server.

[0846] 6. Sending reports to parents and relevant users.

[0847] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[0848] Specific example: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements."

[0849] Example of a prompt

[0850] 1. "Please record the audio from the classroom and send it to the server."

[0851] 2. "Convert the audio file to text and extract the sentiment data."

[0852] 3. "Generate a report based on the extracted text data and sentiment data."

[0853] 4. Edit the generated report and send it to the parents.

[0854] The above describes an embodiment of the present invention, which enables a seamless workflow from recording audio data to generating, editing, and transmitting reports. This workflow streamlines communication between teachers and parents, reducing the burden on both parties. Furthermore, by combining this with emotion recognition technology, it is possible to reflect the user's emotional information in the report, providing more detailed and useful information.

[0855] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0856] Step 1:

[0857] Audio data recording

[0858] When the user (teacher) launches the dedicated app and taps the record button, the device's microphone activates and records the audio in the classroom.

[0859] Input: User's tap of the recording button

[0860] Data processing: The device's microphone picks up ambient sounds and generates audio data.

[0861] Output: Audio data (e.g., .wav format)

[0862] Specific operation: Recording begins when the user taps the "Start Recording" button in the app, and the recording time is displayed on the app screen.

[0863] Step 2:

[0864] Temporary storage of recording data

[0865] Once recording is complete, the device temporarily saves the recorded audio data to its internal storage.

[0866] Input: Recording termination operation (user taps the record button)

[0867] Data processing: The generated audio data is saved to the device's internal storage.

[0868] Output: Temporarily saved audio data

[0869] Specific action: Tapping the record button again will end the recording and a pop-up notification will appear stating, "Recording saved."

[0870] Step 3:

[0871] Sending audio data to the server

[0872] The device uploads the stored audio data to the server via an internet connection.

[0873] Input: Temporarily stored audio data

[0874] Data processing: Upload audio data to the server.

[0875] Output: Audio data sent to the server

[0876] Specific operation: The device uploads the audio file to the server using Wi-Fi or mobile data, and the progress is displayed with a progress bar. Once complete, a "Sending Complete" message is displayed.

[0877] Step 4:

[0878] Analysis of audio data

[0879] The server automatically launches a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to analyze the received audio data.

[0880] Input: Audio data sent to the server

[0881] Data processing: Analyze audio data and convert it into text data.

[0882] Output: Text data

[0883] Specific operation: The server receives the audio file and calls the Google Cloud Speech-to-Text API to begin analyzing the audio data. The processing status is displayed on the dashboard.

[0884] Step 5:

[0885] Convert to text data

[0886] The server converts the analyzed audio data into text data. The converted text data is stored in a database on the server.

[0887] Input: Audio data analyzed by a speech recognition engine

[0888] Data manipulation: Converting audio data to text.

[0889] Output: Text data (e.g., .txt format)

[0890] Specific operation: The server converts audio files into text data and saves that data to a database. A preview of the conversion results can be viewed on the dashboard.

[0891] Step 6:

[0892] Extraction of emotional data

[0893] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to extract the user's emotions based on the text data and adds emotion labels to the text data.

[0894] Input: Text data

[0895] Data processing: Identify emotions from text data and add emotion labels.

[0896] Output: Text data with emotion labels attached.

[0897] Specific operation: The server inputs text data into the emotion recognition engine and generates text data with emotion labels such as "anger" and "sadness" attached.

[0898] Step 7:

[0899] Automatic report generation

[0900] The server automatically generates a report based on text data and sentiment data. The generated report is sent to the user's (teacher's) terminal.

[0901] Input: Text data with emotion labels attached

[0902] Data processing: Generate reports using pre-configured report templates.

[0903] Output: Initial Report

[0904] Specific operation: The server uses a template to generate a report such as, "Today, XX and YY had a fight (anger). The cause was XX and YY," and sends it to the user's terminal.

[0905] Step 8:

[0906] Editing the report

[0907] The user (teacher) reviews the report using a dedicated app and edits the necessary parts. Once editing is complete, they resubmit the report to the server.

[0908] Input: Initial report

[0909] Data processing: Users edit reports and resend them to the server.

[0910] Output: Edited report

[0911] Specific actions: The user reviews the report, adds an "additional comment" to the input field, saves the edits, and then resends the report to the server.

[0912] Step 9:

[0913] Sending reports to parents and related users

[0914] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[0915] Input: Edited report

[0916] Data processing: Send the report to parents and relevant teachers.

[0917] Output: Sent reports and push notifications

[0918] Specific operation: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements".

[0919] Step 10:

[0920] Sharing among teachers

[0921] The server will also share the report with other relevant teachers (e.g., homeroom teachers, school nurses, grade level heads) as needed.

[0922] Input: Report data

[0923] Data processing: Automatically send reports to relevant faculty members.

[0924] Output: Shared report

[0925] Specific operation: The server sends the report to the homeroom teacher and school nurse, and sends a notification to each teacher's terminal stating, "A new report has been shared."

[0926] (Application Example 2)

[0927] 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."

[0928] In recent years, there has been a growing demand for improved education and customer service in environments such as classrooms and physical stores by utilizing voice recording and emotion recognition technologies. However, existing systems suffer from insufficient accuracy in transcribing voice data and recognizing emotions, and also lack the functionality to automatically generate reports using this data effectively. As a result, it is difficult for teachers and store managers to quickly and accurately grasp the necessary information, hindering improvements in operational efficiency.

[0929] 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. In this invention, the server includes a voice analysis means that analyzes voice data and converts it into text data, an emotion recognition means that recognizes emotions and generates emotion data, and a report generation means that automatically generates a report based on the text data and emotion data. This makes it possible to obtain highly accurate text data and emotion data from voice data and to automatically generate a report based on them.

[0930] "Voice recording means" refers to a device or system for recording sound in an environment such as a classroom or a physical store.

[0931] "Voice data transmission means" refers to a device or system for transmitting recorded voice data to a server.

[0932] "Voice analysis means" refers to a device or system that analyzes voice data received by a server and converts that voice data into text data.

[0933] "Emotion recognition means" refers to a device or system that recognizes a user's emotions from voice data and generates emotion data.

[0934] "Report generation means" refers to a device or system that automatically generates a report based on acquired text data and sentiment data.

[0935] "Report editing means" refers to a device or system that sends the generated report to a terminal in a format that can be edited by the user.

[0936] "Report transmission means" refers to a device or system for resending reports edited by users to a server.

[0937] "Notification means" refers to a device or system that transmits the edited final report to parents and relevant users.

[0938] "Text data" refers to data in text format obtained as a result of analyzing and converting audio data.

[0939] "Emotional data" refers to data that represents the user's emotions as recognized from voice data.

[0940] Modes for carrying out the invention

[0941] A system for carrying out this invention includes the following components:

[0942] 1. Audio recording means:

[0943] Users (teachers and store operators) record ambient sounds in classrooms and physical stores using smartphones or dedicated devices. For example, when a teacher wants to record students' comments or questions during class, they launch a dedicated application and tap the record button to start audio recording.

[0944] 2. Means for transmitting audio data:

[0945] Once recording is complete, the audio data is temporarily stored on the device and then sent to the server. This transmission requires an internet connection. For example, if you tap the "Send" button after recording is finished, the audio file will be automatically uploaded to the server.

[0946] 3. Voice analysis means:

[0947] The server analyzes the received audio data using speech recognition technology and converts it into text data. Machine learning models such as the Google Speech-to-Text API are used for this process. The analyzed text data then proceeds to the next processing step.

[0948] 4. Emotion recognition means:

[0949] On the server, emotion data is generated using emotion recognition technology based on text data. This is done by analyzing the tone of voice and the content of the speaker's speech. For example, if the word "thank you" is included, it will be recognized as "happy".

[0950] 5. Report generation means:

[0951] Using the analyzed text and sentiment data, a report is automatically generated. This report is created based on a pre-configured report template. For example, the report might be in the format, "Today, [Name] is experiencing sadness."

[0952] 6. Report editing methods:

[0953] The generated report is sent to the user's device in an editable format. The user can review the report and enter any necessary corrections or additional information. The edited report is then sent back to the server.

[0954] 7. Method of sending the report:

[0955] The server sends the edited report to parents and other relevant parties. A push notification feature is used to inform recipients that a new report has arrived. For example, tapping the "Notify Parents" button sends the report to the parent's app and displays a push notification.

[0956] 8. Means of notification:

[0957] Once the report has been submitted, the system sends a notification to the recipient using a notification method. For example, to make it easier for parents to check the report, they may be notified via push notification on their smartphone that a new report has arrived.

[0958] Hardware and software to be used

[0959] Hardware:

[0960] Smartphone, dedicated recording terminal, server

[0961] Software / Tools:

[0962] Voice recording application

[0963] Internet connection

[0964] Google Speech-to-Text API (speech recognition technology using machine learning models)

[0965] Emotion recognition engine (proprietary software)

[0966] Report generation and editing software

[0967] User application with push notification functionality

[0968] Examples of specific cases and prompt statements

[0969] For example, consider a scenario in a store where staff explain products to customers and express their gratitude. The system automatically analyzes this audio data and generates a report containing sentences like the following:

[0970] "This product is very popular. Let me explain how to use it... Thank you!"

[0971] Example of a report generated based on this:

[0972] Customer Feedback:

[0973] This product is very popular. Let me explain how to use it. ...Thank you!

[0974] Emotion: happy”

[0975] Example of a prompt

[0976] "Convert the following audio data to text and analyze the customer's emotions. If the conversation includes the word 'thank you,' output 'emotion: happy.'"

[0977] In this way, teachers and store managers can easily generate reports based on voice and sentiment data, and streamline communication with stakeholders.

[0978] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0979] Step 1:

[0980] Users record audio using their smartphones or dedicated devices. Specifically, teachers or store managers launch the application and tap the record button, which activates the device's microphone and records ambient sounds. The input is conversational audio from the classroom or store, and the output is a recorded audio data file.

[0981] Step 2:

[0982] The device temporarily stores the audio data and sends it to the server via the internet. Specifically, when you tap the "Send" button after recording is finished, the audio data file is uploaded to the server. The input is the recorded audio data file, and the output is the audio data sent to the server.

[0983] Step 3:

[0984] The server analyzes the received audio data and converts it into text data. Specifically, the audio data is converted into text data using speech recognition technology such as the Google Speech-to-Text API. The input is the audio data sent to the server, and the output is the converted text data.

[0985] Step 4:

[0986] The server inputs the converted text data into an emotion recognition engine to generate emotion data. Specifically, the text data is analyzed by the emotion recognition engine, and the user's emotion is identified. For example, if the text data contains "thank you," it will be recognized as "happy." The input is the text data obtained from speech analysis, and the output is the generated emotion data.

[0987] Step 5:

[0988] The server generates a report based on text data and sentiment data. Specifically, text data and sentiment data are embedded in a pre-configured report template. The input is text data and sentiment data, and the output is the generated report.

[0989] Step 6:

[0990] The server sends the generated report to the user's terminal, and the user edits the report. Specifically, the user can review the report content and enter corrections or additional information as needed. The input is the generated report, and the output is the report edited by the user.

[0991] Step 7:

[0992] The device resends the edited report to the server. Specifically, tapping the "Save" button uploads the edited report to the server. The input is the report edited by the user, and the output is the edited report sent to the server.

[0993] Step 8:

[0994] The server sends the edited report to parents and relevant users and sends notifications. Specifically, it uses a push notification function to inform recipients that a new report has arrived. The input is the edited report, and the output is the report sent to parents and relevant users, along with the push notification.

[0995] 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.

[0996] 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.

[0997] 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.

[0998] [Third Embodiment]

[0999] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1000] 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.

[1001] 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).

[1002] 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.

[1003] 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.

[1004] 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).

[1005] 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.

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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".

[1011] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[1012] System Examples

[1013] 1. Recording and transmitting audio data

[1014] Device (Teacher's device)

[1015] Teachers record classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[1016] Specific example:

[1017] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[1018] 2. Analysis and transcription of audio data

[1019] server

[1020] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. A machine learning model is used for speech recognition, applying a model that is strong in everyday conversation and specific terminology. The server then analyzes this audio data and converts it back into text data.

[1021] Specific example:

[1022] When the server receives an audio file, it automatically starts the speech recognition engine. The engine analyzes the audio and generates the corresponding text. For example, the audio "The fight that just happened was between A and B" is transcribed as "The fight that just happened was between A and B."

[1023] 3. Report generation and editing

[1024] server

[1025] The system automatically generates a report by applying text data to a report template. The generated report is then sent to the teacher's device.

[1026] User (Teacher)

[1027] Teachers can review the report and edit the necessary parts. For example, they can add specific details about the situation or additional information. The edited report is then sent back to the server.

[1028] Specific example:

[1029] The server embeds text data into a report template and generates a report stating, "Today, XX and YY had a fight. The cause was XX and YY." This report is sent to the teacher's terminal, where the teacher can add additional comments.

[1030] 4. Sending reports and notifications to parents / guardians

[1031] server

[1032] The edited final report is sent to the parents. The report sent will include a push notification so that parents are immediately notified when a new report is received.

[1033] Specific example:

[1034] The server sends the edited report to the parent's app and simultaneously sends a push notification. When the parent opens the app, the report will appear in the "Today's Announcements" section.

[1035] 5. Sharing among faculty members

[1036] server

[1037] Share the report with other relevant teachers as needed. For example, send the report to the homeroom teacher, school nurse, grade level head, etc., to facilitate information sharing among teachers.

[1038] Specific example:

[1039] If the server is configured to share the same report with other relevant faculty members, the report will be automatically sent to each of their devices.

[1040] The above is a specific embodiment of the present invention, a system that streamlines communication between teachers and parents and reduces the burden on both parties by seamlessly performing a series of steps from recording audio data to automatically generating, editing, and sending reports.

[1041] The following describes the processing flow.

[1042] Step 1:

[1043] User (Teacher)

[1044] The teacher launches the dedicated app and selects the recording function. Tapping the record button prompts the app to request access to the microphone, and recording begins. The teacher records the necessary scenes and then taps the end-of-recording button.

[1045] Step 2:

[1046] Device (Teacher's device)

[1047] Once recording is complete, the audio data is temporarily stored on the device. The stored audio data is then automatically uploaded to a server via the internet.

[1048] Step 3:

[1049] server

[1050] The server receives the audio data. Upon receiving the audio data, the speech recognition engine within the server starts up and converts the audio data into text data. The speech recognition technology employs a machine learning model to analyze and convert the data with high accuracy.

[1051] Step 4:

[1052] server

[1053] Based on the converted text data, the automated report generation module generates a report. The text data is embedded in a pre-configured report template, and the initial report is created.

[1054] Step 5:

[1055] server

[1056] The initial generated report is sent to the teacher's device. A notification function is used to inform the teacher so they can receive and review the report.

[1057] Step 6:

[1058] User (Teacher)

[1059] The teacher reviews the report and edits it as needed. For example, they might add specific details or additional information. Once editing is complete, the teacher saves the edited report and resubmits it to the server.

[1060] Step 7:

[1061] Device (Teacher's device)

[1062] Upload the edited report data to the server.

[1063] Step 8:

[1064] server

[1065] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[1066] Step 9:

[1067] User (Parent / Guardian)

[1068] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[1069] Step 10:

[1070] server

[1071] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[1072] (Example 1)

[1073] 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."

[1074] Traditional methods for recording classroom events and creating reports involved a great deal of manual work, placing a significant burden on both teachers and parents. Furthermore, manual recording and report creation were time-consuming, and the accuracy of the information could not always be guaranteed. Moreover, in today's world, where efficiency and speed in reporting are crucial, existing systems have failed to adequately meet these needs. Therefore, this invention provides a system for automatically analyzing audio data and automatically generating reports to solve these problems.

[1075] 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.

[1076] In this invention, the server includes a speech recognition engine control means that, after receiving audio data, activates a speech recognition engine and performs a process of converting the audio data into text data; an automatic report generation means that applies the text data based on the audio data to a report template; and a report sharing means that shares the generated report with other faculty and staff. This eliminates the need to manually convert audio data to text, enabling the rapid and accurate generation and sharing of reports.

[1077] "Audio recording means" refers to devices or functions for recording audio in real time in environments such as classrooms.

[1078] "Voice data transmission means" refers to a function for transmitting voice data obtained through recording to a server via the internet.

[1079] "Speech analysis means" refers to a function that performs the process of analyzing the received speech data on the server and converting it into text data.

[1080] The "report generation method" is a function that automatically generates a report by applying the analyzed text data to a report template.

[1081] The "report editing means" is a function that sends the generated report to the terminal in a format that the user can edit.

[1082] The "report transmission method" refers to the function of resending the edited report to the server.

[1083] "Notification method" refers to a function that sends the edited final report to parents and relevant users and notifies them of its receipt.

[1084] The "speech recognition engine control means" is a function that activates the speech recognition engine after receiving speech data and executes the process of converting the speech data into text data.

[1085] The "automatic report generation method" is a function that automatically generates a report by applying text data to a report template based on audio data.

[1086] "Report sharing means" refers to a function for sharing generated reports with other faculty and staff members.

[1087] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The aim of this system is to streamline communication between teachers and parents and reduce the burden on both parties.

[1088] Hardware and software to be used

[1089] Device (Teacher's device)

[1090] This system uses devices such as smartphones and tablets as terminals. These devices have dedicated apps installed that provide functions such as recording, saving, and transmitting audio.

[1091] server

[1092] Many processes, such as receiving, analyzing, storing, generating reports on, and sharing audio data, are performed by the server. The server has high processing power and runs multiple software components, including a speech recognition engine and a database.

[1093] Speech recognition engine

[1094] To convert audio data into text data, a machine learning-based speech recognition engine (e.g., Google Cloud Speech-to-Text API) is used.

[1095] Report generation template

[1096] When generating reports based on text data, predefined templates are used. This makes it easy to create reports in a consistent format.

[1097] Data processing and calculation

[1098] Voice recording

[1099] The user (teacher) operates the audio recording device through a dedicated app to record the sound in the classroom. When recording begins, the device's microphone activates, recording ambient sounds in real time.

[1100] Storing and transmitting audio data

[1101] When the teacher's device finishes recording audio, the audio data is temporarily stored in local storage and then uploaded to the server via the internet.

[1102] Start the speech recognition engine

[1103] When the server receives audio data, it uses the speech recognition engine control means to activate the speech recognition engine and convert the audio data into text data.

[1104] Automatic report generation

[1105] Based on the converted text data, the automated report generation system embeds the data into a template and automatically generates the report.

[1106] Sharing of reports

[1107] The server not only sends the generated reports to the teacher's device but also shares them with other faculty members' devices as needed.

[1108] Specific example

[1109] Specific examples of audio data recording:

[1110] When the teacher taps the record button in the app, the device activates the microphone to record the classroom environment and the children's conversations.

[1111] Specific examples of sending audio data:

[1112] After recording is complete, the device uploads the audio data to a server via the internet. An HTTP POST request is used for the upload.

[1113] Specific examples of audio data analysis and transcription:

[1114] When the server receives an audio file, it automatically starts the speech recognition engine. For example, the audio "The fight that just happened was between A and B" is converted to text "The fight that just happened was between A and B."

[1115] Example of a prompt

[1116] Example of a prompt:

[1117] "I want to create an app that records events that occur in the classroom and saves them as audio data. Please explain what kind of mechanism is needed to implement a system that sends the recorded audio data to a server and converts the audio data into text. Furthermore, please explain in detail the process of automatically generating, editing, and sending the generated text data as a report."

[1118] The system described above allows for a seamless workflow from recording audio data to automatically generating, editing, and sending reports, thereby streamlining communication between teachers and parents.

[1119] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1120] Step 1:

[1121] The user taps the record button in the dedicated app.

[1122] Input: User tap operation

[1123] Operation: When the user taps the app's record button, the device's microphone is activated, and ambient sounds are recorded in real time. At the time recording begins, metadata (start time, location information, etc.) is also recorded.

[1124] Output: Audio files and metadata stored on the device

[1125] Step 2:

[1126] After recording is complete, the device temporarily saves the audio data to local storage.

[1127] Input: Action to end recording

[1128] Operation: When recording ends, the audio data is saved to the device's local storage. Important metadata (start time, end time, device ID, etc.) is also saved at this time.

[1129] Output: Audio files and metadata saved to local storage

[1130] Step 3:

[1131] The device uploads the stored audio data to the server via the internet.

[1132] Input: Audio files and metadata stored in local storage

[1133] Operation: The terminal sends audio data to the server using an HTTP POST request. In this process, a request containing the audio file and associated metadata is created.

[1134] Output: Audio files and metadata sent to the server

[1135] Step 4:

[1136] The server receives the audio data and starts the speech recognition engine.

[1137] Input: Audio files and metadata sent to the server

[1138] Operation: When the server receives an HTTP POST request, it saves the audio file to a temporary directory and starts a speech recognition engine (e.g., Google Cloud Speech-to-Text API).

[1139] Output: Speech recognition engine is operational.

[1140] Step 5:

[1141] The server uses a speech recognition engine to convert the audio data into text data.

[1142] Input: Audio files saved in a temporary directory

[1143] Operation: The speech recognition engine analyzes the audio data and generates corresponding text data. In this process, specific keywords and phrases are converted into text data.

[1144] Output: Generated text data

[1145] Step 6:

[1146] The server applies the generated text data to the report template to automatically generate the report.

[1147] Input: Generated text data

[1148] Operation: The server automatically generates a report by embedding text data into a predefined report template. Important information (date, time, location, stakeholders, etc.) is included in the report.

[1149] Output: Automated report

[1150] Step 7:

[1151] The server sends the generated report to the teacher's terminal.

[1152] Input: Automated report

[1153] Operation: The server uses email and push notifications to send the generated report to the teacher's device. Along with the report, an editing link and an in-app notification are sent.

[1154] Output: Report sent to the teacher's terminal

[1155] Step 8:

[1156] The user (teacher) reviews the report and edits it as needed.

[1157] Input: Report sent to the teacher's terminal

[1158] Operation: The user opens the report in the app and reviews its contents. They can add comments or make edits as needed. The edited report is then saved.

[1159] Output: Edited report

[1160] Step 9:

[1161] The terminal resends the edited report to the server.

[1162] Input: Edited report

[1163] Operation: Once editing is complete, the report is sent back to the server using an HTTP POST request.

[1164] Output: Edited report sent to the server

[1165] Step 10:

[1166] The server sends the edited final report to the parents and sends a push notification.

[1167] Input: Edited report sent to the server

[1168] Operation: The server sends the final report to the parent's device and simultaneously sends a push notification. This immediately notifies the parent that a new report has been received.

[1169] Output: Final report and push notification sent to the parent's device.

[1170] Step 11:

[1171] The server shares the generated report with other faculty and staff members.

[1172] Input: Automated or edited report

[1173] Operation: The server also sends the report to other relevant faculty members (e.g., homeroom teacher, school nurse, grade level head). This transmission also uses HTTP POST requests or email.

[1174] Output: Report sent to other faculty members' terminals

[1175] (Application Example 1)

[1176] 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."

[1177] In current classroom and retail environments, recording audio data and creating reports based on it is time-consuming and labor-intensive. Furthermore, the process of sharing these reports among stakeholders is cumbersome, making it particularly difficult to quickly share troubleshooting information. This increases the burden on teachers and technicians, hindering efficient information dissemination.

[1178] 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.

[1179] In this invention, the server includes: an audio recording means for recording audio in an environment such as a classroom; an audio data transmission means for transmitting the recorded audio data to the server; an audio analysis means for analyzing the audio data on the server and converting it into text data; a report generation means for automatically generating a report based on the text data; a report editing means for transmitting the generated report to a terminal in a user-editable format; a report transmission means for retransmitting the edited report to the server; a notification means for sending the final edited report to parents and relevant users; a technician support means for recording audio data in a physical store and generating a troubleshooting report based on it; and a technician information sharing means for sharing the generated report with relevant technicians. This enables efficient report creation and information sharing from audio data.

[1180] "Audio recording means" refers to devices or software used to record sounds that occur in environments such as classrooms or physical stores.

[1181] "Voice data transmission means" refers to a means for transmitting recorded voice data to a server via a network.

[1182] "Voice analysis means" refers to a technology that analyzes voice data sent to a server and converts it into text data.

[1183] The "report generation means" is a means of automatically creating a report using text data generated by the speech analysis means.

[1184] "Report editing means" refers to a means for sending the generated report to a terminal in a format that can be edited by the user.

[1185] "Report transmission means" refers to the means by which a user can resend an edited report to the server.

[1186] "Notification method" refers to the means of sending the edited final report to parents and relevant users for notification.

[1187] "Technical support methods" refer to methods for recording voice data in physical stores and generating troubleshooting reports based on that data.

[1188] A "means for sharing engineer information" refers to a method for sharing generated reports with relevant engineers.

[1189] This invention is a system that records audio in environments such as classrooms and physical stores, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The main components include audio recording means, audio data transmission means, audio analysis means, report generation means, report editing means, report transmission means, notification means, engineer support means, and engineer information sharing means.

[1190] This system uses the following hardware and software:

[1191] Smartphones and smart glasses are used as means of voice recording. This allows users to easily record conversations and ambient sounds.

[1192] As a means of speech analysis, the Google Web Speech API is used to convert speech data into text data.

[1193] A web application using the Django framework will be used as the means for generating and editing reports.

[1194] As a means of supporting engineers and sharing engineer information, the system enables the automatic generation of troubleshooting reports based on audio data and their sharing with relevant engineers.

[1195] Voice recording and transmission

[1196] Users (classroom teachers or store technicians) record audio through a dedicated app installed on their smartphones or smart glasses. When recording begins, the device's microphone activates and records ambient sounds. Once recording is complete, the audio data is temporarily stored on the device and then transmitted to a server via the internet.

[1197] Audio data analysis and transcription

[1198] When the server receives audio data, the speech recognition engine (Google Web Speech API) is activated and converts the audio data into text data. The converted text is then used for subsequent processing.

[1199] Report generation and editing

[1200] Based on the generated text data, the server automatically generates a report. A report template is used to allow users to easily fill in specific information. The generated report is sent to the user's terminal, where the user can enter additional information and edit the report as needed.

[1201] Sending reports to parents and relevant technicians

[1202] The edited final report is resent to the server. The server sends the final report to the parent or relevant technician and uses a notification system to inform them that a new report has been received. Furthermore, a technician information sharing system allows the report to be shared among relevant technicians, enabling rapid troubleshooting.

[1203] Specific example

[1204] For example, in a classroom, a recording might be made stating, "Today, Mr. Tanaka taught a math class. During the class, two students got into a fight." This recording is then converted into text data, generating a report like this: "Today, during Mr. Tanaka's math class, students A and B got into a fight. The cause was a dispute over seats."

[1205] Example of a prompt

[1206] Examples of prompts to input into a generative AI model are as follows:

[1207] "Based on this week's technical support report, please list the main troubleshooting issues in bullet points."

[1208] As a result, this system enables efficient report creation and information sharing from audio data, significantly reducing the burden on classrooms and physical stores.

[1209] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1210] Step 1:

[1211] The user (teacher or technician) launches a dedicated app installed on their smartphone or smart glasses and taps the record button. The input is voice, which is collected through the device's microphone. The recorded voice data is temporarily stored on the device.

[1212] Step 2:

[1213] The device transmits the recorded audio data to the server via the internet. The input is the recorded audio data, and the output is the audio data uploaded to the server.

[1214] Step 3:

[1215] The server analyzes the received audio data using speech analysis tools. Specifically, it uses the Google Web Speech API to convert the audio data into text data. The input is audio data, and the output is the analyzed text data.

[1216] Step 4:

[1217] The server automatically generates a report using a report generation device based on the text data obtained by the speech analysis device. The input is text data, and the output is an automatically generated report.

[1218] Step 5:

[1219] The server sends the generated report to the terminal, and the user reviews and edits the report through a dedicated app. The input is an automatically generated report, and the output is the edited report. The user can enter additional information and edit the report as needed.

[1220] Step 6:

[1221] The terminal resends the edited report to the server. The input is the edited report, and the output is the edited report resent to the server.

[1222] Step 7:

[1223] The server sends the edited final report to parents and relevant technicians and uses notification methods to inform them that the new report has been delivered. The input is the edited final report, and the output is the notification and report received by parents and relevant technicians.

[1224] Step 8:

[1225] The server uses a technical information sharing system to share the generated report with other relevant technical personnel. The input is the final report, and the output is the report sent to the relevant technical personnel's terminals.

[1226] Each step aims to efficiently generate and share reports automatically from audio data, thereby reducing the burden on users.

[1227] 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.

[1228] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by incorporating an emotion engine, this system also has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[1229] System Examples

[1230] 1. Recording and transmitting audio data

[1231] User (Teacher)

[1232] The teacher records classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[1233] Specific example:

[1234] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[1235] 2. Analysis and transcription of audio data

[1236] server

[1237] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. The speech recognition technology uses machine learning models to analyze and convert the data with high accuracy. The server then analyzes this audio data and converts it back into text data.

[1238] Emotional Engine

[1239] The server inputs the received audio data into the emotion engine, which recognizes the user's emotions from the voice. The emotion engine identifies emotions by analyzing voice tone, word choice, and other acoustic characteristics. The emotion data is added to the text data and used to create reports.

[1240] Specific example:

[1241] When the server receives an audio file, it automatically activates the speech recognition engine and emotion engine. The engines analyze the audio and generate corresponding text, while the emotion engine identifies emotions such as "anger" and "sadness" and adds emotion data to the text. For example, "The fight that just happened was between A and B" is converted to "The fight that just happened was between A and B (anger)."

[1242] 3. Report generation and editing

[1243] server

[1244] Based on the converted text data and sentiment data, the automated report generation module generates a report. The text data and sentiment data are embedded in a pre-configured report template, and the initial report is created.

[1245] User (Teacher)

[1246] Teachers can review the report and edit the necessary sections. Sentiment data is also reflected in the report, and they can enter specific situational descriptions and additional information. Once editing is complete, teachers save the edited report and resubmit it to the server.

[1247] Specific example:

[1248] The server embeds text and sentiment data into a report template and generates a report that reads, "Today, XX and YY had a fight (anger). The cause was XX and YY." This report is sent to the teacher's terminal, where the teacher can add additional comments.

[1249] 4. Sending reports and notifications to parents / guardians

[1250] server

[1251] The edited final report is sent to the parents. The report sent will include a push notification so that parents are immediately notified when a new report is received.

[1252] Specific example:

[1253] The server sends the edited report to the parent's app and simultaneously sends a push notification. When the parent opens the app, the report appears in the "Today's Announcements" section, and includes emotional information such as, "It seemed like [child's name] was feeling sad today."

[1254] 5. Sharing among teachers

[1255] server

[1256] Share the report with other relevant teachers as needed. For example, send the report to the homeroom teacher, school nurse, grade level head, etc., to facilitate information sharing among teachers.

[1257] Specific example:

[1258] If the server is configured to share the same report with other relevant faculty members, the report will be automatically sent to each of their devices.

[1259] The above is a specific embodiment of the present invention, a system that streamlines communication between teachers and parents and reduces the burden on both parties by seamlessly performing a series of steps from recording audio data to automatically generating, editing, and sending reports. Furthermore, by combining it with an emotion engine, it is possible to understand the user's emotions in more detail and provide appropriate feedback.

[1260] The following describes the processing flow.

[1261] Step 1:

[1262] User (Teacher)

[1263] The teacher launches the dedicated app and selects the recording function. Tapping the record button prompts the app to request access to the microphone, and recording begins. The teacher records the desired scenes and then taps the end-of-recording button.

[1264] Step 2:

[1265] Device (Teacher's device)

[1266] Once recording is complete, the audio data is temporarily stored on the device. The stored audio data is then automatically uploaded to a server via the internet.

[1267] Step 3:

[1268] server

[1269] The server receives the audio data and starts the speech recognition engine. The audio data is then converted into text data. This allows the audio recording to be output as text.

[1270] Step 4:

[1271] server

[1272] The server also inputs the received audio data into the emotion engine, which recognizes the user's emotions from the voice. The emotion engine analyzes the tone of voice, word choice, and other acoustic characteristics to identify emotions and adds that data to the text data.

[1273] Step 5:

[1274] server

[1275] Based on the converted text data and sentiment data, the automated report generation module generates a report. The text data and sentiment data are embedded in a pre-configured report template, and the initial report is created.

[1276] Step 6:

[1277] server

[1278] The initial generated report is sent to the teacher's device. A notification function is used to inform the teacher so they can receive and review the report.

[1279] Step 7:

[1280] User (Teacher)

[1281] The teacher reviews the report and edits the necessary parts. Since sentiment data is also reflected in the report, the teacher enters specific situational descriptions and additional information. Once editing is complete, the teacher saves the edited report and resubmits it to the server.

[1282] Step 8:

[1283] Device (Teacher's device)

[1284] Upload the edited report data to the server.

[1285] Step 9:

[1286] server

[1287] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[1288] Step 10:

[1289] User (Parent / Guardian)

[1290] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[1291] Step 11:

[1292] server

[1293] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[1294] (Example 2)

[1295] 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."

[1296] Traditionally, recording and analyzing audio data in classrooms and other environments was often done manually, requiring considerable time and effort. Furthermore, report creation was primarily manual, leading to problems with accuracy and consistency. Additionally, there was a lack of technology to identify emotions from audio data and reflect them in reports, highlighting the need for more efficient communication between teachers and parents.

[1297] 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.

[1298] In this invention, the server includes a voice analysis means for analyzing voice data and converting it into text data, an emotion recognition means for extracting emotion data from the text data converted by the voice analysis means, and a report generation means for automatically generating a report based on the text data and emotion data. This allows the user to automatically perform the entire process from recording voice data to generating, editing, and sending reports, significantly improving the efficiency of communication between teachers and parents.

[1299] "Sound recording means" refers to a device or system for recording sounds within an environment.

[1300] "Audio data storage means" refers to a device or system for temporarily storing recorded audio data.

[1301] "Voice data transmission means" refers to a device or system for transmitting stored voice data to a server.

[1302] "Speech analysis means" refers to a device or system for analyzing received speech data and converting it into text data.

[1303] "Emotion recognition means" refers to a device or system for extracting a user's emotions from text data converted by speech analysis means.

[1304] A "report generation means" is a device or system for automatically generating reports based on text data and sentiment data.

[1305] "Report editing means" refers to a device or system for sending a generated report to a terminal in a format that can be edited by the user.

[1306] "Report transmission means" refers to a device or system for resending an edited report to a server.

[1307] "Notification means" refers to a device or system for sending the edited final report to parents and relevant users and notifying them of this fact.

[1308] "Information sharing means" refers to a device or system for sharing generated reports among other relevant users.

[1309] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by combining this system with an emotion recognition engine, it has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[1310] Hardware and software

[1311] User (Teacher)

[1312] Teachers use a dedicated application (e.g., a custom app for iOS or Android) to record audio in the classroom. Recording is done using the microphone on a smartphone or tablet.

[1313] terminal

[1314] The device temporarily stores the recorded data in its internal storage and uploads the data to the server via an internet connection.

[1315] server

[1316] The server analyzes speech data using speech analysis and sentiment recognition technologies such as Google Cloud Speech-to-Text API and IBM Watson Tone Analyzer, and converts it into text data. Based on the converted text data and sentiment data, it automatically generates a report using a text generation library written in Python and sends it to the user.

[1317] Specific explanation of system operation

[1318] 1. Recording audio data

[1319] The user (teacher) launches the dedicated app and taps the record button. This activates the device's microphone and records the audio in the classroom.

[1320] Specific example: When the user taps the "Start Recording" button in the app, the device turns on the microphone and begins recording audio data.

[1321] 2. Temporary storage and transmission of recorded data to the server

[1322] Once recording is complete, the device temporarily saves the audio data to its internal storage. It then uploads the audio data to the server via Wi-Fi or mobile data.

[1323] Specific example: After recording is complete, the device saves the audio file to its internal storage and displays a notification to the user stating, "Recording saved." The saved audio data is then uploaded to the server.

[1324] 3. Analysis and transcription of audio data

[1325] The server converts the received audio data into text data using the Google Cloud Speech-to-Text API.

[1326] Specific example: When the server receives an audio file, it automatically starts the speech recognition engine and converts the audio to text.

[1327] 4. Extraction of emotional data

[1328] The server uses IBM Watson Tone Analyzer to extract sentiment data from the converted text data and adds sentiment labels to the text data.

[1329] Specific example: An emotion recognition engine identifies emotions such as "anger" and "sadness," and adds emotion labels to the text data, such as "The argument earlier was between A and B (anger)."

[1330] 5. Automatic generation and user editing of reports

[1331] The server generates an initial report using a pre-configured report template based on text and sentiment data. The generated report is sent to the user's (teacher's) terminal and can be edited as needed. Once editing is complete, the teacher sends the edited report back to the server.

[1332] Specific example: The server embeds text and sentiment data into a report template and sends the report to the teacher's terminal. The teacher adds necessary comments, saves the edited report, and then resends it to the server.

[1333] 6. Sending reports to parents and relevant users.

[1334] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[1335] Specific example: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements."

[1336] Example of a prompt

[1337] 1. "Please record the audio from the classroom and send it to the server."

[1338] 2. "Convert the audio file to text and extract the sentiment data."

[1339] 3. "Generate a report based on the extracted text data and sentiment data."

[1340] 4. Edit the generated report and send it to the parents.

[1341] The above describes an embodiment of the present invention, which enables a seamless workflow from recording audio data to generating, editing, and transmitting reports. This workflow streamlines communication between teachers and parents, reducing the burden on both parties. Furthermore, by combining this with emotion recognition technology, it is possible to reflect the user's emotional information in the report, providing more detailed and useful information.

[1342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1343] Step 1:

[1344] Audio data recording

[1345] When the user (teacher) launches the dedicated app and taps the record button, the device's microphone activates and records the audio in the classroom.

[1346] Input: User's tap of the recording button

[1347] Data processing: The device's microphone picks up ambient sounds and generates audio data.

[1348] Output: Audio data (e.g., .wav format)

[1349] Specific operation: Recording begins when the user taps the "Start Recording" button in the app, and the recording time is displayed on the app screen.

[1350] Step 2:

[1351] Temporary storage of recording data

[1352] Once recording is complete, the device temporarily saves the recorded audio data to its internal storage.

[1353] Input: Recording termination operation (user taps the record button)

[1354] Data processing: The generated audio data is saved to the device's internal storage.

[1355] Output: Temporarily saved audio data

[1356] Specific action: Tapping the record button again will end the recording and a pop-up notification will appear stating, "Recording saved."

[1357] Step 3:

[1358] Sending audio data to the server

[1359] The device uploads the stored audio data to the server via an internet connection.

[1360] Input: Temporarily stored audio data

[1361] Data processing: Upload audio data to the server.

[1362] Output: Audio data sent to the server

[1363] Specific operation: The device uploads the audio file to the server using Wi-Fi or mobile data, and the progress is displayed with a progress bar. Once complete, a "Sending Complete" message is displayed.

[1364] Step 4:

[1365] Analysis of audio data

[1366] The server automatically launches a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to analyze the received audio data.

[1367] Input: Audio data sent to the server

[1368] Data processing: Analyze audio data and convert it into text data.

[1369] Output: Text data

[1370] Specific operation: The server receives the audio file and calls the Google Cloud Speech-to-Text API to begin analyzing the audio data. The processing status is displayed on the dashboard.

[1371] Step 5:

[1372] Convert to text data

[1373] The server converts the analyzed audio data into text data. The converted text data is stored in a database on the server.

[1374] Input: Audio data analyzed by a speech recognition engine

[1375] Data manipulation: Converting audio data to text.

[1376] Output: Text data (e.g., .txt format)

[1377] Specific operation: The server converts audio files into text data and saves that data to a database. A preview of the conversion results can be viewed on the dashboard.

[1378] Step 6:

[1379] Extraction of emotional data

[1380] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to extract the user's emotions based on the text data and adds emotion labels to the text data.

[1381] Input: Text data

[1382] Data processing: Identify emotions from text data and add emotion labels.

[1383] Output: Text data with emotion labels attached.

[1384] Specific operation: The server inputs text data into the emotion recognition engine and generates text data with emotion labels such as "anger" and "sadness" attached.

[1385] Step 7:

[1386] Automatic report generation

[1387] The server automatically generates a report based on text data and sentiment data. The generated report is sent to the user's (teacher's) terminal.

[1388] Input: Text data with emotion labels attached

[1389] Data processing: Generate reports using pre-configured report templates.

[1390] Output: Initial Report

[1391] Specific operation: The server uses a template to generate a report such as, "Today, XX and YY had a fight (anger). The cause was XX and YY," and sends it to the user's terminal.

[1392] Step 8:

[1393] Editing the report

[1394] The user (teacher) reviews the report using a dedicated app and edits the necessary parts. Once editing is complete, they resubmit the report to the server.

[1395] Input: Initial report

[1396] Data processing: Users edit reports and resend them to the server.

[1397] Output: Edited report

[1398] Specific actions: The user reviews the report, adds an "additional comment" to the input field, saves the edits, and then resends the report to the server.

[1399] Step 9:

[1400] Sending reports to parents and related users

[1401] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[1402] Input: Edited report

[1403] Data processing: Send the report to parents and relevant teachers.

[1404] Output: Sent reports and push notifications

[1405] Specific operation: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements".

[1406] Step 10:

[1407] Sharing among teachers

[1408] The server will also share the report with other relevant teachers (e.g., homeroom teachers, school nurses, grade level heads) as needed.

[1409] Input: Report data

[1410] Data processing: Automatically send reports to relevant faculty members.

[1411] Output: Shared report

[1412] Specific operation: The server sends the report to the homeroom teacher and school nurse, and sends a notification to each teacher's terminal stating, "A new report has been shared."

[1413] (Application Example 2)

[1414] 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."

[1415] In recent years, there has been a growing demand for improved education and customer service in environments such as classrooms and physical stores by utilizing voice recording and emotion recognition technologies. However, existing systems suffer from insufficient accuracy in transcribing voice data and recognizing emotions, and also lack the functionality to automatically generate reports using this data effectively. As a result, it is difficult for teachers and store managers to quickly and accurately grasp the necessary information, hindering improvements in operational efficiency.

[1416] 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. In this invention, the server includes a voice analysis means that analyzes voice data and converts it into text data, an emotion recognition means that recognizes emotions and generates emotion data, and a report generation means that automatically generates a report based on the text data and emotion data. This makes it possible to obtain highly accurate text data and emotion data from voice data and to automatically generate a report based on them.

[1417] "Voice recording means" refers to a device or system for recording sound in an environment such as a classroom or a physical store.

[1418] "Voice data transmission means" refers to a device or system for transmitting recorded voice data to a server.

[1419] "Voice analysis means" refers to a device or system that analyzes voice data received by a server and converts that voice data into text data.

[1420] "Emotion recognition means" refers to a device or system that recognizes a user's emotions from voice data and generates emotion data.

[1421] "Report generation means" refers to a device or system that automatically generates a report based on acquired text data and sentiment data.

[1422] "Report editing means" refers to a device or system that sends the generated report to a terminal in a format that can be edited by the user.

[1423] "Report transmission means" refers to a device or system for resending reports edited by users to a server.

[1424] "Notification means" refers to a device or system that transmits the edited final report to parents and relevant users.

[1425] "Text data" refers to data in text format obtained as a result of analyzing and converting audio data.

[1426] "Emotional data" refers to data that represents the user's emotions as recognized from voice data.

[1427] Modes for carrying out the invention

[1428] A system for carrying out this invention includes the following components:

[1429] 1. Audio recording means:

[1430] Users (teachers and store operators) record ambient sounds in classrooms and physical stores using smartphones or dedicated devices. For example, when a teacher wants to record students' comments or questions during class, they launch a dedicated application and tap the record button to start audio recording.

[1431] 2. Means for transmitting audio data:

[1432] Once recording is complete, the audio data is temporarily stored on the device and then sent to the server. This transmission requires an internet connection. For example, if you tap the "Send" button after recording is finished, the audio file will be automatically uploaded to the server.

[1433] 3. Voice analysis means:

[1434] The server analyzes the received audio data using speech recognition technology and converts it into text data. Machine learning models such as the Google Speech-to-Text API are used for this process. The analyzed text data then proceeds to the next processing step.

[1435] 4. Emotion recognition means:

[1436] On the server, emotion data is generated using emotion recognition technology based on text data. This is done by analyzing the tone of voice and the content of the speaker's speech. For example, if the word "thank you" is included, it will be recognized as "happy".

[1437] 5. Report generation means:

[1438] Using the analyzed text and sentiment data, a report is automatically generated. This report is created based on a pre-configured report template. For example, the report might be in the format, "Today, [Name] is experiencing sadness."

[1439] 6. Report editing methods:

[1440] The generated report is sent to the user's device in an editable format. The user can review the report and enter any necessary corrections or additional information. The edited report is then sent back to the server.

[1441] 7. Method of sending the report:

[1442] The server sends the edited report to parents and other relevant parties. A push notification feature is used to inform recipients that a new report has arrived. For example, tapping the "Notify Parents" button sends the report to the parent's app and displays a push notification.

[1443] 8. Means of notification:

[1444] Once the report has been submitted, the system sends a notification to the recipient using a notification method. For example, to make it easier for parents to check the report, they may be notified via push notification on their smartphone that a new report has arrived.

[1445] Hardware and software to be used

[1446] Hardware:

[1447] Smartphone, dedicated recording terminal, server

[1448] Software / Tools:

[1449] Voice recording application

[1450] Internet connection

[1451] Google Speech-to-Text API (speech recognition technology using machine learning models)

[1452] Emotion recognition engine (proprietary software)

[1453] Report generation and editing software

[1454] User application with push notification functionality

[1455] Examples of specific cases and prompt statements

[1456] For example, consider a scenario in a store where staff explain products to customers and express their gratitude. The system automatically analyzes this audio data and generates a report containing sentences like the following:

[1457] "This product is very popular. Let me explain how to use it... Thank you!"

[1458] Example of a report generated based on this:

[1459] Customer Feedback:

[1460] This product is very popular. Let me explain how to use it. ...Thank you!

[1461] Emotion: happy”

[1462] Example of a prompt

[1463] "Convert the following audio data to text and analyze the customer's emotions. If the conversation includes the word 'thank you,' output 'emotion: happy.'"

[1464] In this way, teachers and store managers can easily generate reports based on voice and sentiment data, and streamline communication with stakeholders.

[1465] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1466] Step 1:

[1467] Users record audio using their smartphones or dedicated devices. Specifically, teachers or store managers launch the application and tap the record button, which activates the device's microphone and records ambient sounds. The input is conversational audio from the classroom or store, and the output is a recorded audio data file.

[1468] Step 2:

[1469] The device temporarily stores the audio data and sends it to the server via the internet. Specifically, when you tap the "Send" button after recording is finished, the audio data file is uploaded to the server. The input is the recorded audio data file, and the output is the audio data sent to the server.

[1470] Step 3:

[1471] The server analyzes the received audio data and converts it into text data. Specifically, the audio data is converted into text data using speech recognition technology such as the Google Speech-to-Text API. The input is the audio data sent to the server, and the output is the converted text data.

[1472] Step 4:

[1473] The server inputs the converted text data into an emotion recognition engine to generate emotion data. Specifically, the text data is analyzed by the emotion recognition engine, and the user's emotion is identified. For example, if the text data contains "thank you," it will be recognized as "happy." The input is the text data obtained from speech analysis, and the output is the generated emotion data.

[1474] Step 5:

[1475] The server generates a report based on text data and sentiment data. Specifically, text data and sentiment data are embedded in a pre-configured report template. The input is text data and sentiment data, and the output is the generated report.

[1476] Step 6:

[1477] The server sends the generated report to the user's terminal, and the user edits the report. Specifically, the user can review the report content and enter corrections or additional information as needed. The input is the generated report, and the output is the report edited by the user.

[1478] Step 7:

[1479] The device resends the edited report to the server. Specifically, tapping the "Save" button uploads the edited report to the server. The input is the report edited by the user, and the output is the edited report sent to the server.

[1480] Step 8:

[1481] The server sends the edited report to parents and relevant users and sends notifications. Specifically, it uses a push notification function to inform recipients that a new report has arrived. The input is the edited report, and the output is the report sent to parents and relevant users, along with the push notification.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] [Fourth Embodiment]

[1486] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1487] 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.

[1488] 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).

[1489] 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.

[1490] 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.

[1491] 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).

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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".

[1499] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[1500] System Examples

[1501] 1. Recording and transmitting audio data

[1502] Device (Teacher's device)

[1503] The teacher records classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[1504] Specific example:

[1505] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[1506] 2. Analysis and transcription of audio data

[1507] server

[1508] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. A machine learning model is used for speech recognition, applying a model that is strong in everyday conversation and specific terminology. The server then analyzes this audio data and converts it back into text data.

[1509] Specific example:

[1510] When the server receives an audio file, it automatically starts the speech recognition engine. The engine analyzes the audio and generates the corresponding text. For example, the audio "The fight that just happened was between A and B" is transcribed as "The fight that just happened was between A and B."

[1511] 3. Report generation and editing

[1512] server

[1513] The system automatically generates a report by applying text data to a report template. The generated report is then sent to the teacher's device.

[1514] User (Teacher)

[1515] Teachers can review the report and edit the necessary parts. For example, they can add specific details about the situation or additional information. The edited report is then sent back to the server.

[1516] Specific example:

[1517] The server embeds text data into a report template and generates a report stating, "Today, XX and YY had a fight. The cause was XX and YY." This report is sent to the teacher's terminal, where the teacher can add additional comments.

[1518] 4. Sending reports and notifications to parents / guardians

[1519] server

[1520] The edited final report is sent to the parents. The report sent will include a push notification so that parents are immediately notified when a new report is received.

[1521] Specific example:

[1522] The server sends the edited report to the parent's app and simultaneously sends a push notification. When the parent opens the app, the report will appear in the "Today's Announcements" section.

[1523] 5. Sharing among teachers

[1524] server

[1525] Share the report with other relevant teachers as needed. For example, send the report to the homeroom teacher, school nurse, grade level head, etc., to facilitate information sharing among teachers.

[1526] Specific example:

[1527] If the server is configured to share the same report with other relevant faculty members, the report will be automatically sent to each of their devices.

[1528] The above is a specific embodiment of the present invention, a system that streamlines communication between teachers and parents and reduces the burden on both parties by seamlessly performing a series of steps from recording audio data to automatically generating, editing, and sending reports.

[1529] The following describes the processing flow.

[1530] Step 1:

[1531] User (Teacher)

[1532] The teacher launches the dedicated app and selects the recording function. Tapping the record button prompts the app to request access to the microphone, and recording begins. The teacher records the necessary scenes and then taps the end-of-recording button.

[1533] Step 2:

[1534] Device (Teacher's device)

[1535] Once recording is complete, the audio data is temporarily stored on the device. The stored audio data is then automatically uploaded to a server via the internet.

[1536] Step 3:

[1537] server

[1538] The server receives the audio data. Upon receiving the audio data, the speech recognition engine within the server starts up and converts the audio data into text data. The speech recognition technology employs a machine learning model to analyze and convert the data with high accuracy.

[1539] Step 4:

[1540] server

[1541] Based on the converted text data, the automated report generation module generates a report. The text data is embedded in a pre-configured report template, and the initial report is created.

[1542] Step 5:

[1543] server

[1544] The initial generated report is sent to the teacher's device. A notification function is used to inform the teacher so they can receive and review the report.

[1545] Step 6:

[1546] User (Teacher)

[1547] The teacher reviews the report and edits it as needed. For example, they might add specific details or additional information. Once editing is complete, the teacher saves the edited report and resubmits it to the server.

[1548] Step 7:

[1549] Device (Teacher's device)

[1550] Upload the edited report data to the server.

[1551] Step 8:

[1552] server

[1553] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[1554] Step 9:

[1555] User (Parent / Guardian)

[1556] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[1557] Step 10:

[1558] server

[1559] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[1560] (Example 1)

[1561] 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".

[1562] Traditional methods for recording classroom events and creating reports involved a great deal of manual work, placing a significant burden on both teachers and parents. Furthermore, manual recording and report creation were time-consuming, and the accuracy of the information could not always be guaranteed. Moreover, in today's world, where efficiency and speed in reporting are crucial, existing systems have failed to adequately meet these needs. Therefore, this invention provides a system for automatically analyzing audio data and automatically generating reports to solve these problems.

[1563] 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.

[1564] In this invention, the server includes a speech recognition engine control means that, after receiving audio data, activates a speech recognition engine and performs a process of converting the audio data into text data; an automatic report generation means that applies the text data based on the audio data to a report template; and a report sharing means that shares the generated report with other faculty and staff. This eliminates the need to manually convert audio data to text, enabling the rapid and accurate generation and sharing of reports.

[1565] "Audio recording means" refers to devices or functions for recording audio in real time in environments such as classrooms.

[1566] "Voice data transmission means" refers to a function for transmitting voice data obtained through recording to a server via the internet.

[1567] "Speech analysis means" refers to a function that performs the process of analyzing the received speech data on the server and converting it into text data.

[1568] The "report generation method" is a function that automatically generates a report by applying the analyzed text data to a report template.

[1569] The "report editing means" is a function that sends the generated report to the terminal in a format that the user can edit.

[1570] The "report transmission method" refers to the function of resending the edited report to the server.

[1571] "Notification method" refers to a function that sends the edited final report to parents and relevant users and notifies them of its receipt.

[1572] The "speech recognition engine control means" is a function that activates the speech recognition engine after receiving speech data and executes the process of converting the speech data into text data.

[1573] The "automatic report generation method" is a function that automatically generates a report by applying text data to a report template based on audio data.

[1574] "Report sharing means" refers to a function for sharing generated reports with other faculty and staff members.

[1575] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The aim of this system is to streamline communication between teachers and parents and reduce the burden on both parties.

[1576] Hardware and software to be used

[1577] Device (Teacher's device)

[1578] This system uses devices such as smartphones and tablets as terminals. These devices have dedicated apps installed that provide functions such as recording, saving, and transmitting audio.

[1579] server

[1580] Many processes, such as receiving, analyzing, storing, generating reports on, and sharing audio data, are performed by the server. The server has high processing power and runs multiple software components, including a speech recognition engine and a database.

[1581] Speech recognition engine

[1582] To convert audio data into text data, a machine learning-based speech recognition engine (e.g., Google Cloud Speech-to-Text API) is used.

[1583] Report generation template

[1584] When generating reports based on text data, predefined templates are used. This makes it easy to create reports in a consistent format.

[1585] Data processing and calculation

[1586] Voice recording

[1587] The user (teacher) operates the audio recording device through a dedicated app to record the sound in the classroom. When recording begins, the device's microphone activates, recording ambient sounds in real time.

[1588] Storing and transmitting audio data

[1589] When the teacher's device finishes recording audio, the audio data is temporarily stored in local storage and then uploaded to the server via the internet.

[1590] Start the speech recognition engine

[1591] When the server receives audio data, it uses the speech recognition engine control means to activate the speech recognition engine and convert the audio data into text data.

[1592] Automatic report generation

[1593] Based on the converted text data, the automated report generation system embeds the data into a template and automatically generates the report.

[1594] Sharing of reports

[1595] The server not only sends the generated reports to the teacher's device but also shares them with other faculty members' devices as needed.

[1596] Specific example

[1597] Specific examples of audio data recording:

[1598] When the teacher taps the record button in the app, the device activates the microphone to record the classroom environment and the children's conversations.

[1599] Specific examples of sending audio data:

[1600] After recording is complete, the device uploads the audio data to a server via the internet. An HTTP POST request is used for the upload.

[1601] Specific examples of audio data analysis and transcription:

[1602] When the server receives an audio file, it automatically starts the speech recognition engine. For example, the audio "The fight that just happened was between A and B" is converted to text "The fight that just happened was between A and B."

[1603] Example of a prompt

[1604] Example of a prompt:

[1605] "I want to create an app that records events that occur in the classroom and saves them as audio data. Please explain what kind of mechanism is needed to implement a system that sends the recorded audio data to a server and converts the audio data into text. Furthermore, please explain in detail the process of automatically generating, editing, and sending the generated text data as a report."

[1606] The system described above allows for a seamless workflow from recording audio data to automatically generating, editing, and sending reports, thereby streamlining communication between teachers and parents.

[1607] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1608] Step 1:

[1609] The user taps the record button in the dedicated app.

[1610] Input: User tap operation

[1611] Operation: When the user taps the app's record button, the device's microphone is activated, and ambient sounds are recorded in real time. At the time recording begins, metadata (start time, location information, etc.) is also recorded.

[1612] Output: Audio files and metadata stored on the device

[1613] Step 2:

[1614] After recording is complete, the device temporarily saves the audio data to local storage.

[1615] Input: Action to end recording

[1616] Operation: When recording ends, the audio data is saved to the device's local storage. Important metadata (start time, end time, device ID, etc.) is also saved at this time.

[1617] Output: Audio files and metadata saved to local storage

[1618] Step 3:

[1619] The device uploads the stored audio data to the server via the internet.

[1620] Input: Audio files and metadata stored in local storage

[1621] Operation: The terminal sends audio data to the server using an HTTP POST request. In this process, a request containing the audio file and associated metadata is created.

[1622] Output: Audio files and metadata sent to the server

[1623] Step 4:

[1624] The server receives the audio data and starts the speech recognition engine.

[1625] Input: Audio files and metadata sent to the server

[1626] Operation: When the server receives an HTTP POST request, it saves the audio file to a temporary directory and starts a speech recognition engine (e.g., Google Cloud Speech-to-Text API).

[1627] Output: Speech recognition engine is operational.

[1628] Step 5:

[1629] The server uses a speech recognition engine to convert the audio data into text data.

[1630] Input: Audio files saved in a temporary directory

[1631] Operation: The speech recognition engine analyzes the audio data and generates corresponding text data. In this process, specific keywords and phrases are converted into text data.

[1632] Output: Generated text data

[1633] Step 6:

[1634] The server applies the generated text data to the report template to automatically generate the report.

[1635] Input: Generated text data

[1636] Operation: The server automatically generates a report by embedding text data into a predefined report template. Important information (date, time, location, stakeholders, etc.) is included in the report.

[1637] Output: Automated report

[1638] Step 7:

[1639] The server sends the generated report to the teacher's terminal.

[1640] Input: Automated report

[1641] Operation: The server uses email and push notifications to send the generated report to the teacher's device. Along with the report, an editing link and an in-app notification are sent.

[1642] Output: Report sent to the teacher's terminal

[1643] Step 8:

[1644] The user (teacher) reviews the report and edits it as needed.

[1645] Input: Report sent to the teacher's terminal

[1646] Operation: The user opens the report in the app and reviews its contents. They can add comments or make edits as needed. The edited report is then saved.

[1647] Output: Edited report

[1648] Step 9:

[1649] The terminal resends the edited report to the server.

[1650] Input: Edited report

[1651] Operation: Once editing is complete, the report is sent back to the server using an HTTP POST request.

[1652] Output: Edited report sent to the server

[1653] Step 10:

[1654] The server sends the edited final report to the parents and sends a push notification.

[1655] Input: Edited report sent to the server

[1656] Operation: The server sends the final report to the parent's device and simultaneously sends a push notification. This immediately notifies the parent that a new report has been received.

[1657] Output: Final report and push notification sent to the parent's device.

[1658] Step 11:

[1659] The server shares the generated report with other faculty and staff members.

[1660] Input: Automated or edited report

[1661] Operation: The server also sends the report to other relevant faculty members (e.g., homeroom teacher, school nurse, grade level head). This transmission also uses HTTP POST requests or email.

[1662] Output: Report sent to other faculty members' terminals

[1663] (Application Example 1)

[1664] 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".

[1665] In current classroom and retail environments, recording audio data and creating reports based on it is time-consuming and labor-intensive. Furthermore, the process of sharing these reports among stakeholders is cumbersome, making it particularly difficult to quickly share troubleshooting information. This increases the burden on teachers and technicians, hindering efficient information dissemination.

[1666] 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.

[1667] In this invention, the server includes: an audio recording means for recording audio in an environment such as a classroom; an audio data transmission means for transmitting the recorded audio data to the server; an audio analysis means for analyzing the audio data on the server and converting it into text data; a report generation means for automatically generating a report based on the text data; a report editing means for transmitting the generated report to a terminal in a user-editable format; a report transmission means for retransmitting the edited report to the server; a notification means for sending the final edited report to parents and relevant users; a technician support means for recording audio data in a physical store and generating a troubleshooting report based on it; and a technician information sharing means for sharing the generated report with relevant technicians. This enables efficient report creation and information sharing from audio data.

[1668] "Audio recording means" refers to devices or software used to record sounds that occur in environments such as classrooms or physical stores.

[1669] "Voice data transmission means" refers to a means for transmitting recorded voice data to a server via a network.

[1670] "Voice analysis means" refers to a technology that analyzes voice data sent to a server and converts it into text data.

[1671] The "report generation means" is a means of automatically creating a report using text data generated by the speech analysis means.

[1672] "Report editing means" refers to a means for sending the generated report to a terminal in a format that can be edited by the user.

[1673] "Report transmission means" refers to the means by which a user can resend an edited report to the server.

[1674] "Notification method" refers to the means of sending the edited final report to parents and relevant users for notification.

[1675] "Technical support methods" refer to methods for recording voice data in physical stores and generating troubleshooting reports based on that data.

[1676] A "means for sharing engineer information" refers to a method for sharing generated reports with relevant engineers.

[1677] This invention is a system that records audio in environments such as classrooms and physical stores, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. The main components include audio recording means, audio data transmission means, audio analysis means, report generation means, report editing means, report transmission means, notification means, engineer support means, and engineer information sharing means.

[1678] This system uses the following hardware and software:

[1679] Smartphones and smart glasses are used as means of voice recording. This allows users to easily record conversations and ambient sounds.

[1680] As a means of speech analysis, the Google Web Speech API is used to convert speech data into text data.

[1681] A web application using the Django framework will be used as the means for generating and editing reports.

[1682] As a means of supporting engineers and sharing engineer information, the system enables the automatic generation of troubleshooting reports based on audio data and their sharing with relevant engineers.

[1683] Voice recording and transmission

[1684] Users (classroom teachers or store technicians) record audio through a dedicated app installed on their smartphones or smart glasses. When recording begins, the device's microphone activates and records ambient sounds. Once recording is complete, the audio data is temporarily stored on the device and then transmitted to a server via the internet.

[1685] Audio data analysis and transcription

[1686] When the server receives audio data, the speech recognition engine (Google Web Speech API) is activated and converts the audio data into text data. The converted text is then used for subsequent processing.

[1687] Report generation and editing

[1688] Based on the generated text data, the server automatically generates a report. A report template is used to allow users to easily fill in specific information. The generated report is sent to the user's terminal, where the user can enter additional information and edit the report as needed.

[1689] Sending reports to parents and relevant technicians

[1690] The edited final report is resent to the server. The server sends the final report to the parent or relevant technician and uses a notification system to inform them that a new report has been received. Furthermore, a technician information sharing system allows the report to be shared among relevant technicians, enabling rapid troubleshooting.

[1691] Specific example

[1692] For example, in a classroom, a recording might be made stating, "Today, Mr. Tanaka taught a math class. During the class, two students got into a fight." This recording is then converted into text data, generating a report like this: "Today, during Mr. Tanaka's math class, students A and B got into a fight. The cause was a dispute over seats."

[1693] Example of a prompt

[1694] Examples of prompts to input into a generative AI model are as follows:

[1695] "Based on this week's technical support report, please list the main troubleshooting issues in bullet points."

[1696] As a result, this system enables efficient report creation and information sharing from audio data, significantly reducing the burden on classrooms and physical stores.

[1697] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1698] Step 1:

[1699] The user (teacher or technician) launches a dedicated app installed on their smartphone or smart glasses and taps the record button. The input is voice, which is collected through the device's microphone. The recorded voice data is temporarily stored on the device.

[1700] Step 2:

[1701] The device transmits the recorded audio data to the server via the internet. The input is the recorded audio data, and the output is the audio data uploaded to the server.

[1702] Step 3:

[1703] The server analyzes the received audio data using speech analysis tools. Specifically, it uses the Google Web Speech API to convert the audio data into text data. The input is audio data, and the output is the analyzed text data.

[1704] Step 4:

[1705] The server automatically generates a report using a report generation device based on the text data obtained by the speech analysis device. The input is text data, and the output is an automatically generated report.

[1706] Step 5:

[1707] The server sends the generated report to the terminal, and the user reviews and edits the report through a dedicated app. The input is an automatically generated report, and the output is the edited report. The user can enter additional information and edit the report as needed.

[1708] Step 6:

[1709] The terminal resends the edited report to the server. The input is the edited report, and the output is the edited report resent to the server.

[1710] Step 7:

[1711] The server sends the edited final report to parents and relevant technicians and uses notification methods to inform them that the new report has been delivered. The input is the edited final report, and the output is the notification and report received by parents and relevant technicians.

[1712] Step 8:

[1713] The server uses a technical information sharing system to share the generated report with other relevant technical personnel. The input is the final report, and the output is the report sent to the relevant technical personnel's terminals.

[1714] Each step aims to efficiently generate and share reports automatically from audio data, thereby reducing the burden on users.

[1715] 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.

[1716] This invention relates to a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by incorporating an emotion engine, this system also has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[1717] System Examples

[1718] 1. Recording and transmitting audio data

[1719] User (Teacher)

[1720] The teacher records classroom audio through a dedicated app. When recording begins, the device's microphone activates and records ambient sounds. After recording ends, the audio data is temporarily stored on the device. Next, the stored audio data is uploaded to a server via the internet.

[1721] Specific example:

[1722] When the user (teacher) taps the record button in the app, the device activates the microphone and starts recording audio. After recording is complete, the device uploads the audio data to the server.

[1723] 2. Analysis and transcription of audio data

[1724] server

[1725] When the server receives audio data, the speech recognition engine starts and converts the audio data into text. The speech recognition technology uses machine learning models to analyze and convert the data with high accuracy. The server then analyzes this audio data and converts it back into text data.

[1726] Emotional Engine

[1727] The server inputs the received audio data into the emotion engine, which recognizes the user's emotions from the voice. The emotion engine identifies emotions by analyzing voice tone, word choice, and other acoustic characteristics. The emotion data is added to the text data and used to create reports.

[1728] Specific example:

[1729] When the server receives an audio file, it automatically activates the speech recognition engine and emotion engine. The engines analyze the audio and generate corresponding text, while the emotion engine identifies emotions such as "anger" and "sadness" and adds emotion data to the text. For example, "The fight that just happened was between A and B" is converted to "The fight that just happened was between A and B (anger)."

[1730] 3. Report generation and editing

[1731] server

[1732] Based on the converted text data and sentiment data, the automated report generation module generates a report. The text data and sentiment data are embedded in a pre-configured report template, and the initial report is created.

[1733] User (Teacher)

[1734] Teachers can review the report and edit the necessary sections. Sentiment data is also reflected in the report, and they can enter specific situational descriptions and additional information. Once editing is complete, teachers save the edited report and resubmit it to the server.

[1735] Specific example:

[1736] The server embeds text and sentiment data into a report template and generates a report that reads, "Today, XX and YY had a fight (anger). The cause was XX and YY." This report is sent to the teacher's terminal, where the teacher can add additional comments.

[1737] 4. Sending reports and notifications to parents / guardians

[1738] server

[1739] The edited final report is sent to the parents. The report sent will include a push notification so that parents are immediately notified when a new report is received.

[1740] Specific example:

[1741] The server sends the edited report to the parent's app and simultaneously sends a push notification. When the parent opens the app, the report appears in the "Today's Announcements" section, and includes emotional information such as, "It seemed like [child's name] was feeling sad today."

[1742] 5. Sharing among teachers

[1743] server

[1744] Share the report with other relevant teachers as needed. For example, send the report to the homeroom teacher, school nurse, grade level head, etc., to facilitate information sharing among teachers.

[1745] Specific example:

[1746] If the server is configured to share the same report with other relevant faculty members, the report will be automatically sent to each of their devices.

[1747] The above is a specific embodiment of the present invention, a system that streamlines communication between teachers and parents and reduces the burden on both parties by seamlessly performing a series of steps from recording audio data to automatically generating, editing, and sending reports. Furthermore, by combining it with an emotion engine, it is possible to understand the user's emotions in more detail and provide appropriate feedback.

[1748] The following describes the processing flow.

[1749] Step 1:

[1750] User (Teacher)

[1751] The teacher launches the dedicated app and selects the recording function. Tapping the record button prompts the app to request access to the microphone, and recording begins. The teacher records the necessary scenes and then taps the end-of-recording button.

[1752] Step 2:

[1753] Device (Teacher's device)

[1754] Once recording is complete, the audio data is temporarily stored on the device. The stored audio data is then automatically uploaded to a server via the internet.

[1755] Step 3:

[1756] server

[1757] The server receives the audio data and starts the speech recognition engine. The audio data is then converted into text data. This allows the audio recording to be output as text.

[1758] Step 4:

[1759] server

[1760] The server also inputs the received audio data into the emotion engine, which recognizes the user's emotions from the voice. The emotion engine analyzes the tone of voice, word choice, and other acoustic characteristics to identify emotions and adds that data to the text data.

[1761] Step 5:

[1762] server

[1763] Based on the converted text data and sentiment data, the automated report generation module generates a report. The text data and sentiment data are embedded in a pre-configured report template, and the initial report is created.

[1764] Step 6:

[1765] server

[1766] The initial generated report is sent to the teacher's device. A notification function is used to inform the teacher so they can receive and review the report.

[1767] Step 7:

[1768] User (Teacher)

[1769] The teacher reviews the report and edits the necessary parts. Since sentiment data is also reflected in the report, the teacher enters specific situational descriptions and additional information. Once editing is complete, the teacher saves the edited report and resubmits it to the server.

[1770] Step 8:

[1771] Device (Teacher's device)

[1772] Upload the edited report data to the server.

[1773] Step 9:

[1774] server

[1775] Prepare to send the edited final report to the parent's device. Use push notifications to inform the parent that a new report has arrived.

[1776] Step 10:

[1777] User (Parent / Guardian)

[1778] A push notification is sent to the parent's device, and when they open the app, the new report is displayed. The parent can review the report and contact the teacher if necessary.

[1779] Step 11:

[1780] server

[1781] If necessary, the report will also be sent to other relevant faculty members. Once the sharing settings are confirmed, the report will be processed so that it is delivered to the relevant faculty members' devices.

[1782] (Example 2)

[1783] 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".

[1784] Traditionally, recording and analyzing audio data in classrooms and other environments was often done manually, requiring considerable time and effort. Furthermore, report creation was primarily manual, leading to problems with accuracy and consistency. Additionally, there was a lack of technology to identify emotions from audio data and reflect them in reports, highlighting the need for more efficient communication between teachers and parents.

[1785] 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.

[1786] In this invention, the server includes a voice analysis means for analyzing voice data and converting it into text data, an emotion recognition means for extracting emotion data from the text data converted by the voice analysis means, and a report generation means for automatically generating a report based on the text data and emotion data. This allows the user to automatically perform the entire process from recording voice data to generating, editing, and sending reports, significantly improving the efficiency of communication between teachers and parents.

[1787] "Sound recording means" refers to a device or system for recording sounds within an environment.

[1788] "Audio data storage means" refers to a device or system for temporarily storing recorded audio data.

[1789] "Voice data transmission means" refers to a device or system for transmitting stored voice data to a server.

[1790] "Speech analysis means" refers to a device or system for analyzing received speech data and converting it into text data.

[1791] "Emotion recognition means" refers to a device or system for extracting a user's emotions from text data converted by speech analysis means.

[1792] A "report generation means" is a device or system for automatically generating reports based on text data and sentiment data.

[1793] "Report editing means" refers to a device or system for sending a generated report to a terminal in a format that can be edited by the user.

[1794] "Report transmission means" refers to a device or system for resending an edited report to a server.

[1795] "Notification means" refers to a device or system for sending the edited final report to parents and relevant users and notifying them of this fact.

[1796] "Information sharing means" refers to a device or system for sharing generated reports among other relevant users.

[1797] This invention is a system that records audio in environments such as classrooms, transmits the recorded audio data to a server, analyzes the audio data on the server, converts it into text data, and automatically generates a report based on that text data. Furthermore, by combining this system with an emotion recognition engine, it has the function of recognizing the user's emotions and reflecting the emotion data in the report. This system streamlines communication between teachers and parents and reduces the burden on both parties.

[1798] Hardware and software

[1799] User (Teacher)

[1800] Teachers use a dedicated application (e.g., a custom app for iOS or Android) to record audio in the classroom. Recording is done using the microphone on a smartphone or tablet.

[1801] terminal

[1802] The device temporarily stores the recorded data in its internal storage and uploads the data to the server via an internet connection.

[1803] server

[1804] The server analyzes speech data using speech analysis and sentiment recognition technologies such as Google Cloud Speech-to-Text API and IBM Watson Tone Analyzer, and converts it into text data. Based on the converted text data and sentiment data, it automatically generates a report using a text generation library written in Python and sends it to the user.

[1805] Specific explanation of system operation

[1806] 1. Recording audio data

[1807] The user (teacher) launches the dedicated app and taps the record button. This activates the device's microphone and records the audio in the classroom.

[1808] Specific example: When the user taps the "Start Recording" button in the app, the device turns on the microphone and begins recording audio data.

[1809] 2. Temporary storage and transmission of recorded data to the server

[1810] Once recording is complete, the device temporarily saves the audio data to its internal storage. It then uploads the audio data to the server via Wi-Fi or mobile data.

[1811] Specific example: After recording is complete, the device saves the audio file to its internal storage and displays a notification to the user stating, "Recording saved." The saved audio data is then uploaded to the server.

[1812] 3. Analysis and transcription of audio data

[1813] The server converts the received audio data into text data using the Google Cloud Speech-to-Text API.

[1814] Specific example: When the server receives an audio file, it automatically starts the speech recognition engine and converts the audio to text.

[1815] 4. Extraction of emotional data

[1816] The server uses IBM Watson Tone Analyzer to extract sentiment data from the converted text data and adds sentiment labels to the text data.

[1817] Specific example: An emotion recognition engine identifies emotions such as "anger" and "sadness," and adds emotion labels to the text data, such as "The argument earlier was between A and B (anger)."

[1818] 5. Automatic generation and user editing of reports

[1819] The server generates an initial report using a pre-configured report template based on text and sentiment data. The generated report is sent to the user's (teacher's) terminal and can be edited as needed. Once editing is complete, the teacher sends the edited report back to the server.

[1820] Specific example: The server embeds text and sentiment data into a report template and sends the report to the teacher's terminal. The teacher adds necessary comments, saves the edited report, and then resends it to the server.

[1821] 6. Sending reports to parents and relevant users.

[1822] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[1823] Specific example: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements."

[1824] Example of a prompt

[1825] 1. "Please record the audio from the classroom and send it to the server."

[1826] 2. "Convert the audio file to text and extract the sentiment data."

[1827] 3. "Generate a report based on the extracted text data and sentiment data."

[1828] 4. Edit the generated report and send it to the parents.

[1829] The above describes an embodiment of the present invention, which enables a seamless workflow from recording audio data to generating, editing, and transmitting reports. This workflow streamlines communication between teachers and parents, reducing the burden on both parties. Furthermore, by combining this with emotion recognition technology, it is possible to reflect the user's emotional information in the report, providing more detailed and useful information.

[1830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1831] Step 1:

[1832] Audio data recording

[1833] When the user (teacher) launches the dedicated app and taps the record button, the device's microphone activates and records the audio in the classroom.

[1834] Input: User's tap of the recording button

[1835] Data processing: The device's microphone picks up ambient sounds and generates audio data.

[1836] Output: Audio data (e.g., .wav format)

[1837] Specific operation: Recording begins when the user taps the "Start Recording" button in the app, and the recording time is displayed on the app screen.

[1838] Step 2:

[1839] Temporary storage of recording data

[1840] Once recording is complete, the device temporarily saves the recorded audio data to its internal storage.

[1841] Input: Recording termination operation (user taps the record button)

[1842] Data processing: The generated audio data is saved to the device's internal storage.

[1843] Output: Temporarily saved audio data

[1844] Specific action: Tapping the record button again will end the recording and a pop-up notification will appear stating, "Recording saved."

[1845] Step 3:

[1846] Sending audio data to the server

[1847] The device uploads the stored audio data to the server via an internet connection.

[1848] Input: Temporarily stored audio data

[1849] Data processing: Upload audio data to the server.

[1850] Output: Audio data sent to the server

[1851] Specific operation: The device uploads the audio file to the server using Wi-Fi or mobile data, and the progress is displayed with a progress bar. Once complete, a "Sending Complete" message is displayed.

[1852] Step 4:

[1853] Analysis of audio data

[1854] The server automatically launches a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to analyze the received audio data.

[1855] Input: Audio data sent to the server

[1856] Data processing: Analyze audio data and convert it into text data.

[1857] Output: Text data

[1858] Specific operation: The server receives the audio file and calls the Google Cloud Speech-to-Text API to begin analyzing the audio data. The processing status is displayed on the dashboard.

[1859] Step 5:

[1860] Convert to text data

[1861] The server converts the analyzed audio data into text data. The converted text data is stored in a database on the server.

[1862] Input: Audio data analyzed by a speech recognition engine

[1863] Data manipulation: Converting audio data to text.

[1864] Output: Text data (e.g., .txt format)

[1865] Specific operation: The server converts audio files into text data and saves that data to a database. A preview of the conversion results can be viewed on the dashboard.

[1866] Step 6:

[1867] Extraction of emotional data

[1868] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to extract the user's emotions based on the text data and adds emotion labels to the text data.

[1869] Input: Text data

[1870] Data processing: Identify emotions from text data and add emotion labels.

[1871] Output: Text data with emotion labels attached.

[1872] Specific operation: The server inputs text data into the emotion recognition engine and generates text data with emotion labels such as "anger" and "sadness" attached.

[1873] Step 7:

[1874] Automatic report generation

[1875] The server automatically generates a report based on text data and sentiment data. The generated report is sent to the user's (teacher's) terminal.

[1876] Input: Text data with emotion labels attached

[1877] Data processing: Generate reports using pre-configured report templates.

[1878] Output: Initial Report

[1879] Specific operation: The server uses a template to generate a report such as, "Today, XX and YY had a fight (anger). The cause was XX and YY," and sends it to the user's terminal.

[1880] Step 8:

[1881] Editing the report

[1882] The user (teacher) reviews the report using a dedicated app and edits the necessary parts. Once editing is complete, they resubmit the report to the server.

[1883] Input: Initial report

[1884] Data processing: Users edit reports and resend them to the server.

[1885] Output: Edited report

[1886] Specific actions: The user reviews the report, adds an "additional comment" to the input field, saves the edits, and then resends the report to the server.

[1887] Step 9:

[1888] Sending reports to parents and related users

[1889] The server sends the edited final report to parents and relevant teachers. Push notifications are sent when the new report arrives.

[1890] Input: Edited report

[1891] Data processing: Send the report to parents and relevant teachers.

[1892] Output: Sent reports and push notifications

[1893] Specific operation: The server sends the edited report to the parent's dedicated app and notifies them via push notification. When the parent opens the app, the report is displayed as "Today's Announcements".

[1894] Step 10:

[1895] Sharing among teachers

[1896] The server will also share the report with other relevant teachers (e.g., homeroom teachers, school nurses, grade level heads) as needed.

[1897] Input: Report data

[1898] Data processing: Automatically send reports to relevant faculty members.

[1899] Output: Shared report

[1900] Specific operation: The server sends the report to the homeroom teacher and school nurse, and sends a notification to each teacher's terminal stating, "A new report has been shared."

[1901] (Application Example 2)

[1902] 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".

[1903] In recent years, there has been a growing demand for improved education and customer service in environments such as classrooms and physical stores by utilizing voice recording and emotion recognition technologies. However, existing systems suffer from insufficient accuracy in transcribing voice data and recognizing emotions, and also lack the functionality to automatically generate reports using this data effectively. As a result, it is difficult for teachers and store managers to quickly and accurately grasp the necessary information, hindering improvements in operational efficiency.

[1904] 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. In this invention, the server includes a voice analysis means that analyzes voice data and converts it into text data, an emotion recognition means that recognizes emotions and generates emotion data, and a report generation means that automatically generates a report based on the text data and emotion data. This makes it possible to obtain highly accurate text data and emotion data from voice data and to automatically generate a report based on them.

[1905] "Voice recording means" refers to a device or system for recording sound in an environment such as a classroom or a physical store.

[1906] "Voice data transmission means" refers to a device or system for transmitting recorded voice data to a server.

[1907] "Voice analysis means" refers to a device or system that analyzes voice data received by a server and converts that voice data into text data.

[1908] "Emotion recognition means" refers to a device or system that recognizes a user's emotions from voice data and generates emotion data.

[1909] "Report generation means" refers to a device or system that automatically generates a report based on acquired text data and sentiment data.

[1910] "Report editing means" refers to a device or system that sends the generated report to a terminal in a format that can be edited by the user.

[1911] "Report transmission means" refers to a device or system for resending reports edited by users to a server.

[1912] "Notification means" refers to a device or system that transmits the edited final report to parents and relevant users.

[1913] "Text data" refers to data in text format obtained as a result of analyzing and converting audio data.

[1914] "Emotional data" refers to data that represents the user's emotions as recognized from voice data.

[1915] Modes for carrying out the invention

[1916] A system for carrying out this invention includes the following components:

[1917] 1. Audio recording means:

[1918] Users (teachers and store operators) record ambient sounds in classrooms and physical stores using smartphones or dedicated devices. For example, when a teacher wants to record students' comments or questions during class, they launch a dedicated application and tap the record button to start audio recording.

[1919] 2. Means for transmitting audio data:

[1920] Once recording is complete, the audio data is temporarily stored on the device and then sent to the server. This transmission requires an internet connection. For example, if you tap the "Send" button after recording is finished, the audio file will be automatically uploaded to the server.

[1921] 3. Voice analysis means:

[1922] The server analyzes the received audio data using speech recognition technology and converts it into text data. Machine learning models such as the Google Speech-to-Text API are used for this process. The analyzed text data then proceeds to the next processing step.

[1923] 4. Emotion recognition means:

[1924] On the server, emotion data is generated using emotion recognition technology based on text data. This is done by analyzing the tone of voice and the content of the speaker's speech. For example, if the word "thank you" is included, it will be recognized as "happy".

[1925] 5. Report generation means:

[1926] Using the analyzed text and sentiment data, a report is automatically generated. This report is created based on a pre-configured report template. For example, the report might be in the format, "Today, [Name] is experiencing sadness."

[1927] 6. Report editing methods:

[1928] The generated report is sent to the user's device in an editable format. The user can review the report and enter any necessary corrections or additional information. The edited report is then sent back to the server.

[1929] 7. Method of sending the report:

[1930] The server sends the edited report to parents and other relevant parties. A push notification feature is used to inform recipients that a new report has arrived. For example, tapping the "Notify Parents" button sends the report to the parent's app and displays a push notification.

[1931] 8. Means of notification:

[1932] Once the report has been submitted, the system sends a notification to the recipient using a notification method. For example, to make it easier for parents to check the report, they may be notified via push notification on their smartphone that a new report has arrived.

[1933] Hardware and software to be used

[1934] Hardware:

[1935] Smartphone, dedicated recording terminal, server

[1936] Software / Tools:

[1937] Voice recording application

[1938] Internet connection

[1939] Google Speech-to-Text API (speech recognition technology using machine learning models)

[1940] Emotion recognition engine (proprietary software)

[1941] Report generation and editing software

[1942] User application with push notification functionality

[1943] Examples of specific cases and prompt statements

[1944] For example, consider a scenario in a store where staff explain products to customers and express their gratitude. The system automatically analyzes this audio data and generates a report containing sentences like the following:

[1945] "This product is very popular. Let me explain how to use it... Thank you!"

[1946] Example of a report generated based on this:

[1947] Customer Feedback:

[1948] This product is very popular. Let me explain how to use it. ...Thank you!

[1949] Emotion: happy”

[1950] Example of a prompt

[1951] "Convert the following audio data to text and analyze the customer's emotions. If the conversation includes the word 'thank you,' output 'emotion: happy.'"

[1952] In this way, teachers and store managers can easily generate reports based on voice and sentiment data, and streamline communication with stakeholders.

[1953] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1954] Step 1:

[1955] Users record audio using their smartphones or dedicated devices. Specifically, teachers or store managers launch the application and tap the record button, which activates the device's microphone and records ambient sounds. The input is conversational audio from the classroom or store, and the output is a recorded audio data file.

[1956] Step 2:

[1957] The device temporarily stores the audio data and sends it to the server via the internet. Specifically, when you tap the "Send" button after recording is finished, the audio data file is uploaded to the server. The input is the recorded audio data file, and the output is the audio data sent to the server.

[1958] Step 3:

[1959] The server analyzes the received audio data and converts it into text data. Specifically, the audio data is converted into text data using speech recognition technology such as the Google Speech-to-Text API. The input is the audio data sent to the server, and the output is the converted text data.

[1960] Step 4:

[1961] The server inputs the converted text data into an emotion recognition engine to generate emotion data. Specifically, the text data is analyzed by the emotion recognition engine, and the user's emotion is identified. For example, if the text data contains "thank you," it will be recognized as "happy." The input is the text data obtained from speech analysis, and the output is the generated emotion data.

[1962] Step 5:

[1963] The server generates a report based on text data and sentiment data. Specifically, text data and sentiment data are embedded in a pre-configured report template. The input is text data and sentiment data, and the output is the generated report.

[1964] Step 6:

[1965] The server sends the generated report to the user's terminal, and the user edits the report. Specifically, the user can review the report content and enter corrections or additional information as needed. The input is the generated report, and the output is the report edited by the user.

[1966] Step 7:

[1967] The device resends the edited report to the server. Specifically, tapping the "Save" button uploads the edited report to the server. The input is the report edited by the user, and the output is the edited report sent to the server.

[1968] Step 8:

[1969] The server sends the edited report to parents and relevant users and sends notifications. Specifically, it uses a push notification function to inform recipients that a new report has arrived. The input is the edited report, and the output is the report sent to parents and relevant users, along with the push notification.

[1970] 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.

[1971] 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.

[1972] 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 robot 414.

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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."

[1979] 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.

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] 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.

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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 as being incorporated by reference.

[1991] The following is further disclosed regarding the embodiments described above.

[1992] (Claim 1)

[1993] A means of recording sound in an environment such as a classroom,

[1994] A means for transmitting recorded audio data to a server,

[1995] A voice analysis means on a server that analyzes voice data and converts it into text data,

[1996] A report generation method that automatically generates reports based on text data,

[1997] A report editing method that sends the generated report to the terminal in a format that the user can edit,

[1998] A report transmission means for resending the edited report to the server,

[1999] A notification mechanism to send the edited final report to parents and relevant users,

[2000] A system that includes this.

[2001] (Claim 2)

[2002] The system according to claim 1, wherein the speech analysis means converts speech data into text data using speech recognition technology.

[2003] (Claim 3)

[2004] The system according to claim 1, wherein the report generation means automatically generates a report using a pre-configured report template.

[2005] "Example 1"

[2006] (Claim 1)

[2007] A means of recording sound in an environment such as a classroom,

[2008] A means for transmitting recorded audio data to a server,

[2009] A voice analysis means on a server that analyzes voice data and converts it into text data,

[2010] A report generation method that automatically generates reports based on text data,

[2011] A report editing method that sends the generated report to the terminal in a format that the user can edit,

[2012] A report transmission means for resending the edited report to the server,

[2013] A notification mechanism to send the edited final report to parents and relevant users,

[2014] A speech recognition engine control means that, after receiving audio data, starts the speech recognition engine and executes the process of converting the audio data into text data,

[2015] A method for automatically generating reports that uses audio data to apply text data to a report template,

[2016] A report sharing method that allows the generated report to be shared with other faculty and staff,

[2017] A system that includes this.

[2018] (Claim 2)

[2019] The system according to claim 1, wherein the speech analysis means converts speech data into text data using a machine learning model.

[2020] (Claim 3)

[2021] The report generation means is a system according to claim 1 that provides information to be inserted into a report in an editable format when automatically generating a report using a template.

[2022] "Application Example 1"

[2023] (Claim 1)

[2024] A means of recording sound in an environment such as a classroom,

[2025] A means for transmitting recorded audio data to a server,

[2026] A voice analysis means on a server that analyzes voice data and converts it into text data,

[2027] A report generation method that automatically generates reports based on text data,

[2028] A report editing method that sends the generated report to the terminal in a format that the user can edit,

[2029] A report transmission means for resending the edited report to the server,

[2030] A notification mechanism to send the edited final report to parents and relevant users,

[2031] A technical support system that records voice data in physical stores and generates troubleshooting reports based on that data,

[2032] A means of sharing engineer information to relevant engineers,

[2033] A system that includes this.

[2034] (Claim 2)

[2035] The system according to claim 1, wherein the speech analysis means converts speech data into text data using speech recognition technology.

[2036] (Claim 3)

[2037] The system according to claim 1, wherein the report generation means automatically generates a report using a pre-configured report template.

[2038] "Example 2 of combining an emotion engine"

[2039] (Claim 1)

[2040] A means for recording sounds in the environment,

[2041] Audio data storage means for temporarily storing recorded audio data,

[2042] A means for transmitting audio data to a server,

[2043] A voice analysis means that converts voice data into text data on a server,

[2044] An emotion recognition means that extracts emotion data from text data converted by a speech analysis means,

[2045] A report generation method that automatically generates reports based on text data and sentiment data,

[2046] A report editing method that sends the generated report to the terminal in a format that the user can edit,

[2047] A report transmission means for resending the edited report to the server,

[2048] A notification mechanism to send the edited final report to parents and relevant users,

[2049] Information sharing means for sharing reports among relevant users,

[2050] A system that includes this.

[2051] (Claim 2)

[2052] The system according to claim 1, wherein the speech analysis means converts speech data into text data using speech recognition technology.

[2053] (Claim 3)

[2054] The system according to claim 1, wherein the report generation means automatically generates a report using a pre-configured report template.

[2055] "Application example 2 when combining with an emotional engine"

[2056] (Claim 1)

[2057] A means of recording sound in an environment such as a classroom,

[2058] A means for transmitting recorded audio data to a server,

[2059] A voice analysis means on a server that analyzes voice data and converts it into text data,

[2060] An emotion recognition means that recognizes emotions and generates emotion data,

[2061] A report generation method that automatically generates reports based on text data and sentiment data,

[2062] A report editing method that sends the generated report to the terminal in a format that the user can edit,

[2063] A report transmission means for resending the edited report to the server,

[2064] A notification mechanism to send the edited final report to parents and relevant users,

[2065] A system that includes this.

[2066] (Claim 2)

[2067] The system according to claim 1, wherein the voice analysis means converts voice data into text data using voice recognition technology and generates emotion data using emotion recognition technology.

[2068] (Claim 3)

[2069] The system according to claim 1, wherein the report generation means automatically generates a report using a pre-configured report template and sentiment data. [Explanation of Symbols]

[2070] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of recording sound in an environment such as a classroom, A means for transmitting recorded audio data to a server, A voice analysis means on a server that analyzes voice data and converts it into text data, A report generation method that automatically generates reports based on text data, A report editing method that sends the generated report to the terminal in a format that the user can edit, A report transmission means for resending the edited report to the server, A notification mechanism to send the edited final report to parents and relevant users, A system that includes this.

2. The system according to claim 1, wherein the speech analysis means converts speech data into text data using speech recognition technology.

3. The system according to claim 1, wherein the report generation means automatically generates a report using a pre-configured report template.

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