system

Abstract

Provide a system. A means for recording user utterances; means for transmitting the recorded voice data to a server; means for converting received voice data into text data; A means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions; means for generating feedback based on the analysis results; means for providing the generated feedback to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] Effective communication in today's world is important not only in specialized fields but also in various other settings, such as business and education. However, it is not easy to review one's own speech or presentation content and identify appropriate areas for improvement. It is particularly difficult to avoid immoral or misleading language and to achieve a sophisticated, easy-to-understand communication style. In addition, incorporating a sense of humor appropriately is also complex. The present invention aims to solve these problems and provide a system that provides users with feedback to effectively improve their speech. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a means for recording a user's speech, a means for transmitting the recorded speech data to a server, a means for converting the received speech data into text data, a means for analyzing the converted text data to detect immoral behavior, misleading expressions, easy-to-understand ways of communicating, and witty expressions, a means for generating feedback based on the analysis results, and a means for providing the generated feedback to the user. This allows the user to review their own speech and obtain specific guidelines for improvement. Furthermore, the feedback is provided in audio or text format, and by detecting immoral behavior using a specific list of prohibited words, more accurate advice can be provided.

[0006] A "user" is an entity that uses the system, records their own speech and presentations, and receives feedback.

[0007] An "utterance" is a word or sentence expressed by a user in spoken form.

[0008] "Recording" is the act of recording a user's speech as audio data.

[0009] A "terminal" is a device (such as a smartphone or computer) that a user uses to operate the system.

[0010] A "server" is a central processing unit that receives voice data, converts it into text data, performs analysis, and generates feedback.

[0011] "Audio data" is digital data that records the user's speech.

[0012] "Text data" is data in character format obtained by converting voice data.

[0013] "Analysis" is the process of evaluating text data to detect moral violations, misleading expressions, easy-to-understand communication, witty expressions, etc.

[0014] "Moral violation" refers to expressions or behavior that are considered socially or ethically inappropriate.

[0015] "Misleading expressions" are expressions that may give the recipient a wrong perception or misunderstanding.

[0016] "Easy-to-understand communication" refers to expressions and structures that allow information to be conveyed clearly and concisely to the recipient.

[0017] "Witty expressions" are expressions that contain humor and wit and are attractive and memorable to the listener.

[0018] "Feedback" is information that provides improvements and advice on user comments based on the analysis results.

[0019] A "banned word list" is a list of inappropriate words and expressions used to detect moral violations. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention relates to a system for enabling users to effectively improve their speech and presentations, by allowing users to record and analyze speech and receive specific feedback.

[0042] System configuration

[0043] 1. Recording Method:

[0044] Users record speeches and presentations using devices such as smartphones and computers. The devices have a voice recording application installed and can record audio through a microphone.

[0045] 2. Audio data transmission means:

[0046] The device can transmit the recorded audio data to a server via an internet connection.

[0047] 3. Audio data conversion means:

[0048] The server converts the received voice data into text data, using a speech recognition engine to convert the recorded content into text information.

[0049] 4. Text data analysis methods:

[0050] The server analyzes the converted text data. The purpose of the analysis is to detect immoral content, misleading expressions, easy-to-understand communication, and witty expressions. This is done using a specific list of prohibited words and natural language processing technology.

[0051] 5. Feedback Generation Methods:

[0052] The server generates feedback based on the analysis results, including suggestions for correcting misused expressions and making improvements.

[0053] 6. How to provide feedback:

[0054] The terminal receives the feedback sent from the server and provides it to the user, which is displayed in voice or text format so that the user can easily check it.

[0055] Specific examples

[0056] For example, consider a user giving a business presentation. The user opens a recording application and starts the presentation. Once the recording is complete, the audio data is sent from the device to a server. The server uses a speech recognition engine to convert the audio into text.

[0057] The server then analyzes the text data, checking for example:

[0058] Detect and remove meaningless expressions such as "um."

[0059] We propose changing the inappropriate expression "yabai" to "seijiu."

[0060] Suggest adding examples or statistics to make the data more specific.

[0061] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[0062] Once the analysis is complete, the server generates specific feedback and sends it to the device, which displays it via a user interface, allowing the user to see areas for improvement. Feedback can also be provided in audio format if desired, allowing users flexibility in how they receive it.

[0063] The present invention allows users to objectively evaluate their own comments and obtain specific measures for improvement, thereby improving the quality of communication.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user opens a voice recording application. The device displays a "Start Recording" button.

[0067] Step 2:

[0068] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[0069] Step 3:

[0070] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[0071] Step 4:

[0072] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[0073] Step 5:

[0074] The device sends the generated audio data file to a server via the Internet.

[0075] Step 6:

[0076] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[0077] Step 7:

[0078] The server parses the text data, which includes the following elements:

[0079] Detecting moral violations (matching against a specific list of banned words).

[0080] Evaluate whether there were any problems with the way you communicated (check grammar and structure).

[0081] Suggestions for ways to communicate information in an easy-to-understand manner (simplifying or elaborating information).

[0082] Step 8:

[0083] The server generates feedback based on the analysis, including suggestions for ethical violations, ways to improve communication, and specific examples and statistics.

[0084] Step 9:

[0085] The server then sends the generated feedback to the device via the Internet.

[0086] Step 10:

[0087] The device receives the feedback and displays it through the user interface, and the user can view the feedback on the screen.

[0088] Step 11:

[0089] The device can also provide feedback in audio format, allowing users to receive feedback either in text or audio format.

[0090] By following these steps, users can identify specific areas for improvement based on the analysis of their own comments and use them in their next presentation or conversation.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Currently, methods for users to improve their speech and presentation skills are problematic because they require a lot of time and effort. For example, users must record their speech, transcribe it into text, and then analyze it. Furthermore, manually analyzing the transcribed content and obtaining feedback requires advanced knowledge and specialized tools. Therefore, there is a need for a method to effectively and efficiently improve one's communication skills.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to a computer, means for converting the received voice data into text information, means for analyzing the converted text information to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions, means for generating feedback based on the analysis results, and means for providing the generated feedback to the user, thereby enabling the user to efficiently improve their speech and presentation in a short amount of time.

[0096] "User" refers to a person who uses the system to record, analyze, and receive feedback on their own speech or presentation.

[0097] "Utterance" refers to the words spoken by a user or the content of a presentation.

[0098] "Recording means" refers to a device or application that allows a user to record their own speech as audio data.

[0099] "Voice data" refers to data that is a digital recording of a user's speech.

[0100] "Means for transmitting to a computer" refers to a process or system for transferring recorded audio data over the Internet to a computing device such as a server.

[0101] "Text information" refers to data that has been converted from audio data into text format.

[0102] "Means for converting" refers to software or hardware for converting voice data into text information.

[0103] "Means for analyzing" refers to a system for analyzing the converted text information and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions.

[0104] "Moral violations" refer to expressions or content that are socially unacceptable.

[0105] "Misleading expressions" refer to expressions that are not clear and may lead to a misunderstanding on the part of the recipient.

[0106] "Easy-to-understand communication" refers to a way of communicating that is clear and easy for the recipient to understand.

[0107] "Witty expressions" refer to expressions that are witty and interesting.

[0108] "Means for generating feedback" refers to a system that generates suggestions and recommendations for improvement based on the analysis results.

[0109] "Generated feedback" refers to specific improvement plans and suggestions created based on the analysis results.

[0110] "Means for providing" refers to the process or system for visually or audibly conveying the generated feedback to the user.

[0111] MODE FOR CARRYING OUT THE INVENTION

[0112] The present invention relates to a system for enabling users to effectively improve their speech and presentations. The system allows users to record and analyze speech and receive specific feedback. Specific embodiments of the present invention are described below.

[0113] Hardware and Software Configuration

[0114] This system mainly uses the following hardware and software:

[0115] Hardware: smartphones, computers, servers

[0116] Software: Voice recording application, Internet connection, speech recognition engine (e.g., Google® Cloud Speech-to-Text API), natural language processing engine (e.g., TENSORFLOW®, NLTK)

[0117] Overall processing

[0118] Users record their own speech or presentation, send the audio data to the server, where it is converted into text information, analyzed, and specific feedback is generated based on the analysis results and provided to the user.

[0119] Detailed processing steps

[0120] 1. Recording:

[0121] Users use smartphones or computers to record their own speeches or presentations. These devices have voice recording applications installed. For example, recordings can be made using "Voice Memos" or "Google Translate." During recording, the device's microphone picks up the user's voice and stores it as digital audio data.

[0122] 2. Audio data transmission:

[0123] After the device has finished recording, it sends the saved audio data to a server over the Internet, encrypted using the HTTPS protocol.

[0124] 3. Audio data conversion:

[0125] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The speech recognition engine analyzes the voice waveform and converts it into corresponding text information.

[0126] 4. Text data analysis:

[0127] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow, NLTK). The analysis includes checking grammar, detecting immoral expressions, detecting meaningless expressions, and extracting witty expressions. Problematic expressions are identified using a list of prohibited words and pre-trained AI models.

[0128] 5. Feedback Generation:

[0129] Based on the analysis, the server generates specific feedback for the user, such as suggestions to replace the expression "yabai" with "seiji" or to remove the meaningless "uh." The feedback is prepared in text and audio formats (using a TTS engine).

[0130] 6. Providing Feedback:

[0131] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually understandable format and, if necessary, can also be provided in audio format.

[0132] Specific examples

[0133] For example, consider a user giving a business presentation. The user records the presentation using the recording app "Voice Memos" on their smartphone. Once the recording is complete, the audio data is sent from the device to a server. The server converts the audio into text data using the Google Cloud Speech-to-Text API. The server then analyzes the text data using TensorFlow or NLTK to generate feedback, such as the following:

[0134] Suggestions to detect and remove nonsensical expressions such as "um."

[0135] A proposal to change the inappropriate expression "yabai" to "seiji."

[0136] Suggest adding statistics and examples to make it more concrete.

[0137] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[0138] The generated feedback is sent to the device and displayed visually through a recording application or a dedicated user interface, and can also be played back in audio format.

[0139] Prompt Sentence Examples

[0140] Transcribe the audio recording of a user's business presentation into text and generate feedback based on:

[0141] Detecting and removing nonsense

[0142] Correction of inappropriate expressions

[0143] Additional examples and statistics

[0144] Proposals for improving immoral expressions

[0145] The generated feedback is provided to the user in an easy-to-understand voice and text format.

[0146] This system allows users to quickly receive specific feedback to efficiently improve their own speech and presentations, thereby improving their communication skills.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1:

[0149] Users use their smartphones or computers to record their own speech or presentation. They launch a voice recording application installed on their device (e.g., Voice Memos or Google Translate), press the record button, and begin speaking. While recording, the device's microphone picks up the user's voice, which is then saved as digital audio data. When recording is complete, the user presses the stop button to end the recording.

[0150] Input: User's voice

[0151] Output: Digital audio data

[0152] Step 2:

[0153] The device sends the saved audio data to the server over the internet as soon as the user finishes recording. The audio data is sent encrypted using a secure protocol (HTTPS). This process ensures the data is transferred from the device to the server, such as by pressing a "send" button after completing the recording.

[0154] Input: Digital audio data

[0155] Output: Audio data sent to the server

[0156] Step 3:

[0157] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, where the voice waveform is analyzed and mapped to a corresponding string. First, the voice data is input into a speech recognition engine, and then the text information is output from the API.

[0158] Input: Audio data sent to the server

[0159] Output: Text data

[0160] Step 4:

[0161] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow or NLTK). During this analysis stage, the following processes take place:

[0162] Detecting immoral expressions

[0163] Detecting and removing meaningless expressions (e.g., "um")

[0164] Identifying misleading statements and generating correction suggestions

[0165] Extracting witty expressions

[0166] For example, text data is input into an analysis engine, and the analyzed information is output in JSON format.

[0167] Input: Text data

[0168] Output: Analysis results

[0169] Step 5:

[0170] The server generates specific feedback for the user based on the results of analyzing the text data. The generated feedback includes suggestions for correcting misused expressions and suggestions for improvement. The generative AI model prepares the text and audio data for the feedback. For example, it generates suggestions for correcting inappropriate parts of the text data and adding specific examples and statistics.

[0171] Input: Analysis results

[0172] Output: Feedback data (text and audio)

[0173] Step 6:

[0174] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually verifiable format, and audio feedback is also provided if necessary. For example, the terminal may display a highlight of the feedback on the screen and provide an audio playback button, allowing the user to check the feedback content in detail.

[0175] Input: Feedback data

[0176] Output: Feedback provided to the user

[0177] By following the above specific steps, users can efficiently improve the quality of their own speech and presentations.

[0178] (Application example 1)

[0179] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0180] The goal of this invention is to provide a system that allows users, particularly staff working in the customer service industry, to effectively improve their own speech and conversation. Conventional methods have had the problem of requiring a great deal of time and effort to improve customer service skills, and of making it difficult to obtain specific feedback. Therefore, there is a need for a system that allows users to quickly and effectively improve their skills by recording and analyzing their conversations while serving customers and receiving appropriate feedback.

[0181] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0182] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to the server, means for converting the received voice data into text data, means for analyzing the converted text data and detecting immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for generating feedback based on the analysis results, means for providing the generated feedback to the user, and means for analyzing conversations during customer service using a specific natural language processing technology and suggesting appropriate methods of customer service, thereby enabling the user to quickly and effectively improve their customer service skills.

[0183] The "means for recording user utterances" is a device or function that allows a user to record his or her utterances in audio format.

[0184] The "means for transmitting recorded voice data to a server" refers to a device or software for transferring recorded voice data to a server via a network.

[0185] The "means for converting received voice data into text data" refers to a device or software that converts voice data received by the server into character information (text) using voice recognition technology.

[0186] "Means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions" refers to devices or software that evaluate the linguistic elements contained in the text data and identify inappropriate expressions or expressions that need improvement.

[0187] The "means for generating feedback based on the analysis results" refers to a device or software that generates specific improvement proposals or advice for the user based on the analyzed data.

[0188] A "means for providing generated feedback to a user" is a device or software for presenting generated feedback to a user in the form of audio or text.

[0189] "Means for analyzing conversations during customer service using specific natural language processing technology and proposing appropriate methods of customer service" refers to a device or software that uses natural language processing (NLP) technology to analyze conversation data during customer service and present more appropriate and effective methods of customer service to the user.

[0190] The system of the present invention records and analyzes the user's speech to provide specific feedback and improve customer service skills. This system is composed of the following components:

[0191] Recording medium

[0192] Users use a smartphone or smart glasses to record their speech and conversations. The recording is done through a voice input device (microphone) and saved as audio data.

[0193] Audio data transmission means

[0194] The recorded audio data is transmitted over the internet to a server, with an application installed on the smartphone or smart glasses managing the transmission process.

[0195] Audio data conversion means

[0196] The server converts the received voice data into text data using a speech recognition engine such as the Google Cloud Speech-to-Text API, which is then used for the subsequent analysis process.

[0197] Text data analysis methods

[0198] The server analyzes the converted text data using natural language processing techniques (e.g., TensorFlow or SpaCy). The goal of the analysis is to detect the following elements:

[0199] meaningless expressions

[0200] Inappropriate expressions

[0201] Easy-to-understand communication

[0202] Witty expressions

[0203] Feedback Generation Method

[0204] Based on the analysis, the server generates specific feedback for the user, including suggestions for correcting misused expressions and making improvements.

[0205] Feedback methods

[0206] The generated feedback is sent to the user's smartphone or smart glasses and provided to them in voice or text format, allowing them to receive specific advice on how to improve their customer service skills.

[0207] A means of analyzing conversations during customer service and suggesting appropriate ways to serve customers

[0208] The server uses specific natural language processing technology to analyze conversation data during customer service, which allows it to propose appropriate customer service methods. The analysis also uses a list of prohibited words to detect moral violations.

[0209] Specific use cases

[0210] For example, consider a case where a user wants to record a conversation while serving a customer. The user opens a recording application on their smartphone and starts serving the customer. Once the recording is complete, the audio data is automatically sent to the server. The server converts the received audio data into text using a speech recognition engine, and then analyzes it using natural language processing technology. By using prompt sentences like the following, specific feedback can be generated and provided to the user based on the analysis results:

[0211] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[0212] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[0213] In this way, users can receive feedback based on the analysis results and learn specific areas for improvement, thereby effectively improving their customer service skills.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] Users use smartphones or smart glasses to record conversations while serving customers. The input is audio data, and the recorded audio is saved as output. Specifically, users launch the application and press the record button to record the conversation.

[0217] Step 2:

[0218] The recorded audio data is sent from the device to the server via the Internet. The input is the audio data generated in step 1, and the server receives the audio data as output. Specifically, the device application automatically uploads the audio data to the server after recording is complete.

[0219] Step 3:

[0220] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. Specifically, the server sends the voice data to the speech recognition engine and saves the returned text data.

[0221] Step 4:

[0222] The server analyzes the converted text data using natural language processing technology (for example, TensorFlow or SpaCy). The input is text data, and the analysis results are obtained as output. Specifically, the server analyzes the text data to detect meaningless expressions, inappropriate expressions, easy-to-understand expressions, and witty expressions.

[0223] Step 5:

[0224] Based on the analysis results, the server generates feedback by inputting a prompt sentence to the generative AI model. The input is the analysis results and the prompt sentence, and the output is feedback. Specifically, the server sends the following prompt sentence to the generative AI model and saves the returned result as feedback:

[0225] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[0226] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[0227] Step 6:

[0228] The server sends the generated feedback to the terminal in the form of voice or text. The input is feedback data, and the terminal displays or plays the feedback as output. In concrete terms, the feedback data is sent to the terminal, and the application on the terminal displays or plays the feedback to the user in voice.

[0229] Step 7:

[0230] The user checks the feedback displayed on the device and uses it to improve their customer service skills. The input is the feedback information, and the output is the user's understanding of the areas for improvement. Specifically, the user reads the feedback displayed on the screen and reflects it in their next customer service encounter.

[0231] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0232] This system allows users to record their own speech and presentations, analyze their emotions, and provide more specific feedback to improve their communication skills.By combining it with an emotion engine, the system analyzes not only the content of speech but also the emotional expression, and reflects this in the feedback.

[0233] System configuration

[0234] 1. Recording Method:

[0235] Users use devices such as smartphones or computers to open a dedicated audio recording application and record speeches or presentations.

[0236] 2. Audio data transmission means:

[0237] The device sends the recorded audio data to a server via an internet connection.

[0238] 3. Audio data conversion means:

[0239] The server uses a speech recognition engine to convert the received voice data into text data, which is then used for analysis.

[0240] 4. Text data analysis methods:

[0241] The server parses the converted text data, which includes the following elements:

[0242] Detecting moral violations (matching against a specific list of banned words).

[0243] Detect misleading statements and suggest improvements.

[0244] Proposal for an easy-to-understand explanation.

[0245] A suggestion for witty expressions.

[0246] 5. Emotion Engine:

[0247] The server uses an emotion engine to identify the user's emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and reflects the results in the analysis.

[0248] 6. Feedback Generation Methods:

[0249] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech, as well as suggestions for improvement regarding emotional expression.

[0250] 7. How to provide feedback:

[0251] The terminal receives the feedback sent from the server and provides it to the user in the form of voice or text, making it easy for the user to understand.

[0252] Specific examples

[0253] For example, suppose a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to the server. The server then uses a speech recognition engine to convert the audio data into text.

[0254] The server then parses the text data and checks the following:

[0255] Eliminating meaningless expressions such as "um."

[0256] A proposal to change the inappropriate expression "yabai" to "seiji."

[0257] Suggest adding examples and statistics to increase the specificity of the data.

[0258] We check for any immoral expressions and suggest alternatives.

[0259] At the same time, the emotion engine analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension."

[0260] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[0261] In this way, the present invention provides users with concrete improvements in terms of both content and emotion, and supports the improvement of communication skills.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] The user opens a voice recording application. The device displays a "Start Recording" button.

[0265] Step 2:

[0266] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[0267] Step 3:

[0268] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[0269] Step 4:

[0270] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[0271] Step 5:

[0272] The device then sends the generated audio data file to the server via the Internet.

[0273] Step 6:

[0274] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[0275] Step 7:

[0276] The server parses the text data, which includes the following elements:

[0277] Detecting moral violations (matching against a specific list of banned words).

[0278] Detect misleading statements and suggest improvements.

[0279] Proposal for an easy-to-understand explanation.

[0280] A suggestion for witty expressions.

[0281] Step 8:

[0282] The server uses an emotion engine to identify emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and incorporates the results into the analysis.

[0283] Step 9:

[0284] The server generates feedback based on the analysis and sentiment analysis results, including advice on the content of the speech and suggestions for improvement regarding emotional expression.

[0285] Step 10:

[0286] The server then sends the generated feedback to the device over the Internet.

[0287] Step 11:

[0288] The device receives the feedback and displays it through a user interface, allowing the user to check the analysis results and the feedback content.

[0289] Step 12:

[0290] The device also provides feedback in audio format, and the user can receive feedback in text or audio format.

[0291] Step 13:

[0292] Based on the feedback provided, users can identify areas for improvement in their own comments and presentations and use this information to improve their comments in future sessions.

[0293] This specific processing flow allows users to receive comprehensive feedback not only on the content of their own statements but also on their emotional expressions, thereby improving the quality of their communication.

[0294] Example 2

[0295] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0296] In today's business and educational settings, improving the quality of speech and presentations is extremely important. However, conventional feedback systems focus on analyzing speech content, failing to consider the speaker's emotions and tone when providing feedback. Furthermore, they focus too much on eliminating expressions that violate morals or are misleading, failing to provide more specific improvement measures. To address these issues, it is necessary to analyze users' emotions and incorporate them into feedback to improve communication skills more effectively.

[0297] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0298] In this invention, the server includes means for converting received voice data into text data, means for analyzing the converted text data to detect immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for analyzing the user's emotions from the voice data, and means for generating feedback based on the analysis results and emotion analysis results, thereby enabling more specific and multifaceted feedback that includes not only the content of the speech but also emotional expressions.

[0299] "User" refers to any individual or organization that uses the system to record speeches or presentations and receive feedback.

[0300] "Means for recording speech" refers to a device or application for recording a user's speech in digital form as audio data.

[0301] "Means for transmitting audio data to a server" refers to a device or software that includes network functionality for transmitting recorded audio data to a remote server via the Internet.

[0302] "Means for converting voice data into text data" refers to software or hardware for converting voice data into text using voice recognition technology.

[0303] "Means for analyzing text data" refers to software or hardware that includes natural language processing technology for detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions based on the converted text data.

[0304] "Means for analyzing user emotions from voice data" refers to algorithms that analyze the tone and intonation of voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[0305] The "means for generating feedback" refers to software or hardware for automatically constructing advice or improvement suggestions to be provided to the user based on the results of text data analysis and sentiment analysis.

[0306] "Means for providing feedback to the user" refers to a display device or application for presenting the generated feedback to the user in audio or text form.

[0307] This invention is a system that allows users to record their own speech or presentation and analyze their emotions to provide more specific feedback and improve their communication skills. This system is configured using the following hardware and software.

[0308] 1. Recording Method:

[0309] A user uses a device such as a smartphone or computer to open a dedicated voice recording application (e.g., a general audio recording application) to record speech or a presentation. This application records the user's voice as digital data.

[0310] 2. Means of data transmission:

[0311] The device sends the recorded audio data to a server via an internet connection, typically Wi-Fi or a mobile data network.

[0312] 3. Audio data conversion means:

[0313] The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text, and the voice data is analyzed in the cloud by calling an API, and the returned text data is obtained.

[0314] 4. Text data analysis methods:

[0315] The server analyzes the text data using Python and NLP libraries (e.g., spaCy). The analysis includes detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions. Furthermore, it detects moral violations using a specific list of prohibited words.

[0316] 5. Emotion Engine:

[0317] The server uses an emotion engine, such as IBM Watson® Tone Analyzer, to analyze the user's emotions from the voice data. The emotion engine analyzes the tone and intonation of the voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[0318] 6. Feedback Generation Methods:

[0319] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[0320] 7. How to provide feedback:

[0321] The terminal receives the feedback sent from the server and provides it to the user through a user interface, which may be displayed in audio or text format and configured to be easily understood by the user.

[0322] Specific examples

[0323] For example, say a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to a server. The server then converts the audio data into text using Google Cloud Speech-to-Text.

[0324] The server then parses the text data and checks the following:

[0325] Eliminating meaningless expressions such as "um."

[0326] A proposal to change the inappropriate expression "yabai" to "seiji."

[0327] Suggest adding examples and statistics to increase the specificity of the data.

[0328] We check for any immoral expressions and suggest alternatives.

[0329] At the same time, the emotion engine (IBM Watson Tone Analyzer) analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension" based on the results.

[0330] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[0331] Prompt Sentence Examples

[0332] markdown

[0333] A user has recorded an important business presentation. Analyze the audio data below and generate feedback based on the text data and sentiment analysis results.

[0334] Audio data: [Audio data link or file]

[0335] Information to include in your feedback:

[0336] 1. Eliminating meaningless expressions

[0337] 2. Proposing appropriate alternatives to inappropriate expressions

[0338] 3. Suggestions for improvement (adding specific data or examples, etc.)

[0339] 4. Advice based on sentiment analysis results

[0340] This system allows users to obtain specific improvement measures in terms of both content and emotion, enabling them to improve their communication skills more effectively.

[0341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0342] Step 1:

[0343] Users install a dedicated voice recording application on their smartphone or computer. They launch the application and press the "Start Recording" button to record speech or presentations. The input is the user's voice, and the output is audio data. This audio data is recorded in digital format.

[0344] Specific behavior:

[0345] The user opens the application.

[0346] When a user speaks into the microphone, the application records the voice as digital data.

[0347] Step 2:

[0348] After the device finishes recording, it sends the recorded audio data to the server via an Internet connection. The input is the recorded audio data, and the output is the audio data sent to the server. This transmission is done automatically using the API within the application.

[0349] Specific behavior:

[0350] The device has an internet connection.

[0351] The terminal divides the voice data into packets and sends them to the server over the network.

[0352] Step 3:

[0353] The server analyzes the received voice data using a speech recognition engine such as Google Cloud Speech-to-Text and converts it into text data. The input is voice data and the output is text data. Analysis is performed on the cloud by calling an API.

[0354] Specific behavior:

[0355] The server calls the Google Cloud Speech-to-Text API to upload the audio data.

[0356] The server retrieves the returned text data.

[0357] Step 4:

[0358] The server uses Python and NLP libraries (e.g., spaCy) to perform detailed analysis of the text data. The input for the analysis is the text data, and the output is the analysis results. Specifically, it checks for the following elements: moral violations, misleading expressions, easy-to-understand communication, and witty expressions.

[0359] Specific behavior:

[0360] After the server receives the text data, it executes the Python script.

[0361] The server uses an NLP library to generate the analysis results.

[0362] Step 5:

[0363] The server uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotion from the voice data. The input is the voice data, and the output is the emotion analysis result. The emotion engine analyzes the tone and intonation of the voice to identify the emotion.

[0364] Specific behavior:

[0365] The server calls the IBM Watson Tone Analyzer API to send the audio data.

[0366] The server receives the returned emotion analysis results and reflects them in the analysis.

[0367] Step 6:

[0368] The server generates feedback based on the results of text analysis and sentiment analysis. The input is the analysis results and sentiment analysis results, and the output is feedback data. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[0369] Specific behavior:

[0370] The server generates feedback data based on the analysis results.

[0371] The server converts the feedback data into a format that is easy for the user to understand.

[0372] Step 7:

[0373] The terminal receives the feedback sent from the server and provides it to the user through a user interface. The input is feedback data, and the output is feedback in voice or text format. The feedback is displayed in voice or text format.

[0374] Specific behavior:

[0375] The terminal receives the feedback data via the Internet.

[0376] The device displays the feedback data in its user interface (e.g., smartphone notifications, messages within a specific application).

[0377] Through the above processing steps, the system is able to analyze the content of the user's remarks and presentations and provide effective feedback.

[0378] (Application example 2)

[0379] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0380] Conventional speech analysis systems focus on analyzing the content of a user's speech, but do not analyze emotions, making it difficult to comprehensively improve a user's communication skills. Furthermore, because specific feedback on emotional expressions is not provided, users are unable to acquire specific measures to improve their own emotional expressions. The present invention aims to solve these problems by providing specific feedback on both the content of a user's speech and their emotional expressions, thereby comprehensively improving communication skills.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0382] In this invention, the server includes a device for recording user utterances, a device for transmitting the recorded voice data to a remote device, a device for converting the received voice data into text data, a device for analyzing the converted text data and detecting immoral expressions, misleading expressions, easy-to-understand communication methods, and witty expressions, a device for generating feedback based on the analysis results, a device for providing the generated feedback to the user, a device for identifying the user's emotions from the analyzed voice data, and a device for providing feedback that includes suggestions for improving emotional expressions based on the identified emotion analysis results. This allows the user to receive specific feedback not only on the content of their own speech but also on their emotional expressions, thereby enabling them to improve their overall communication skills.

[0383] "User" means an individual or corporation that uses the system.

[0384] "Utterance" refers to content that is verbally expressed by a user.

[0385] A "recording device" is a device for saving a user's speech as voice data.

[0386] "Voice data" refers to data in which a user's speech is saved in digital form.

[0387] "Remote device" refers to a server or cloud that exists away from the user's local device to process data.

[0388] "Text data" is data obtained by converting voice data into text format.

[0389] "Analyzing" is the process of examining the content of data and identifying specific patterns or information.

[0390] "Moral violation" refers to the inclusion of content or expressions that are socially or ethically inappropriate.

[0391] "Misleading expressions" refer to expressions that may be interpreted differently from the meaning intended by the user.

[0392] "Easy-to-understand communication" refers to a way of expressing information so that it can be easily understood by the recipient.

[0393] "Witty expression" refers to an expression that is humorous or intellectually clever.

[0394] "Feedback" refers to evaluations and advice provided based on the user's speech and analysis results.

[0395] "Emotion" refers to a psychological state such as joy, sadness, anger, surprise, or fear that a user expresses while speaking.

[0396] An "identifying apparatus" is a device for detecting and identifying a user's emotion from voice data.

[0397] "Suggestion" is an act of explicitly notifying a specific problem or point for improvement contained in a user's utterance.

[0398] "Improvement suggestions" are specific suggestions for users to improve their speech and emotional expression based on the feedback.

[0399] The present invention is a system that analyzes both the content of user speech and emotional expressions and provides specific feedback. This system includes a series of processes: recording, transmitting the voice data, converting it to text data, analyzing the text data, analyzing emotions, and generating and providing feedback. Each step is described in detail below.

[0400] First, a user opens a dedicated recording application to record their speech. This application runs on a device such as a smartphone, tablet, PC, or robot. The user begins speaking and records it. The recorded audio data is then sent to a server via the Internet.

[0401] The server receives the voice data and then converts it into text data using a speech recognition engine (such as Google's Speech-to-Text API). This converted text data is further analyzed by an analysis engine, which performs functions such as detecting moral violations, detecting misleading expressions, analyzing easy-to-understand communication, and detecting witty expressions.

[0402] The server then uses a sentiment analysis engine to identify the user's emotions from the voice data. The sentiment analysis engine uses a generative AI model, such as IBM Watson's Tone Analyzer, to identify the user's emotions, such as joy, sadness, anger, surprise, and fear. The results of this sentiment analysis, along with text analysis, are reflected in the feedback.

[0403] The feedback generation engine generates specific feedback based on the results of text analysis and sentiment analysis. This feedback includes suggestions for improving not only the content of speech but also the expression of emotions. For example, specific advice such as "You need to be careful because you use 'um' a lot in your speech" or "We recommend that you incorporate breathing techniques to relieve tension during presentations" is provided.

[0404] Finally, the generated feedback is sent to the user's device and provided to them in voice or text format, which the user can refer to and use to improve their own speech and emotional expression.

[0405] As a concrete example, consider a user giving a business presentation. The user opens a recording application for the presentation and records the content of the presentation. Once the recording is complete, the audio data is sent to a server and converted into text data. The server analyzes the converted text and performs sentiment analysis. Based on the analysis results, specific feedback on the user's presentation content and emotional expressions is generated and provided to the user.

[0406] An example prompt is:

[0407] "I'm going to begin my presentation. I'll record everything I say for the next five minutes and provide sentiment analysis and feedback afterward."

[0408] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0409] Step 1:

[0410] The user launches a dedicated recording application. The user presses the record button in the application to start speaking, and when they finish speaking, they press the record button again to stop recording. The input is the user's speech, and the output is the recorded voice data.

[0411] Step 2:

[0412] The device sends the recorded audio data to the server using an internet connection. The input is the recorded audio data, and the output is the audio data sent to the server.

[0413] Step 3:

[0414] The server converts the received voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The input is voice data, and the output is the converted text data.

[0415] Step 4:

[0416] The server passes the converted text data to an analysis engine to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions. The analysis engine performs the analysis using a specific list of prohibited words and other analysis rules. The input is the text data, and the output is the analysis results.

[0417] Step 5:

[0418] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotion from the voice data. Analyzed emotions include joy, sadness, anger, surprise, and fear. The input is the voice data, and the output is the emotion analysis result.

[0419] Step 6:

[0420] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes suggestions for improving content and emotional expression. For example, it includes specific examples such as "You use 'um' a lot in your comments, so be careful" or "You seemed nervous during the presentation, so I recommend trying some breathing techniques to relax." The input is the results of text analysis and sentiment analysis, and the output is feedback.

[0421] Step 7:

[0422] The server sends the generated feedback to the terminal, and the terminal presents the received feedback to the user in voice or text format. The input is the feedback, and the output is the feedback displayed on the user's terminal.

[0423] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0424] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0429] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0430] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0431] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0433] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0434] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0435] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0436] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0437] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0438] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0439] The present invention relates to a system for enabling users to effectively improve their speech and presentations, by allowing users to record and analyze speech and receive specific feedback.

[0440] System configuration

[0441] 1. Recording Method:

[0442] Users record speeches and presentations using devices such as smartphones and computers. The devices have a voice recording application installed and can record audio through a microphone.

[0443] 2. Audio data transmission means:

[0444] The device can transmit the recorded audio data to a server via an internet connection.

[0445] 3. Audio data conversion means:

[0446] The server converts the received voice data into text data, using a speech recognition engine to convert the recorded content into text information.

[0447] 4. Text data analysis methods:

[0448] The server analyzes the converted text data. The purpose of the analysis is to detect immoral content, misleading expressions, easy-to-understand communication, and witty expressions. This is done using a specific list of prohibited words and natural language processing technology.

[0449] 5. Feedback Generation Methods:

[0450] The server generates feedback based on the analysis results, including suggestions for correcting misused expressions and making improvements.

[0451] 6. How to provide feedback:

[0452] The terminal receives the feedback sent from the server and provides it to the user, which is displayed in voice or text format so that the user can easily check it.

[0453] Specific examples

[0454] For example, consider a user giving a business presentation. The user opens a recording application and starts the presentation. Once the recording is complete, the audio data is sent from the device to a server. The server uses a speech recognition engine to convert the audio into text.

[0455] The server then analyzes the text data, checking for example:

[0456] Detect and remove meaningless expressions such as "um."

[0457] We propose changing the inappropriate expression "yabai" to "seijiu."

[0458] Suggest adding examples or statistics to make the data more specific.

[0459] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[0460] Once the analysis is complete, the server generates specific feedback and sends it to the device, which displays it via a user interface, allowing the user to see areas for improvement. Feedback can also be provided in audio format if desired, allowing users flexibility in how they receive it.

[0461] The present invention allows users to objectively evaluate their own comments and obtain specific measures for improvement, thereby improving the quality of communication.

[0462] The processing flow will be explained below.

[0463] Step 1:

[0464] The user opens a voice recording application. The device displays a "Start Recording" button.

[0465] Step 2:

[0466] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[0467] Step 3:

[0468] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[0469] Step 4:

[0470] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[0471] Step 5:

[0472] The device sends the generated audio data file to a server via the Internet.

[0473] Step 6:

[0474] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[0475] Step 7:

[0476] The server parses the text data, which includes the following elements:

[0477] Detecting moral violations (matching against a specific list of banned words).

[0478] Evaluate whether there were any problems with the way you communicated (check grammar and structure).

[0479] Suggestions for ways to communicate information in an easy-to-understand manner (simplifying or elaborating information).

[0480] Step 8:

[0481] The server generates feedback based on the analysis, including suggestions for ethical violations, ways to improve communication, and specific examples and statistics.

[0482] Step 9:

[0483] The server then sends the generated feedback to the device via the Internet.

[0484] Step 10:

[0485] The device receives the feedback and displays it through the user interface, and the user can view the feedback on the screen.

[0486] Step 11:

[0487] The device can also provide feedback in audio format, allowing users to receive feedback either in text or audio format.

[0488] By following these steps, users can identify specific areas for improvement based on the analysis of their own comments and use them in their next presentation or conversation.

[0489] Example 1

[0490] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0491] Currently, methods for users to improve their speech and presentation skills are problematic because they require a lot of time and effort. For example, users must record their speech, transcribe it into text, and then analyze it. Furthermore, manually analyzing the transcribed content and obtaining feedback requires advanced knowledge and specialized tools. Therefore, there is a need for a method to effectively and efficiently improve one's communication skills.

[0492] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0493] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to a computer, means for converting the received voice data into text information, means for analyzing the converted text information to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions, means for generating feedback based on the analysis results, and means for providing the generated feedback to the user, thereby enabling the user to efficiently improve their speech and presentation in a short amount of time.

[0494] "User" refers to a person who uses the system to record, analyze, and receive feedback on their own speech or presentation.

[0495] "Utterance" refers to the words spoken by a user or the content of a presentation.

[0496] "Recording means" refers to a device or application that allows a user to record their own speech as audio data.

[0497] "Voice data" refers to data that is a digital recording of a user's speech.

[0498] "Means for transmitting to a computer" refers to a process or system for transferring recorded audio data over the Internet to a computing device such as a server.

[0499] "Text information" refers to data that has been converted from audio data into text format.

[0500] "Means for converting" refers to software or hardware for converting voice data into text information.

[0501] "Means for analyzing" refers to a system for analyzing the converted text information and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions.

[0502] "Moral violations" refer to expressions or content that are socially unacceptable.

[0503] "Misleading expressions" refer to expressions that are not clear and may lead to a misunderstanding on the part of the recipient.

[0504] "Easy-to-understand communication" refers to a way of communicating that is clear and easy for the recipient to understand.

[0505] "Witty expressions" refer to expressions that are witty and interesting.

[0506] "Means for generating feedback" refers to a system that generates suggestions and recommendations for improvement based on the analysis results.

[0507] "Generated feedback" refers to specific improvement plans and suggestions created based on the analysis results.

[0508] "Means for providing" refers to the process or system for visually or audibly conveying the generated feedback to the user.

[0509] MODE FOR CARRYING OUT THE INVENTION

[0510] The present invention relates to a system for enabling users to effectively improve their speech and presentations. The system allows users to record and analyze speech and receive specific feedback. Specific embodiments of the present invention are described below.

[0511] Hardware and Software Configuration

[0512] This system mainly uses the following hardware and software:

[0513] Hardware: smartphones, computers, servers

[0514] Software: Voice recording application, Internet connection, speech recognition engine (e.g., Google Cloud Speech-to-Text API), natural language processing engine (e.g., TensorFlow, NLTK)

[0515] Overall processing

[0516] Users record their own speech or presentation, send the audio data to the server, where it is converted into text information, analyzed, and specific feedback is generated based on the analysis results and provided to the user.

[0517] Detailed processing steps

[0518] 1. Recording:

[0519] Users use smartphones or computers to record their own speeches or presentations. These devices have voice recording applications installed. For example, recordings can be made using "Voice Memos" or "Google Translate." During recording, the device's microphone picks up the user's voice and stores it as digital audio data.

[0520] 2. Audio data transmission:

[0521] After the device has finished recording, it sends the saved audio data to a server over the Internet, encrypted using the HTTPS protocol.

[0522] 3. Audio data conversion:

[0523] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The speech recognition engine analyzes the voice waveform and converts it into corresponding text information.

[0524] 4. Text data analysis:

[0525] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow, NLTK). The analysis includes checking grammar, detecting immoral expressions, detecting meaningless expressions, and extracting witty expressions. Problematic expressions are identified using a list of prohibited words and pre-trained AI models.

[0526] 5. Feedback Generation:

[0527] Based on the analysis, the server generates specific feedback for the user, such as suggestions to replace the expression "yabai" with "seiji" or to remove the meaningless "uh." The feedback is prepared in text and audio formats (using a TTS engine).

[0528] 6. Providing Feedback:

[0529] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually understandable format and, if necessary, can also be provided in audio format.

[0530] Specific examples

[0531] For example, consider a user giving a business presentation. The user records the presentation using the recording app "Voice Memos" on their smartphone. Once the recording is complete, the audio data is sent from the device to a server. The server converts the audio into text data using the Google Cloud Speech-to-Text API. The server then analyzes the text data using TensorFlow or NLTK to generate feedback, such as the following:

[0532] Suggestions to detect and remove nonsensical expressions such as "um."

[0533] A proposal to change the inappropriate expression "yabai" to "seiji."

[0534] Suggest adding statistics and examples to make it more concrete.

[0535] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[0536] The generated feedback is sent to the device and displayed visually through a recording application or a dedicated user interface, and can also be played back in audio format.

[0537] Prompt Sentence Examples

[0538] Transcribe the audio recording of a user's business presentation into text and generate feedback based on:

[0539] Detecting and removing nonsense

[0540] Correction of inappropriate expressions

[0541] Additional examples and statistics

[0542] Proposals for improving immoral expressions

[0543] The generated feedback is provided to the user in an easy-to-understand voice and text format.

[0544] This system allows users to quickly receive specific feedback to efficiently improve their own speech and presentations, thereby improving their communication skills.

[0545] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0546] Step 1:

[0547] Users use their smartphones or computers to record their own speech or presentation. They launch a voice recording application installed on their device (e.g., Voice Memos or Google Translate), press the record button, and begin speaking. While recording, the device's microphone picks up the user's voice, which is then saved as digital audio data. When recording is complete, the user presses the stop button to end the recording.

[0548] Input: User's voice

[0549] Output: Digital audio data

[0550] Step 2:

[0551] The device sends the saved audio data to the server over the internet as soon as the user finishes recording. The audio data is sent encrypted using a secure protocol (HTTPS). This process ensures the data is transferred from the device to the server, such as by pressing a "send" button after completing the recording.

[0552] Input: Digital audio data

[0553] Output: Audio data sent to the server

[0554] Step 3:

[0555] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, where the voice waveform is analyzed and mapped to a corresponding string. First, the voice data is input into a speech recognition engine, and then the text information is output from the API.

[0556] Input: Audio data sent to the server

[0557] Output: Text data

[0558] Step 4:

[0559] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow or NLTK). During this analysis stage, the following processes take place:

[0560] Detecting immoral expressions

[0561] Detecting and removing meaningless expressions (e.g., "um")

[0562] Identifying misleading statements and generating correction suggestions

[0563] Extracting witty expressions

[0564] For example, text data is input into an analysis engine, and the analyzed information is output in JSON format.

[0565] Input: Text data

[0566] Output: Analysis results

[0567] Step 5:

[0568] The server generates specific feedback for the user based on the results of analyzing the text data. The generated feedback includes suggestions for correcting misused expressions and suggestions for improvement. The generative AI model prepares the text and audio data for the feedback. For example, it generates suggestions for correcting inappropriate parts of the text data and adding specific examples and statistics.

[0569] Input: Analysis results

[0570] Output: Feedback data (text and audio)

[0571] Step 6:

[0572] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually verifiable format, and audio feedback is also provided if necessary. For example, the terminal may display a highlight of the feedback on the screen and provide an audio playback button, allowing the user to check the feedback content in detail.

[0573] Input: Feedback data

[0574] Output: Feedback provided to the user

[0575] By following the above specific steps, users can efficiently improve the quality of their own speech and presentations.

[0576] (Application example 1)

[0577] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0578] The goal of this invention is to provide a system that allows users, particularly staff working in the customer service industry, to effectively improve their own speech and conversation. Conventional methods have had the problem of requiring a great deal of time and effort to improve customer service skills, and of making it difficult to obtain specific feedback. Therefore, there is a need for a system that allows users to quickly and effectively improve their skills by recording and analyzing their conversations while serving customers and receiving appropriate feedback.

[0579] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0580] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to the server, means for converting the received voice data into text data, means for analyzing the converted text data and detecting immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for generating feedback based on the analysis results, means for providing the generated feedback to the user, and means for analyzing conversations during customer service using a specific natural language processing technology and suggesting appropriate methods of customer service, thereby enabling the user to quickly and effectively improve their customer service skills.

[0581] The "means for recording user utterances" is a device or function that allows a user to record his or her utterances in audio format.

[0582] The "means for transmitting recorded voice data to a server" refers to a device or software for transferring recorded voice data to a server via a network.

[0583] The "means for converting received voice data into text data" refers to a device or software that converts voice data received by the server into character information (text) using voice recognition technology.

[0584] "Means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions" refers to devices or software that evaluate the linguistic elements contained in the text data and identify inappropriate expressions or expressions that need improvement.

[0585] The "means for generating feedback based on the analysis results" refers to a device or software that generates specific improvement proposals or advice for the user based on the analyzed data.

[0586] A "means for providing generated feedback to a user" is a device or software for presenting generated feedback to a user in the form of audio or text.

[0587] "Means for analyzing conversations during customer service using specific natural language processing technology and proposing appropriate methods of customer service" refers to a device or software that uses natural language processing (NLP) technology to analyze conversation data during customer service and present more appropriate and effective methods of customer service to the user.

[0588] The system of the present invention records and analyzes the user's speech to provide specific feedback and improve customer service skills. This system is composed of the following components:

[0589] Recording medium

[0590] Users use a smartphone or smart glasses to record their speech and conversations. The recording is done through a voice input device (microphone) and saved as audio data.

[0591] Audio data transmission means

[0592] The recorded audio data is transmitted over the internet to a server, with an application installed on the smartphone or smart glasses managing the transmission process.

[0593] Audio data conversion means

[0594] The server converts the received voice data into text data using a speech recognition engine such as the Google Cloud Speech-to-Text API, which is then used for the subsequent analysis process.

[0595] Text data analysis methods

[0596] The server analyzes the converted text data using natural language processing techniques (e.g., TensorFlow or SpaCy). The goal of the analysis is to detect the following elements:

[0597] meaningless expressions

[0598] Inappropriate expressions

[0599] Easy-to-understand communication

[0600] Witty expressions

[0601] Feedback Generation Method

[0602] Based on the analysis, the server generates specific feedback for the user, including suggestions for correcting misused expressions and making improvements.

[0603] Feedback methods

[0604] The generated feedback is sent to the user's smartphone or smart glasses and provided to them in voice or text format, allowing them to receive specific advice on how to improve their customer service skills.

[0605] A means of analyzing conversations during customer service and suggesting appropriate ways to serve customers

[0606] The server uses specific natural language processing technology to analyze conversation data during customer service, which allows it to propose appropriate customer service methods. The analysis also uses a list of prohibited words to detect moral violations.

[0607] Specific use cases

[0608] For example, consider a case where a user wants to record a conversation while serving a customer. The user opens a recording application on their smartphone and starts serving the customer. Once the recording is complete, the audio data is automatically sent to the server. The server converts the received audio data into text using a speech recognition engine, and then analyzes it using natural language processing technology. By using prompt sentences like the following, specific feedback can be generated and provided to the user based on the analysis results:

[0609] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[0610] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[0611] In this way, users can receive feedback based on the analysis results and learn specific areas for improvement, thereby effectively improving their customer service skills.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Step 1:

[0614] Users use smartphones or smart glasses to record conversations while serving customers. The input is audio data, and the recorded audio is saved as output. Specifically, users launch the application and press the record button to record the conversation.

[0615] Step 2:

[0616] The recorded audio data is sent from the device to the server via the Internet. The input is the audio data generated in step 1, and the server receives the audio data as output. Specifically, the device application automatically uploads the audio data to the server after recording is complete.

[0617] Step 3:

[0618] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. Specifically, the server sends the voice data to the speech recognition engine and saves the returned text data.

[0619] Step 4:

[0620] The server analyzes the converted text data using natural language processing technology (for example, TensorFlow or SpaCy). The input is text data, and the analysis results are obtained as output. Specifically, the server analyzes the text data to detect meaningless expressions, inappropriate expressions, easy-to-understand expressions, and witty expressions.

[0621] Step 5:

[0622] Based on the analysis results, the server generates feedback by inputting a prompt sentence to the generative AI model. The input is the analysis results and the prompt sentence, and the output is feedback. Specifically, the server sends the following prompt sentence to the generative AI model and saves the returned result as feedback:

[0623] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[0624] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[0625] Step 6:

[0626] The server sends the generated feedback to the terminal in the form of voice or text. The input is feedback data, and the terminal displays or plays the feedback as output. In concrete terms, the feedback data is sent to the terminal, and the application on the terminal displays or plays the feedback to the user in voice.

[0627] Step 7:

[0628] The user checks the feedback displayed on the device and uses it to improve their customer service skills. The input is the feedback information, and the output is the user's understanding of the areas for improvement. Specifically, the user reads the feedback displayed on the screen and reflects it in their next customer service encounter.

[0629] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0630] This system allows users to record their own speech and presentations, analyze their emotions, and provide more specific feedback to improve their communication skills.By combining it with an emotion engine, the system analyzes not only the content of speech but also the emotional expression, and reflects this in the feedback.

[0631] System configuration

[0632] 1. Recording Method:

[0633] Users use devices such as smartphones or computers to open a dedicated audio recording application and record speeches or presentations.

[0634] 2. Audio data transmission means:

[0635] The device sends the recorded audio data to a server via an internet connection.

[0636] 3. Audio data conversion means:

[0637] The server uses a speech recognition engine to convert the received voice data into text data, which is then used for analysis.

[0638] 4. Text data analysis methods:

[0639] The server parses the converted text data, which includes the following elements:

[0640] Detecting moral violations (matching against a specific list of banned words).

[0641] Detect misleading statements and suggest improvements.

[0642] Proposal for an easy-to-understand explanation.

[0643] A suggestion for witty expressions.

[0644] 5. Emotion Engine:

[0645] The server uses an emotion engine to identify the user's emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and reflects the results in the analysis.

[0646] 6. Feedback Generation Methods:

[0647] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech, as well as suggestions for improvement regarding emotional expression.

[0648] 7. How to provide feedback:

[0649] The terminal receives the feedback sent from the server and provides it to the user in the form of voice or text, making it easy for the user to understand.

[0650] Specific examples

[0651] For example, suppose a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to the server. The server then uses a speech recognition engine to convert the audio data into text.

[0652] The server then parses the text data and checks the following:

[0653] Eliminating meaningless expressions such as "um."

[0654] A proposal to change the inappropriate expression "yabai" to "seiji."

[0655] Suggest adding examples and statistics to increase the specificity of the data.

[0656] We check for any immoral expressions and suggest alternatives.

[0657] At the same time, the emotion engine analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension."

[0658] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[0659] In this way, the present invention provides users with concrete improvements in terms of both content and emotion, and supports the improvement of communication skills.

[0660] The processing flow will be explained below.

[0661] Step 1:

[0662] The user opens a voice recording application. The device displays a "Start Recording" button.

[0663] Step 2:

[0664] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[0665] Step 3:

[0666] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[0667] Step 4:

[0668] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[0669] Step 5:

[0670] The device then sends the generated audio data file to the server via the Internet.

[0671] Step 6:

[0672] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[0673] Step 7:

[0674] The server parses the text data, which includes the following elements:

[0675] Detecting moral violations (matching against a specific list of banned words).

[0676] Detect misleading statements and suggest improvements.

[0677] Proposal for an easy-to-understand explanation.

[0678] A suggestion for witty expressions.

[0679] Step 8:

[0680] The server uses an emotion engine to identify emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and incorporates the results into the analysis.

[0681] Step 9:

[0682] The server generates feedback based on the analysis and sentiment analysis results, including advice on the content of the speech and suggestions for improvement regarding emotional expression.

[0683] Step 10:

[0684] The server then sends the generated feedback to the device over the Internet.

[0685] Step 11:

[0686] The device receives the feedback and displays it through a user interface, allowing the user to check the analysis results and the feedback content.

[0687] Step 12:

[0688] The device also provides feedback in audio format, and the user can receive feedback in text or audio format.

[0689] Step 13:

[0690] Based on the feedback provided, users can identify areas for improvement in their own comments and presentations and use this information to improve their comments in future sessions.

[0691] This specific processing flow allows users to receive comprehensive feedback not only on the content of their own statements but also on their emotional expressions, thereby improving the quality of their communication.

[0692] Example 2

[0693] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0694] In today's business and educational settings, improving the quality of speech and presentations is extremely important. However, conventional feedback systems focus on analyzing speech content, failing to consider the speaker's emotions and tone when providing feedback. Furthermore, they focus too much on eliminating expressions that violate morals or are misleading, failing to provide more specific improvement measures. To address these issues, it is necessary to analyze users' emotions and incorporate them into feedback to improve communication skills more effectively.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0696] In this invention, the server includes means for converting received voice data into text data, means for analyzing the converted text data to detect immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for analyzing the user's emotions from the voice data, and means for generating feedback based on the analysis results and emotion analysis results, thereby enabling more specific and multifaceted feedback that includes not only the content of the speech but also emotional expressions.

[0697] "User" refers to any individual or organization that uses the system to record speeches or presentations and receive feedback.

[0698] "Means for recording speech" refers to a device or application for recording a user's speech in digital form as audio data.

[0699] "Means for transmitting audio data to a server" refers to a device or software that includes network functionality for transmitting recorded audio data to a remote server via the Internet.

[0700] "Means for converting voice data into text data" refers to software or hardware for converting voice data into text using voice recognition technology.

[0701] "Means for analyzing text data" refers to software or hardware that includes natural language processing technology for detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions based on the converted text data.

[0702] "Means for analyzing user emotions from voice data" refers to algorithms that analyze the tone and intonation of voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[0703] The "means for generating feedback" refers to software or hardware for automatically constructing advice or improvement suggestions to be provided to the user based on the results of text data analysis and sentiment analysis.

[0704] "Means for providing feedback to the user" refers to a display device or application for presenting the generated feedback to the user in audio or text form.

[0705] This invention is a system that allows users to record their own speech or presentation and analyze their emotions to provide more specific feedback and improve their communication skills. This system is configured using the following hardware and software.

[0706] 1. Recording Method:

[0707] A user uses a device such as a smartphone or computer to open a dedicated voice recording application (e.g., a general audio recording application) to record speech or a presentation. This application records the user's voice as digital data.

[0708] 2. Means of data transmission:

[0709] The device sends the recorded audio data to a server via an internet connection, typically Wi-Fi or a mobile data network.

[0710] 3. Audio data conversion means:

[0711] The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text, and the voice data is analyzed in the cloud by calling an API, and the returned text data is obtained.

[0712] 4. Text data analysis methods:

[0713] The server analyzes the text data using Python and NLP libraries (e.g., spaCy). The analysis includes detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions. Furthermore, it detects moral violations using a specific list of prohibited words.

[0714] 5. Emotion Engine:

[0715] The server uses an emotion engine, such as IBM Watson Tone Analyzer, to analyze the user's emotions from the voice data. The emotion engine analyzes the tone and intonation of the voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[0716] 6. Feedback Generation Methods:

[0717] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[0718] 7. How to provide feedback:

[0719] The terminal receives the feedback sent from the server and provides it to the user through a user interface, which may be displayed in audio or text format and configured to be easily understood by the user.

[0720] Specific examples

[0721] For example, say a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to a server. The server then converts the audio data into text using Google Cloud Speech-to-Text.

[0722] The server then parses the text data and checks the following:

[0723] Eliminating meaningless expressions such as "um."

[0724] A proposal to change the inappropriate expression "yabai" to "seiji."

[0725] Suggest adding examples and statistics to increase the specificity of the data.

[0726] We check for any immoral expressions and suggest alternatives.

[0727] At the same time, the emotion engine (IBM Watson Tone Analyzer) analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension" based on the results.

[0728] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[0729] Prompt Sentence Examples

[0730] markdown

[0731] A user has recorded an important business presentation. Analyze the audio data below and generate feedback based on the text data and sentiment analysis results.

[0732] Audio data: [Audio data link or file]

[0733] Information to include in your feedback:

[0734] 1. Eliminating meaningless expressions

[0735] 2. Proposing appropriate alternatives to inappropriate expressions

[0736] 3. Suggestions for improvement (adding specific data or examples, etc.)

[0737] 4. Advice based on sentiment analysis results

[0738] This system allows users to obtain specific improvement measures in terms of both content and emotion, enabling them to improve their communication skills more effectively.

[0739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0740] Step 1:

[0741] Users install a dedicated voice recording application on their smartphone or computer. They launch the application and press the "Start Recording" button to record speech or presentations. The input is the user's voice, and the output is audio data. This audio data is recorded in digital format.

[0742] Specific behavior:

[0743] The user opens the application.

[0744] When a user speaks into the microphone, the application records the voice as digital data.

[0745] Step 2:

[0746] After the device finishes recording, it sends the recorded audio data to the server via an Internet connection. The input is the recorded audio data, and the output is the audio data sent to the server. This transmission is done automatically using the API within the application.

[0747] Specific behavior:

[0748] The device has an internet connection.

[0749] The terminal divides the voice data into packets and sends them to the server over the network.

[0750] Step 3:

[0751] The server analyzes the received voice data using a speech recognition engine such as Google Cloud Speech-to-Text and converts it into text data. The input is voice data and the output is text data. Analysis is performed on the cloud by calling an API.

[0752] Specific behavior:

[0753] The server calls the Google Cloud Speech-to-Text API to upload the audio data.

[0754] The server retrieves the returned text data.

[0755] Step 4:

[0756] The server uses Python and NLP libraries (e.g., spaCy) to perform detailed analysis of the text data. The input for the analysis is the text data, and the output is the analysis results. Specifically, it checks for the following elements: moral violations, misleading expressions, easy-to-understand communication, and witty expressions.

[0757] Specific behavior:

[0758] After the server receives the text data, it executes the Python script.

[0759] The server uses an NLP library to generate the analysis results.

[0760] Step 5:

[0761] The server uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotion from the voice data. The input is the voice data, and the output is the emotion analysis result. The emotion engine analyzes the tone and intonation of the voice to identify the emotion.

[0762] Specific behavior:

[0763] The server calls the IBM Watson Tone Analyzer API to send the audio data.

[0764] The server receives the returned emotion analysis results and reflects them in the analysis.

[0765] Step 6:

[0766] The server generates feedback based on the results of text analysis and sentiment analysis. The input is the analysis results and sentiment analysis results, and the output is feedback data. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[0767] Specific behavior:

[0768] The server generates feedback data based on the analysis results.

[0769] The server converts the feedback data into a format that is easy for the user to understand.

[0770] Step 7:

[0771] The terminal receives the feedback sent from the server and provides it to the user through a user interface. The input is feedback data, and the output is feedback in voice or text format. The feedback is displayed in voice or text format.

[0772] Specific behavior:

[0773] The terminal receives the feedback data via the Internet.

[0774] The device displays the feedback data in its user interface (e.g., smartphone notifications, messages within a specific application).

[0775] Through the above processing steps, the system is able to analyze the content of the user's remarks and presentations and provide effective feedback.

[0776] (Application example 2)

[0777] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0778] Conventional speech analysis systems focus on analyzing the content of a user's speech, but do not analyze emotions, making it difficult to comprehensively improve a user's communication skills. Furthermore, because specific feedback on emotional expressions is not provided, users are unable to acquire specific measures to improve their own emotional expressions. The present invention aims to solve these problems by providing specific feedback on both the content of a user's speech and their emotional expressions, thereby comprehensively improving communication skills.

[0779] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0780] In this invention, the server includes a device for recording user utterances, a device for transmitting the recorded voice data to a remote device, a device for converting the received voice data into text data, a device for analyzing the converted text data and detecting immoral expressions, misleading expressions, easy-to-understand communication methods, and witty expressions, a device for generating feedback based on the analysis results, a device for providing the generated feedback to the user, a device for identifying the user's emotions from the analyzed voice data, and a device for providing feedback that includes suggestions for improving emotional expressions based on the identified emotion analysis results. This allows the user to receive specific feedback not only on the content of their own speech but also on their emotional expressions, thereby enabling them to improve their overall communication skills.

[0781] "User" means an individual or corporation that uses the system.

[0782] "Utterance" refers to content that is verbally expressed by a user.

[0783] A "recording device" is a device for saving a user's speech as voice data.

[0784] "Voice data" refers to data in which a user's speech is saved in digital form.

[0785] "Remote device" refers to a server or cloud that exists away from the user's local device to process data.

[0786] "Text data" is data obtained by converting voice data into text format.

[0787] "Analyzing" is the process of examining the content of data and identifying specific patterns or information.

[0788] "Moral violation" refers to the inclusion of content or expressions that are socially or ethically inappropriate.

[0789] "Misleading expressions" refer to expressions that may be interpreted differently from the meaning intended by the user.

[0790] "Easy-to-understand communication" refers to a way of expressing information so that it can be easily understood by the recipient.

[0791] "Witty expression" refers to an expression that is humorous or intellectually clever.

[0792] "Feedback" refers to evaluations and advice provided based on the user's speech and analysis results.

[0793] "Emotion" refers to a psychological state such as joy, sadness, anger, surprise, or fear that a user expresses while speaking.

[0794] An "identifying apparatus" is a device for detecting and identifying a user's emotion from voice data.

[0795] "Suggestion" is an act of explicitly notifying a specific problem or point for improvement contained in a user's utterance.

[0796] "Improvement suggestions" are specific suggestions for users to improve their speech and emotional expression based on the feedback.

[0797] The present invention is a system that analyzes both the content of user speech and emotional expressions and provides specific feedback. This system includes a series of processes: recording, transmitting the voice data, converting it to text data, analyzing the text data, analyzing emotions, and generating and providing feedback. Each step is described in detail below.

[0798] First, a user opens a dedicated recording application to record their speech. This application runs on a device such as a smartphone, tablet, PC, or robot. The user begins speaking and records it. The recorded audio data is then sent to a server via the Internet.

[0799] The server receives the voice data and then converts it into text data using a speech recognition engine (such as Google's Speech-to-Text API). This converted text data is further analyzed by an analysis engine, which performs functions such as detecting moral violations, detecting misleading expressions, analyzing easy-to-understand communication, and detecting witty expressions.

[0800] The server then uses a sentiment analysis engine to identify the user's emotions from the voice data. The sentiment analysis engine uses a generative AI model, such as IBM Watson's Tone Analyzer, to identify the user's emotions, such as joy, sadness, anger, surprise, and fear. The results of this sentiment analysis, along with text analysis, are reflected in the feedback.

[0801] The feedback generation engine generates specific feedback based on the results of text analysis and sentiment analysis. This feedback includes suggestions for improving not only the content of speech but also the expression of emotions. For example, specific advice such as "You need to be careful because you use 'um' a lot in your speech" or "We recommend that you incorporate breathing techniques to relieve tension during presentations" is provided.

[0802] Finally, the generated feedback is sent to the user's device and provided to them in voice or text format, which the user can refer to and use to improve their own speech and emotional expression.

[0803] As a concrete example, consider a user giving a business presentation. The user opens a recording application for the presentation and records the content of the presentation. Once the recording is complete, the audio data is sent to a server and converted into text data. The server analyzes the converted text and performs sentiment analysis. Based on the analysis results, specific feedback on the user's presentation content and emotional expressions is generated and provided to the user.

[0804] An example prompt is:

[0805] "I'm going to begin my presentation. I'll record everything I say for the next five minutes and provide sentiment analysis and feedback afterward."

[0806] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0807] Step 1:

[0808] The user launches a dedicated recording application. The user presses the record button in the application to start speaking, and when they finish speaking, they press the record button again to stop recording. The input is the user's speech, and the output is the recorded voice data.

[0809] Step 2:

[0810] The device sends the recorded audio data to the server using an internet connection. The input is the recorded audio data, and the output is the audio data sent to the server.

[0811] Step 3:

[0812] The server converts the received voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The input is voice data, and the output is the converted text data.

[0813] Step 4:

[0814] The server passes the converted text data to an analysis engine to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions. The analysis engine performs the analysis using a specific list of prohibited words and other analysis rules. The input is the text data, and the output is the analysis results.

[0815] Step 5:

[0816] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotion from the voice data. Analyzed emotions include joy, sadness, anger, surprise, and fear. The input is the voice data, and the output is the emotion analysis result.

[0817] Step 6:

[0818] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes suggestions for improving content and emotional expression. For example, it includes specific examples such as "You use 'um' a lot in your comments, so be careful" or "You seemed nervous during the presentation, so I recommend trying some breathing techniques to relax." The input is the results of text analysis and sentiment analysis, and the output is feedback.

[0819] Step 7:

[0820] The server sends the generated feedback to the terminal, and the terminal presents the received feedback to the user in voice or text format. The input is the feedback, and the output is the feedback displayed on the user's terminal.

[0821] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0822] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0823] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0824] [Third embodiment]

[0825] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0826] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0827] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0828] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0829] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0830] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0831] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0832] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0833] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0834] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0835] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0836] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0837] The present invention relates to a system for enabling users to effectively improve their speech and presentations, by allowing users to record and analyze speech and receive specific feedback.

[0838] System configuration

[0839] 1. Recording Method:

[0840] Users record speeches and presentations using devices such as smartphones and computers. The devices have a voice recording application installed and can record audio through a microphone.

[0841] 2. Audio data transmission means:

[0842] The device can transmit the recorded audio data to a server via an internet connection.

[0843] 3. Audio data conversion means:

[0844] The server converts the received voice data into text data, using a speech recognition engine to convert the recorded content into text information.

[0845] 4. Text data analysis methods:

[0846] The server analyzes the converted text data. The purpose of the analysis is to detect immoral content, misleading expressions, easy-to-understand communication, and witty expressions. This is done using a specific list of prohibited words and natural language processing technology.

[0847] 5. Feedback Generation Methods:

[0848] The server generates feedback based on the analysis results, including suggestions for correcting misused expressions and making improvements.

[0849] 6. How to provide feedback:

[0850] The terminal receives the feedback sent from the server and provides it to the user, which is displayed in voice or text format so that the user can easily check it.

[0851] Specific examples

[0852] For example, consider a user giving a business presentation. The user opens a recording application and starts the presentation. Once the recording is complete, the audio data is sent from the device to a server. The server uses a speech recognition engine to convert the audio into text.

[0853] The server then analyzes the text data, checking for example:

[0854] Detect and remove meaningless expressions such as "um."

[0855] We propose changing the inappropriate expression "yabai" to "seijiu."

[0856] Suggest adding examples or statistics to make the data more specific.

[0857] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[0858] Once the analysis is complete, the server generates specific feedback and sends it to the device, which displays it via a user interface, allowing the user to see areas for improvement. Feedback can also be provided in audio format if desired, allowing users flexibility in how they receive it.

[0859] The present invention allows users to objectively evaluate their own comments and obtain specific measures for improvement, thereby improving the quality of communication.

[0860] The processing flow will be explained below.

[0861] Step 1:

[0862] The user opens a voice recording application. The device displays a "Start Recording" button.

[0863] Step 2:

[0864] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[0865] Step 3:

[0866] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[0867] Step 4:

[0868] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[0869] Step 5:

[0870] The device sends the generated audio data file to a server via the Internet.

[0871] Step 6:

[0872] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[0873] Step 7:

[0874] The server parses the text data, which includes the following elements:

[0875] Detecting moral violations (matching against a specific list of banned words).

[0876] Evaluate whether there were any problems with the way you communicated (check grammar and structure).

[0877] Suggestions for ways to communicate information in an easy-to-understand manner (simplifying or elaborating information).

[0878] Step 8:

[0879] The server generates feedback based on the analysis, including suggestions for ethical violations, ways to improve communication, and specific examples and statistics.

[0880] Step 9:

[0881] The server then sends the generated feedback to the device via the Internet.

[0882] Step 10:

[0883] The device receives the feedback and displays it through the user interface, and the user can view the feedback on the screen.

[0884] Step 11:

[0885] The device can also provide feedback in audio format, allowing users to receive feedback either in text or audio format.

[0886] By following these steps, users can identify specific areas for improvement based on the analysis of their own comments and use them in their next presentation or conversation.

[0887] Example 1

[0888] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0889] Currently, methods for users to improve their speech and presentation skills are problematic because they require a lot of time and effort. For example, users must record their speech, transcribe it into text, and then analyze it. Furthermore, manually analyzing the transcribed content and obtaining feedback requires advanced knowledge and specialized tools. Therefore, there is a need for a method to effectively and efficiently improve one's communication skills.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0891] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to a computer, means for converting the received voice data into text information, means for analyzing the converted text information to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions, means for generating feedback based on the analysis results, and means for providing the generated feedback to the user, thereby enabling the user to efficiently improve their speech and presentation in a short amount of time.

[0892] "User" refers to a person who uses the system to record, analyze, and receive feedback on their own speech or presentation.

[0893] "Utterance" refers to the words spoken by a user or the content of a presentation.

[0894] "Recording means" refers to a device or application that allows a user to record their own speech as audio data.

[0895] "Voice data" refers to data that is a digital recording of a user's speech.

[0896] "Means for transmitting to a computer" refers to a process or system for transferring recorded audio data over the Internet to a computing device such as a server.

[0897] "Text information" refers to data that has been converted from audio data into text format.

[0898] "Means for converting" refers to software or hardware for converting voice data into text information.

[0899] "Means for analyzing" refers to a system for analyzing the converted text information and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions.

[0900] "Moral violations" refer to expressions or content that are socially unacceptable.

[0901] "Misleading expressions" refer to expressions that are not clear and may lead to a misunderstanding on the part of the recipient.

[0902] "Easy-to-understand communication" refers to a way of communicating that is clear and easy for the recipient to understand.

[0903] "Witty expressions" refer to expressions that are witty and interesting.

[0904] "Means for generating feedback" refers to a system that generates suggestions and recommendations for improvement based on the analysis results.

[0905] "Generated feedback" refers to specific improvement plans and suggestions created based on the analysis results.

[0906] "Means for providing" refers to the process or system for visually or audibly conveying the generated feedback to the user.

[0907] MODE FOR CARRYING OUT THE INVENTION

[0908] The present invention relates to a system for enabling users to effectively improve their speech and presentations. The system allows users to record and analyze speech and receive specific feedback. Specific embodiments of the present invention are described below.

[0909] Hardware and Software Configuration

[0910] This system mainly uses the following hardware and software:

[0911] Hardware: smartphones, computers, servers

[0912] Software: Voice recording application, Internet connection, speech recognition engine (e.g., Google Cloud Speech-to-Text API), natural language processing engine (e.g., TensorFlow, NLTK)

[0913] Overall processing

[0914] Users record their own speech or presentation, send the audio data to the server, where it is converted into text information, analyzed, and specific feedback is generated based on the analysis results and provided to the user.

[0915] Detailed processing steps

[0916] 1. Recording:

[0917] Users use smartphones or computers to record their own speeches or presentations. These devices have voice recording applications installed. For example, recordings can be made using "Voice Memos" or "Google Translate." During recording, the device's microphone picks up the user's voice and stores it as digital audio data.

[0918] 2. Audio data transmission:

[0919] After the device has finished recording, it sends the saved audio data to a server over the Internet, encrypted using the HTTPS protocol.

[0920] 3. Audio data conversion:

[0921] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The speech recognition engine analyzes the voice waveform and converts it into corresponding text information.

[0922] 4. Text data analysis:

[0923] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow, NLTK). The analysis includes checking grammar, detecting immoral expressions, detecting meaningless expressions, and extracting witty expressions. Problematic expressions are identified using a list of prohibited words and pre-trained AI models.

[0924] 5. Feedback Generation:

[0925] Based on the analysis, the server generates specific feedback for the user, such as suggestions to replace the expression "yabai" with "seiji" or to remove the meaningless "uh." The feedback is prepared in text and audio formats (using a TTS engine).

[0926] 6. Providing Feedback:

[0927] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually understandable format and, if necessary, can also be provided in audio format.

[0928] Specific examples

[0929] For example, consider a user giving a business presentation. The user records the presentation using the recording app "Voice Memos" on their smartphone. Once the recording is complete, the audio data is sent from the device to a server. The server converts the audio into text data using the Google Cloud Speech-to-Text API. The server then analyzes the text data using TensorFlow or NLTK to generate feedback, such as the following:

[0930] Suggestions to detect and remove nonsensical expressions such as "um."

[0931] A proposal to change the inappropriate expression "yabai" to "seiji."

[0932] Suggest adding statistics and examples to make it more concrete.

[0933] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[0934] The generated feedback is sent to the device and displayed visually through a recording application or a dedicated user interface, and can also be played back in audio format.

[0935] Prompt Sentence Examples

[0936] Transcribe the audio recording of a user's business presentation into text and generate feedback based on:

[0937] Detecting and removing nonsense

[0938] Correction of inappropriate expressions

[0939] Additional examples and statistics

[0940] Proposals for improving immoral expressions

[0941] The generated feedback is provided to the user in an easy-to-understand voice and text format.

[0942] This system allows users to quickly receive specific feedback to efficiently improve their own speech and presentations, thereby improving their communication skills.

[0943] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0944] Step 1:

[0945] Users use their smartphones or computers to record their own speech or presentation. They launch a voice recording application installed on their device (e.g., Voice Memos or Google Translate), press the record button, and begin speaking. While recording, the device's microphone picks up the user's voice, which is then saved as digital audio data. When recording is complete, the user presses the stop button to end the recording.

[0946] Input: User's voice

[0947] Output: Digital audio data

[0948] Step 2:

[0949] The device sends the saved audio data to the server over the internet as soon as the user finishes recording. The audio data is sent encrypted using a secure protocol (HTTPS). This process ensures the data is transferred from the device to the server, such as by pressing a "send" button after completing the recording.

[0950] Input: Digital audio data

[0951] Output: Audio data sent to the server

[0952] Step 3:

[0953] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, where the voice waveform is analyzed and mapped to a corresponding string. First, the voice data is input into a speech recognition engine, and then the text information is output from the API.

[0954] Input: Audio data sent to the server

[0955] Output: Text data

[0956] Step 4:

[0957] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow or NLTK). During this analysis stage, the following processes take place:

[0958] Detecting immoral expressions

[0959] Detecting and removing meaningless expressions (e.g., "um")

[0960] Identifying misleading statements and generating correction suggestions

[0961] Extracting witty expressions

[0962] For example, text data is input into an analysis engine, and the analyzed information is output in JSON format.

[0963] Input: Text data

[0964] Output: Analysis results

[0965] Step 5:

[0966] The server generates specific feedback for the user based on the results of analyzing the text data. The generated feedback includes suggestions for correcting misused expressions and suggestions for improvement. The generative AI model prepares the text and audio data for the feedback. For example, it generates suggestions for correcting inappropriate parts of the text data and adding specific examples and statistics.

[0967] Input: Analysis results

[0968] Output: Feedback data (text and audio)

[0969] Step 6:

[0970] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually verifiable format, and audio feedback is also provided if necessary. For example, the terminal may display a highlight of the feedback on the screen and provide an audio playback button, allowing the user to check the feedback content in detail.

[0971] Input: Feedback data

[0972] Output: Feedback provided to the user

[0973] By following the above specific steps, users can efficiently improve the quality of their own speech and presentations.

[0974] (Application example 1)

[0975] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0976] The goal of this invention is to provide a system that allows users, particularly staff working in the customer service industry, to effectively improve their own speech and conversation. Conventional methods have had the problem of requiring a great deal of time and effort to improve customer service skills, and of making it difficult to obtain specific feedback. Therefore, there is a need for a system that allows users to quickly and effectively improve their skills by recording and analyzing their conversations while serving customers and receiving appropriate feedback.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0978] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to the server, means for converting the received voice data into text data, means for analyzing the converted text data and detecting immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for generating feedback based on the analysis results, means for providing the generated feedback to the user, and means for analyzing conversations during customer service using a specific natural language processing technology and suggesting appropriate methods of customer service, thereby enabling the user to quickly and effectively improve their customer service skills.

[0979] The "means for recording user utterances" is a device or function that allows a user to record his or her utterances in audio format.

[0980] The "means for transmitting recorded voice data to a server" refers to a device or software for transferring recorded voice data to a server via a network.

[0981] The "means for converting received voice data into text data" refers to a device or software that converts voice data received by the server into character information (text) using voice recognition technology.

[0982] "Means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions" refers to devices or software that evaluate the linguistic elements contained in the text data and identify inappropriate expressions or expressions that need improvement.

[0983] The "means for generating feedback based on the analysis results" refers to a device or software that generates specific improvement proposals or advice for the user based on the analyzed data.

[0984] A "means for providing generated feedback to a user" is a device or software for presenting generated feedback to a user in the form of audio or text.

[0985] "Means for analyzing conversations during customer service using specific natural language processing technology and proposing appropriate methods of customer service" refers to a device or software that uses natural language processing (NLP) technology to analyze conversation data during customer service and present more appropriate and effective methods of customer service to the user.

[0986] The system of the present invention records and analyzes the user's speech to provide specific feedback and improve customer service skills. This system is composed of the following components:

[0987] Recording medium

[0988] Users use a smartphone or smart glasses to record their speech and conversations. The recording is done through a voice input device (microphone) and saved as audio data.

[0989] Audio data transmission means

[0990] The recorded audio data is transmitted over the internet to a server, with an application installed on the smartphone or smart glasses managing the transmission process.

[0991] Audio data conversion means

[0992] The server converts the received voice data into text data using a speech recognition engine such as the Google Cloud Speech-to-Text API, which is then used for the subsequent analysis process.

[0993] Text data analysis methods

[0994] The server analyzes the converted text data using natural language processing techniques (e.g., TensorFlow or SpaCy). The goal of the analysis is to detect the following elements:

[0995] meaningless expressions

[0996] Inappropriate expressions

[0997] Easy-to-understand communication

[0998] Witty expressions

[0999] Feedback Generation Method

[1000] Based on the analysis, the server generates specific feedback for the user, including suggestions for correcting misused expressions and making improvements.

[1001] Feedback methods

[1002] The generated feedback is sent to the user's smartphone or smart glasses and provided to them in voice or text format, allowing them to receive specific advice on how to improve their customer service skills.

[1003] A means of analyzing conversations during customer service and suggesting appropriate ways to serve customers

[1004] The server uses specific natural language processing technology to analyze conversation data during customer service, which allows it to propose appropriate customer service methods. The analysis also uses a list of prohibited words to detect moral violations.

[1005] Specific use cases

[1006] For example, consider a case where a user wants to record a conversation while serving a customer. The user opens a recording application on their smartphone and starts serving the customer. Once the recording is complete, the audio data is automatically sent to the server. The server converts the received audio data into text using a speech recognition engine, and then analyzes it using natural language processing technology. By using prompt sentences like the following, specific feedback can be generated and provided to the user based on the analysis results:

[1007] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[1008] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[1009] In this way, users can receive feedback based on the analysis results and learn specific areas for improvement, thereby effectively improving their customer service skills.

[1010] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1011] Step 1:

[1012] Users use smartphones or smart glasses to record conversations while serving customers. The input is audio data, and the recorded audio is saved as output. Specifically, users launch the application and press the record button to record the conversation.

[1013] Step 2:

[1014] The recorded audio data is sent from the device to the server via the Internet. The input is the audio data generated in step 1, and the server receives the audio data as output. Specifically, the device application automatically uploads the audio data to the server after recording is complete.

[1015] Step 3:

[1016] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. Specifically, the server sends the voice data to the speech recognition engine and saves the returned text data.

[1017] Step 4:

[1018] The server analyzes the converted text data using natural language processing technology (for example, TensorFlow or SpaCy). The input is text data, and the analysis results are obtained as output. Specifically, the server analyzes the text data to detect meaningless expressions, inappropriate expressions, easy-to-understand expressions, and witty expressions.

[1019] Step 5:

[1020] Based on the analysis results, the server generates feedback by inputting a prompt sentence to the generative AI model. The input is the analysis results and the prompt sentence, and the output is feedback. Specifically, the server sends the following prompt sentence to the generative AI model and saves the returned result as feedback:

[1021] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[1022] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[1023] Step 6:

[1024] The server sends the generated feedback to the terminal in the form of voice or text. The input is feedback data, and the terminal displays or plays the feedback as output. In concrete terms, the feedback data is sent to the terminal, and the application on the terminal displays or plays the feedback to the user in voice.

[1025] Step 7:

[1026] The user checks the feedback displayed on the device and uses it to improve their customer service skills. The input is the feedback information, and the output is the user's understanding of the areas for improvement. Specifically, the user reads the feedback displayed on the screen and reflects it in their next customer service encounter.

[1027] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1028] This system allows users to record their own speech and presentations, analyze their emotions, and provide more specific feedback to improve their communication skills.By combining it with an emotion engine, the system analyzes not only the content of speech but also the emotional expression, and reflects this in the feedback.

[1029] System configuration

[1030] 1. Recording Method:

[1031] Users use devices such as smartphones or computers to open a dedicated audio recording application and record speeches or presentations.

[1032] 2. Audio data transmission means:

[1033] The device sends the recorded audio data to a server via an internet connection.

[1034] 3. Audio data conversion means:

[1035] The server uses a speech recognition engine to convert the received voice data into text data, which is then used for analysis.

[1036] 4. Text data analysis methods:

[1037] The server parses the converted text data, which includes the following elements:

[1038] Detecting moral violations (matching against a specific list of banned words).

[1039] Detect misleading statements and suggest improvements.

[1040] Proposal for an easy-to-understand explanation.

[1041] A suggestion for witty expressions.

[1042] 5. Emotion Engine:

[1043] The server uses an emotion engine to identify the user's emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and reflects the results in the analysis.

[1044] 6. Feedback Generation Methods:

[1045] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech, as well as suggestions for improvement regarding emotional expression.

[1046] 7. How to provide feedback:

[1047] The terminal receives the feedback sent from the server and provides it to the user in the form of voice or text, making it easy for the user to understand.

[1048] Specific examples

[1049] For example, suppose a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to the server. The server then uses a speech recognition engine to convert the audio data into text.

[1050] The server then parses the text data and checks the following:

[1051] Eliminating meaningless expressions such as "um."

[1052] A proposal to change the inappropriate expression "yabai" to "seiji."

[1053] Suggest adding examples and statistics to increase the specificity of the data.

[1054] We check for any immoral expressions and suggest alternatives.

[1055] At the same time, the emotion engine analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension."

[1056] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[1057] In this way, the present invention provides users with concrete improvements in terms of both content and emotion, and supports the improvement of communication skills.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] The user opens a voice recording application. The device displays a "Start Recording" button.

[1061] Step 2:

[1062] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[1063] Step 3:

[1064] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[1065] Step 4:

[1066] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[1067] Step 5:

[1068] The device then sends the generated audio data file to the server via the Internet.

[1069] Step 6:

[1070] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[1071] Step 7:

[1072] The server parses the text data, which includes the following elements:

[1073] Detecting moral violations (matching against a specific list of banned words).

[1074] Detect misleading statements and suggest improvements.

[1075] Proposal for an easy-to-understand explanation.

[1076] A suggestion for witty expressions.

[1077] Step 8:

[1078] The server uses an emotion engine to identify emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and incorporates the results into the analysis.

[1079] Step 9:

[1080] The server generates feedback based on the analysis and sentiment analysis results, including advice on the content of the speech and suggestions for improvement regarding emotional expression.

[1081] Step 10:

[1082] The server then sends the generated feedback to the device over the Internet.

[1083] Step 11:

[1084] The device receives the feedback and displays it through a user interface, allowing the user to check the analysis results and the feedback content.

[1085] Step 12:

[1086] The device also provides feedback in audio format, and the user can receive feedback in text or audio format.

[1087] Step 13:

[1088] Based on the feedback provided, users can identify areas for improvement in their own comments and presentations and use this information to improve their comments in future sessions.

[1089] This specific processing flow allows users to receive comprehensive feedback not only on the content of their own statements but also on their emotional expressions, thereby improving the quality of their communication.

[1090] Example 2

[1091] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1092] In today's business and educational settings, improving the quality of speech and presentations is extremely important. However, conventional feedback systems focus on analyzing speech content, failing to consider the speaker's emotions and tone when providing feedback. Furthermore, they focus too much on eliminating expressions that violate morals or are misleading, failing to provide more specific improvement measures. To address these issues, it is necessary to analyze users' emotions and incorporate them into feedback to improve communication skills more effectively.

[1093] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1094] In this invention, the server includes means for converting received voice data into text data, means for analyzing the converted text data to detect immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for analyzing the user's emotions from the voice data, and means for generating feedback based on the analysis results and emotion analysis results, thereby enabling more specific and multifaceted feedback that includes not only the content of the speech but also emotional expressions.

[1095] "User" refers to any individual or organization that uses the system to record speeches or presentations and receive feedback.

[1096] "Means for recording speech" refers to a device or application for recording a user's speech in digital form as audio data.

[1097] "Means for transmitting audio data to a server" refers to a device or software that includes network functionality for transmitting recorded audio data to a remote server via the Internet.

[1098] "Means for converting voice data into text data" refers to software or hardware for converting voice data into text using voice recognition technology.

[1099] "Means for analyzing text data" refers to software or hardware that includes natural language processing technology for detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions based on the converted text data.

[1100] "Means for analyzing user emotions from voice data" refers to algorithms that analyze the tone and intonation of voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[1101] The "means for generating feedback" refers to software or hardware for automatically constructing advice or improvement suggestions to be provided to the user based on the results of text data analysis and sentiment analysis.

[1102] "Means for providing feedback to the user" refers to a display device or application for presenting the generated feedback to the user in audio or text form.

[1103] This invention is a system that allows users to record their own speech or presentation and analyze their emotions to provide more specific feedback and improve their communication skills. This system is configured using the following hardware and software.

[1104] 1. Recording Method:

[1105] A user uses a device such as a smartphone or computer to open a dedicated voice recording application (e.g., a general audio recording application) to record speech or a presentation. This application records the user's voice as digital data.

[1106] 2. Means of data transmission:

[1107] The device sends the recorded audio data to a server via an internet connection, typically Wi-Fi or a mobile data network.

[1108] 3. Audio data conversion means:

[1109] The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text, and the voice data is analyzed in the cloud by calling an API, and the returned text data is obtained.

[1110] 4. Text data analysis methods:

[1111] The server analyzes the text data using Python and NLP libraries (e.g., spaCy). The analysis includes detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions. Furthermore, it detects moral violations using a specific list of prohibited words.

[1112] 5. Emotion Engine:

[1113] The server uses an emotion engine, such as IBM Watson Tone Analyzer, to analyze the user's emotions from the voice data. The emotion engine analyzes the tone and intonation of the voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[1114] 6. Feedback Generation Methods:

[1115] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[1116] 7. How to provide feedback:

[1117] The terminal receives the feedback sent from the server and provides it to the user through a user interface, which may be displayed in audio or text format and configured to be easily understood by the user.

[1118] Specific examples

[1119] For example, say a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to a server. The server then converts the audio data into text using Google Cloud Speech-to-Text.

[1120] The server then parses the text data and checks the following:

[1121] Eliminating meaningless expressions such as "um."

[1122] A proposal to change the inappropriate expression "yabai" to "seiji."

[1123] Suggest adding examples and statistics to increase the specificity of the data.

[1124] We check for any immoral expressions and suggest alternatives.

[1125] At the same time, the emotion engine (IBM Watson Tone Analyzer) analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension" based on the results.

[1126] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[1127] Prompt Sentence Examples

[1128] markdown

[1129] A user has recorded an important business presentation. Analyze the audio data below and generate feedback based on the text data and sentiment analysis results.

[1130] Audio data: [Audio data link or file]

[1131] Information to include in your feedback:

[1132] 1. Eliminating meaningless expressions

[1133] 2. Proposing appropriate alternatives to inappropriate expressions

[1134] 3. Suggestions for improvement (adding specific data or examples, etc.)

[1135] 4. Advice based on sentiment analysis results

[1136] This system allows users to obtain specific improvement measures in terms of both content and emotion, enabling them to improve their communication skills more effectively.

[1137] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1138] Step 1:

[1139] Users install a dedicated voice recording application on their smartphone or computer. They launch the application and press the "Start Recording" button to record speech or presentations. The input is the user's voice, and the output is audio data. This audio data is recorded in digital format.

[1140] Specific behavior:

[1141] The user opens the application.

[1142] When a user speaks into the microphone, the application records the voice as digital data.

[1143] Step 2:

[1144] After the device finishes recording, it sends the recorded audio data to the server via an Internet connection. The input is the recorded audio data, and the output is the audio data sent to the server. This transmission is done automatically using the API within the application.

[1145] Specific behavior:

[1146] The device has an internet connection.

[1147] The terminal divides the voice data into packets and sends them to the server over the network.

[1148] Step 3:

[1149] The server analyzes the received voice data using a speech recognition engine such as Google Cloud Speech-to-Text and converts it into text data. The input is voice data and the output is text data. Analysis is performed on the cloud by calling an API.

[1150] Specific behavior:

[1151] The server calls the Google Cloud Speech-to-Text API to upload the audio data.

[1152] The server retrieves the returned text data.

[1153] Step 4:

[1154] The server uses Python and NLP libraries (e.g., spaCy) to perform detailed analysis of the text data. The input for the analysis is the text data, and the output is the analysis results. Specifically, it checks for the following elements: moral violations, misleading expressions, easy-to-understand communication, and witty expressions.

[1155] Specific behavior:

[1156] After the server receives the text data, it executes the Python script.

[1157] The server uses an NLP library to generate the analysis results.

[1158] Step 5:

[1159] The server uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotion from the voice data. The input is the voice data, and the output is the emotion analysis result. The emotion engine analyzes the tone and intonation of the voice to identify the emotion.

[1160] Specific behavior:

[1161] The server calls the IBM Watson Tone Analyzer API to send the audio data.

[1162] The server receives the returned emotion analysis results and reflects them in the analysis.

[1163] Step 6:

[1164] The server generates feedback based on the results of text analysis and sentiment analysis. The input is the analysis results and sentiment analysis results, and the output is feedback data. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[1165] Specific behavior:

[1166] The server generates feedback data based on the analysis results.

[1167] The server converts the feedback data into a format that is easy for the user to understand.

[1168] Step 7:

[1169] The terminal receives the feedback sent from the server and provides it to the user through a user interface. The input is feedback data, and the output is feedback in voice or text format. The feedback is displayed in voice or text format.

[1170] Specific behavior:

[1171] The terminal receives the feedback data via the Internet.

[1172] The device displays the feedback data in its user interface (e.g., smartphone notifications, messages within a specific application).

[1173] Through the above processing steps, the system is able to analyze the content of the user's remarks and presentations and provide effective feedback.

[1174] (Application example 2)

[1175] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1176] Conventional speech analysis systems focus on analyzing the content of a user's speech, but do not analyze emotions, making it difficult to comprehensively improve a user's communication skills. Furthermore, because specific feedback on emotional expressions is not provided, users are unable to acquire specific measures to improve their own emotional expressions. The present invention aims to solve these problems by providing specific feedback on both the content of a user's speech and their emotional expressions, thereby comprehensively improving communication skills.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1178] In this invention, the server includes a device for recording user utterances, a device for transmitting the recorded voice data to a remote device, a device for converting the received voice data into text data, a device for analyzing the converted text data and detecting immoral expressions, misleading expressions, easy-to-understand communication methods, and witty expressions, a device for generating feedback based on the analysis results, a device for providing the generated feedback to the user, a device for identifying the user's emotions from the analyzed voice data, and a device for providing feedback that includes suggestions for improving emotional expressions based on the identified emotion analysis results. This allows the user to receive specific feedback not only on the content of their own speech but also on their emotional expressions, thereby enabling them to improve their overall communication skills.

[1179] "User" means an individual or corporation that uses the system.

[1180] "Utterance" refers to content that is verbally expressed by a user.

[1181] A "recording device" is a device for saving a user's speech as voice data.

[1182] "Voice data" refers to data in which a user's speech is saved in digital form.

[1183] "Remote device" refers to a server or cloud that exists away from the user's local device to process data.

[1184] "Text data" is data obtained by converting voice data into text format.

[1185] "Analyzing" is the process of examining the content of data and identifying specific patterns or information.

[1186] "Moral violation" refers to the inclusion of content or expressions that are socially or ethically inappropriate.

[1187] "Misleading expressions" refer to expressions that may be interpreted differently from the meaning intended by the user.

[1188] "Easy-to-understand communication" refers to a way of expressing information so that it can be easily understood by the recipient.

[1189] "Witty expression" refers to an expression that is humorous or intellectually clever.

[1190] "Feedback" refers to evaluations and advice provided based on the user's speech and analysis results.

[1191] "Emotion" refers to a psychological state such as joy, sadness, anger, surprise, or fear that a user expresses while speaking.

[1192] An "identifying apparatus" is a device for detecting and identifying a user's emotion from voice data.

[1193] "Suggestion" is an act of explicitly notifying a specific problem or point for improvement contained in a user's utterance.

[1194] "Improvement suggestions" are specific suggestions for users to improve their speech and emotional expression based on the feedback.

[1195] The present invention is a system that analyzes both the content of user speech and emotional expressions and provides specific feedback. This system includes a series of processes: recording, transmitting the voice data, converting it to text data, analyzing the text data, analyzing emotions, and generating and providing feedback. Each step is described in detail below.

[1196] First, a user opens a dedicated recording application to record their speech. This application runs on a device such as a smartphone, tablet, PC, or robot. The user begins speaking and records it. The recorded audio data is then sent to a server via the Internet.

[1197] The server receives the voice data and then converts it into text data using a speech recognition engine (such as Google's Speech-to-Text API). This converted text data is further analyzed by an analysis engine, which performs functions such as detecting moral violations, detecting misleading expressions, analyzing easy-to-understand communication, and detecting witty expressions.

[1198] The server then uses a sentiment analysis engine to identify the user's emotions from the voice data. The sentiment analysis engine uses a generative AI model, such as IBM Watson's Tone Analyzer, to identify the user's emotions, such as joy, sadness, anger, surprise, and fear. The results of this sentiment analysis, along with text analysis, are reflected in the feedback.

[1199] The feedback generation engine generates specific feedback based on the results of text analysis and sentiment analysis. This feedback includes suggestions for improving not only the content of speech but also the expression of emotions. For example, specific advice such as "You need to be careful because you use 'um' a lot in your speech" or "We recommend that you incorporate breathing techniques to relieve tension during presentations" is provided.

[1200] Finally, the generated feedback is sent to the user's device and provided to them in voice or text format, which the user can refer to and use to improve their own speech and emotional expression.

[1201] As a concrete example, consider a user giving a business presentation. The user opens a recording application for the presentation and records the content of the presentation. Once the recording is complete, the audio data is sent to a server and converted into text data. The server analyzes the converted text and performs sentiment analysis. Based on the analysis results, specific feedback on the user's presentation content and emotional expressions is generated and provided to the user.

[1202] An example prompt is:

[1203] "I'm going to begin my presentation. I'll record everything I say for the next five minutes and provide sentiment analysis and feedback afterward."

[1204] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1205] Step 1:

[1206] The user launches a dedicated recording application. The user presses the record button in the application to start speaking, and when they finish speaking, they press the record button again to stop recording. The input is the user's speech, and the output is the recorded voice data.

[1207] Step 2:

[1208] The device sends the recorded audio data to the server using an internet connection. The input is the recorded audio data, and the output is the audio data sent to the server.

[1209] Step 3:

[1210] The server converts the received voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The input is voice data, and the output is the converted text data.

[1211] Step 4:

[1212] The server passes the converted text data to an analysis engine to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions. The analysis engine performs the analysis using a specific list of prohibited words and other analysis rules. The input is the text data, and the output is the analysis results.

[1213] Step 5:

[1214] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotion from the voice data. Analyzed emotions include joy, sadness, anger, surprise, and fear. The input is the voice data, and the output is the emotion analysis result.

[1215] Step 6:

[1216] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes suggestions for improving content and emotional expression. For example, it includes specific examples such as "You use 'um' a lot in your comments, so be careful" or "You seemed nervous during the presentation, so I recommend trying some breathing techniques to relax." The input is the results of text analysis and sentiment analysis, and the output is feedback.

[1217] Step 7:

[1218] The server sends the generated feedback to the terminal, and the terminal presents the received feedback to the user in voice or text format. The input is the feedback, and the output is the feedback displayed on the user's terminal.

[1219] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1220] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1221] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1222] [Fourth embodiment]

[1223] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1224] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1225] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1226] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1227] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1229] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1230] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1231] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1232] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1233] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1234] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1235] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1236] The present invention relates to a system for enabling users to effectively improve their speech and presentations, by allowing users to record and analyze speech and receive specific feedback.

[1237] System configuration

[1238] 1. Recording Method:

[1239] Users record speeches and presentations using devices such as smartphones and computers. The devices have a voice recording application installed and can record audio through a microphone.

[1240] 2. Audio data transmission means:

[1241] The device can transmit the recorded audio data to a server via an internet connection.

[1242] 3. Audio data conversion means:

[1243] The server converts the received voice data into text data, using a speech recognition engine to convert the recorded content into text information.

[1244] 4. Text data analysis methods:

[1245] The server analyzes the converted text data. The purpose of the analysis is to detect immoral content, misleading expressions, easy-to-understand communication, and witty expressions. This is done using a specific list of prohibited words and natural language processing technology.

[1246] 5. Feedback Generation Methods:

[1247] The server generates feedback based on the analysis results, including suggestions for correcting misused expressions and making improvements.

[1248] 6. How to provide feedback:

[1249] The terminal receives the feedback sent from the server and provides it to the user, which is displayed in voice or text format so that the user can easily check it.

[1250] Specific examples

[1251] For example, consider a user giving a business presentation. The user opens a recording application and starts the presentation. Once the recording is complete, the audio data is sent from the device to a server. The server uses a speech recognition engine to convert the audio into text.

[1252] The server then analyzes the text data, checking for example:

[1253] Detect and remove meaningless expressions such as "um."

[1254] We propose changing the inappropriate expression "yabai" to "seijiu."

[1255] Suggest adding examples or statistics to make the data more specific.

[1256] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[1257] Once the analysis is complete, the server generates specific feedback and sends it to the device, which displays it via a user interface, allowing the user to see areas for improvement. Feedback can also be provided in audio format if desired, allowing users flexibility in how they receive it.

[1258] The present invention allows users to objectively evaluate their own comments and obtain specific measures for improvement, thereby improving the quality of communication.

[1259] The processing flow will be explained below.

[1260] Step 1:

[1261] The user opens a voice recording application. The device displays a "Start Recording" button.

[1262] Step 2:

[1263] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[1264] Step 3:

[1265] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[1266] Step 4:

[1267] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[1268] Step 5:

[1269] The device sends the generated audio data file to a server via the Internet.

[1270] Step 6:

[1271] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[1272] Step 7:

[1273] The server parses the text data, which includes the following elements:

[1274] Detecting moral violations (matching against a specific list of banned words).

[1275] Evaluate whether there were any problems with the way you communicated (check grammar and structure).

[1276] Suggestions for ways to communicate information in an easy-to-understand manner (simplifying or elaborating information).

[1277] Step 8:

[1278] The server generates feedback based on the analysis, including suggestions for ethical violations, ways to improve communication, and specific examples and statistics.

[1279] Step 9:

[1280] The server then sends the generated feedback to the device via the Internet.

[1281] Step 10:

[1282] The device receives the feedback and displays it through the user interface, and the user can view the feedback on the screen.

[1283] Step 11:

[1284] The device can also provide feedback in audio format, allowing users to receive feedback either in text or audio format.

[1285] By following these steps, users can identify specific areas for improvement based on the analysis of their own comments and use them in their next presentation or conversation.

[1286] Example 1

[1287] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1288] Currently, methods for users to improve their speech and presentation skills are problematic because they require a lot of time and effort. For example, users must record their speech, transcribe it into text, and then analyze it. Furthermore, manually analyzing the transcribed content and obtaining feedback requires advanced knowledge and specialized tools. Therefore, there is a need for a method to effectively and efficiently improve one's communication skills.

[1289] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1290] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to a computer, means for converting the received voice data into text information, means for analyzing the converted text information to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions, means for generating feedback based on the analysis results, and means for providing the generated feedback to the user, thereby enabling the user to efficiently improve their speech and presentation in a short amount of time.

[1291] "User" refers to a person who uses the system to record, analyze, and receive feedback on their own speech or presentation.

[1292] "Utterance" refers to the words spoken by a user or the content of a presentation.

[1293] "Recording means" refers to a device or application that allows a user to record their own speech as audio data.

[1294] "Voice data" refers to data that is a digital recording of a user's speech.

[1295] "Means for transmitting to a computer" refers to a process or system for transferring recorded audio data over the Internet to a computing device such as a server.

[1296] "Text information" refers to data that has been converted from audio data into text format.

[1297] "Means for converting" refers to software or hardware for converting voice data into text information.

[1298] "Means for analyzing" refers to a system for analyzing the converted text information and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions.

[1299] "Moral violations" refer to expressions or content that are socially unacceptable.

[1300] "Misleading expressions" refer to expressions that are not clear and may lead to a misunderstanding on the part of the recipient.

[1301] "Easy-to-understand communication" refers to a way of communicating that is clear and easy for the recipient to understand.

[1302] "Witty expressions" refer to expressions that are witty and interesting.

[1303] "Means for generating feedback" refers to a system that generates suggestions and recommendations for improvement based on the analysis results.

[1304] "Generated feedback" refers to specific improvement plans and suggestions created based on the analysis results.

[1305] "Means for providing" refers to the process or system for visually or audibly conveying the generated feedback to the user.

[1306] MODE FOR CARRYING OUT THE INVENTION

[1307] The present invention relates to a system for enabling users to effectively improve their speech and presentations. The system allows users to record and analyze speech and receive specific feedback. Specific embodiments of the present invention are described below.

[1308] Hardware and Software Configuration

[1309] This system mainly uses the following hardware and software:

[1310] Hardware: smartphones, computers, servers

[1311] Software: Voice recording application, Internet connection, speech recognition engine (e.g., Google Cloud Speech-to-Text API), natural language processing engine (e.g., TensorFlow, NLTK)

[1312] Overall processing

[1313] Users record their own speech or presentation, send the audio data to the server, where it is converted into text information, analyzed, and specific feedback is generated based on the analysis results and provided to the user.

[1314] Detailed processing steps

[1315] 1. Recording:

[1316] Users use smartphones or computers to record their own speeches or presentations. These devices have voice recording applications installed. For example, recordings can be made using "Voice Memos" or "Google Translate." During recording, the device's microphone picks up the user's voice and stores it as digital audio data.

[1317] 2. Audio data transmission:

[1318] After the device has finished recording, it sends the saved audio data to a server over the Internet, encrypted using the HTTPS protocol.

[1319] 3. Audio data conversion:

[1320] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The speech recognition engine analyzes the voice waveform and converts it into corresponding text information.

[1321] 4. Text data analysis:

[1322] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow, NLTK). The analysis includes checking grammar, detecting immoral expressions, detecting meaningless expressions, and extracting witty expressions. Problematic expressions are identified using a list of prohibited words and pre-trained AI models.

[1323] 5. Feedback Generation:

[1324] Based on the analysis, the server generates specific feedback for the user, such as suggestions to replace the expression "yabai" with "seiji" or to remove the meaningless "uh." The feedback is prepared in text and audio formats (using a TTS engine).

[1325] 6. Providing Feedback:

[1326] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually understandable format and, if necessary, can also be provided in audio format.

[1327] Specific examples

[1328] For example, consider a user giving a business presentation. The user records the presentation using the recording app "Voice Memos" on their smartphone. Once the recording is complete, the audio data is sent from the device to a server. The server converts the audio into text data using the Google Cloud Speech-to-Text API. The server then analyzes the text data using TensorFlow or NLTK to generate feedback, such as the following:

[1329] Suggestions to detect and remove nonsensical expressions such as "um."

[1330] A proposal to change the inappropriate expression "yabai" to "seiji."

[1331] Suggest adding statistics and examples to make it more concrete.

[1332] We check for any immoral expressions, and if there are any abnormalities, we will provide detailed alternatives.

[1333] The generated feedback is sent to the device and displayed visually through a recording application or a dedicated user interface, and can also be played back in audio format.

[1334] Prompt Sentence Examples

[1335] Transcribe the audio recording of a user's business presentation into text and generate feedback based on:

[1336] Detecting and removing nonsense

[1337] Correction of inappropriate expressions

[1338] Additional examples and statistics

[1339] Proposals for improving immoral expressions

[1340] The generated feedback is provided to the user in an easy-to-understand voice and text format.

[1341] This system allows users to quickly receive specific feedback to efficiently improve their own speech and presentations, thereby improving their communication skills.

[1342] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1343] Step 1:

[1344] Users use their smartphones or computers to record their own speech or presentation. They launch a voice recording application installed on their device (e.g., Voice Memos or Google Translate), press the record button, and begin speaking. While recording, the device's microphone picks up the user's voice, which is then saved as digital audio data. When recording is complete, the user presses the stop button to end the recording.

[1345] Input: User's voice

[1346] Output: Digital audio data

[1347] Step 2:

[1348] The device sends the saved audio data to the server over the internet as soon as the user finishes recording. The audio data is sent encrypted using a secure protocol (HTTPS). This process ensures the data is transferred from the device to the server, such as by pressing a "send" button after completing the recording.

[1349] Input: Digital audio data

[1350] Output: Audio data sent to the server

[1351] Step 3:

[1352] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, where the voice waveform is analyzed and mapped to a corresponding string. First, the voice data is input into a speech recognition engine, and then the text information is output from the API.

[1353] Input: Audio data sent to the server

[1354] Output: Text data

[1355] Step 4:

[1356] Once the server receives the converted text data, it then analyzes it using a natural language processing engine (e.g., TensorFlow or NLTK). During this analysis stage, the following processes take place:

[1357] Detecting immoral expressions

[1358] Detecting and removing meaningless expressions (e.g., "um")

[1359] Identifying misleading statements and generating correction suggestions

[1360] Extracting witty expressions

[1361] For example, text data is input into an analysis engine, and the analyzed information is output in JSON format.

[1362] Input: Text data

[1363] Output: Analysis results

[1364] Step 5:

[1365] The server generates specific feedback for the user based on the results of analyzing the text data. The generated feedback includes suggestions for correcting misused expressions and suggestions for improvement. The generative AI model prepares the text and audio data for the feedback. For example, it generates suggestions for correcting inappropriate parts of the text data and adding specific examples and statistics.

[1366] Input: Analysis results

[1367] Output: Feedback data (text and audio)

[1368] Step 6:

[1369] The terminal receives the feedback data sent from the server and presents it to the user via a user interface. The feedback is displayed in a visually verifiable format, and audio feedback is also provided if necessary. For example, the terminal may display a highlight of the feedback on the screen and provide an audio playback button, allowing the user to check the feedback content in detail.

[1370] Input: Feedback data

[1371] Output: Feedback provided to the user

[1372] By following the above specific steps, users can efficiently improve the quality of their own speech and presentations.

[1373] (Application example 1)

[1374] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1375] The goal of this invention is to provide a system that allows users, particularly staff working in the customer service industry, to effectively improve their own speech and conversation. Conventional methods have had the problem of requiring a great deal of time and effort to improve customer service skills, and of making it difficult to obtain specific feedback. Therefore, there is a need for a system that allows users to quickly and effectively improve their skills by recording and analyzing their conversations while serving customers and receiving appropriate feedback.

[1376] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1377] In this invention, the server includes means for recording user speech, means for transmitting the recorded voice data to the server, means for converting the received voice data into text data, means for analyzing the converted text data and detecting immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for generating feedback based on the analysis results, means for providing the generated feedback to the user, and means for analyzing conversations during customer service using a specific natural language processing technology and suggesting appropriate methods of customer service, thereby enabling the user to quickly and effectively improve their customer service skills.

[1378] The "means for recording user utterances" is a device or function that allows a user to record his or her utterances in audio format.

[1379] The "means for transmitting recorded voice data to a server" refers to a device or software for transferring recorded voice data to a server via a network.

[1380] The "means for converting received voice data into text data" refers to a device or software that converts voice data received by the server into character information (text) using voice recognition technology.

[1381] "Means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions" refers to devices or software that evaluate the linguistic elements contained in the text data and identify inappropriate expressions or expressions that need improvement.

[1382] The "means for generating feedback based on the analysis results" refers to a device or software that generates specific improvement proposals or advice for the user based on the analyzed data.

[1383] A "means for providing generated feedback to a user" is a device or software for presenting generated feedback to a user in the form of audio or text.

[1384] "Means for analyzing conversations during customer service using specific natural language processing technology and proposing appropriate methods of customer service" refers to a device or software that uses natural language processing (NLP) technology to analyze conversation data during customer service and present more appropriate and effective methods of customer service to the user.

[1385] The system of the present invention records and analyzes the user's speech to provide specific feedback and improve customer service skills. This system is composed of the following components:

[1386] Recording medium

[1387] Users use a smartphone or smart glasses to record their speech and conversations. The recording is done through a voice input device (microphone) and saved as audio data.

[1388] Audio data transmission means

[1389] The recorded audio data is transmitted over the internet to a server, with an application installed on the smartphone or smart glasses managing the transmission process.

[1390] Audio data conversion means

[1391] The server converts the received voice data into text data using a speech recognition engine such as the Google Cloud Speech-to-Text API, which is then used for the subsequent analysis process.

[1392] Text data analysis methods

[1393] The server analyzes the converted text data using natural language processing techniques (e.g., TensorFlow or SpaCy). The goal of the analysis is to detect the following elements:

[1394] meaningless expressions

[1395] Inappropriate expressions

[1396] Easy-to-understand communication

[1397] Witty expressions

[1398] Feedback Generation Method

[1399] Based on the analysis, the server generates specific feedback for the user, including suggestions for correcting misused expressions and making improvements.

[1400] Feedback methods

[1401] The generated feedback is sent to the user's smartphone or smart glasses and provided to them in voice or text format, allowing them to receive specific advice on how to improve their customer service skills.

[1402] A means of analyzing conversations during customer service and suggesting appropriate ways to serve customers

[1403] The server uses specific natural language processing technology to analyze conversation data during customer service, which allows it to propose appropriate customer service methods. The analysis also uses a list of prohibited words to detect moral violations.

[1404] Specific use cases

[1405] For example, consider a case where a user wants to record a conversation while serving a customer. The user opens a recording application on their smartphone and starts serving the customer. Once the recording is complete, the audio data is automatically sent to the server. The server converts the received audio data into text using a speech recognition engine, and then analyzes it using natural language processing technology. By using prompt sentences like the following, specific feedback can be generated and provided to the user based on the analysis results:

[1406] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[1407] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[1408] In this way, users can receive feedback based on the analysis results and learn specific areas for improvement, thereby effectively improving their customer service skills.

[1409] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1410] Step 1:

[1411] Users use smartphones or smart glasses to record conversations while serving customers. The input is audio data, and the recorded audio is saved as output. Specifically, users launch the application and press the record button to record the conversation.

[1412] Step 2:

[1413] The recorded audio data is sent from the device to the server via the Internet. The input is the audio data generated in step 1, and the server receives the audio data as output. Specifically, the device application automatically uploads the audio data to the server after recording is complete.

[1414] Step 3:

[1415] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. Specifically, the server sends the voice data to the speech recognition engine and saves the returned text data.

[1416] Step 4:

[1417] The server analyzes the converted text data using natural language processing technology (for example, TensorFlow or SpaCy). The input is text data, and the analysis results are obtained as output. Specifically, the server analyzes the text data to detect meaningless expressions, inappropriate expressions, easy-to-understand expressions, and witty expressions.

[1418] Step 5:

[1419] Based on the analysis results, the server generates feedback by inputting a prompt sentence to the generative AI model. The input is the analysis results and the prompt sentence, and the output is feedback. Specifically, the server sends the following prompt sentence to the generative AI model and saves the returned result as feedback:

[1420] Analyze the following customer service conversation, remove meaningless expressions, and revise them to appropriate expressions. Analysis content:

[1421] "Hello, customer. Um, what are you looking for? We have some amazing products. Um, one of our most recommended items is this..."

[1422] Step 6:

[1423] The server sends the generated feedback to the terminal in the form of voice or text. The input is feedback data, and the terminal displays or plays the feedback as output. In concrete terms, the feedback data is sent to the terminal, and the application on the terminal displays or plays the feedback to the user in voice.

[1424] Step 7:

[1425] The user checks the feedback displayed on the device and uses it to improve their customer service skills. The input is the feedback information, and the output is the user's understanding of the areas for improvement. Specifically, the user reads the feedback displayed on the screen and reflects it in their next customer service encounter.

[1426] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1427] This system allows users to record their own speech and presentations, analyze their emotions, and provide more specific feedback to improve their communication skills.By combining it with an emotion engine, the system analyzes not only the content of speech but also the emotional expression, and reflects this in the feedback.

[1428] System configuration

[1429] 1. Recording Method:

[1430] Users use devices such as smartphones or computers to open a dedicated audio recording application and record speeches or presentations.

[1431] 2. Audio data transmission means:

[1432] The device sends the recorded audio data to a server via an internet connection.

[1433] 3. Audio data conversion means:

[1434] The server uses a speech recognition engine to convert the received voice data into text data, which is then used for analysis.

[1435] 4. Text data analysis methods:

[1436] The server parses the converted text data, which includes the following elements:

[1437] Detecting moral violations (matching against a specific list of banned words).

[1438] Detect misleading statements and suggest improvements.

[1439] Proposal for an easy-to-understand explanation.

[1440] A suggestion for witty expressions.

[1441] 5. Emotion Engine:

[1442] The server uses an emotion engine to identify the user's emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and reflects the results in the analysis.

[1443] 6. Feedback Generation Methods:

[1444] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech, as well as suggestions for improvement regarding emotional expression.

[1445] 7. How to provide feedback:

[1446] The terminal receives the feedback sent from the server and provides it to the user in the form of voice or text, making it easy for the user to understand.

[1447] Specific examples

[1448] For example, suppose a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to the server. The server then uses a speech recognition engine to convert the audio data into text.

[1449] The server then parses the text data and checks the following:

[1450] Eliminating meaningless expressions such as "um."

[1451] A proposal to change the inappropriate expression "yabai" to "seiji."

[1452] Suggest adding examples and statistics to increase the specificity of the data.

[1453] We check for any immoral expressions and suggest alternatives.

[1454] At the same time, the emotion engine analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension."

[1455] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[1456] In this way, the present invention provides users with concrete improvements in terms of both content and emotion, and supports the improvement of communication skills.

[1457] The processing flow will be explained below.

[1458] Step 1:

[1459] The user opens a voice recording application. The device displays a "Start Recording" button.

[1460] Step 2:

[1461] The user presses the "Start Recording" button. The device activates the microphone and begins recording what the user says.

[1462] Step 3:

[1463] Users can record presentations or anything they want to say, and the audio being recorded is saved as digital data in real time.

[1464] Step 4:

[1465] The user presses the "Stop Recording" button. The device stops recording and generates an audio data file.

[1466] Step 5:

[1467] The device then sends the generated audio data file to the server via the Internet.

[1468] Step 6:

[1469] The server receives the voice data and converts it into text data using a voice recognition engine, which is then stored in a database.

[1470] Step 7:

[1471] The server parses the text data, which includes the following elements:

[1472] Detecting moral violations (matching against a specific list of banned words).

[1473] Detect misleading statements and suggest improvements.

[1474] Proposal for an easy-to-understand explanation.

[1475] A suggestion for witty expressions.

[1476] Step 8:

[1477] The server uses an emotion engine to identify emotions from the voice data. The emotion engine identifies basic emotions such as joy, sadness, anger, surprise, and fear, and incorporates the results into the analysis.

[1478] Step 9:

[1479] The server generates feedback based on the analysis and sentiment analysis results, including advice on the content of the speech and suggestions for improvement regarding emotional expression.

[1480] Step 10:

[1481] The server then sends the generated feedback to the device over the Internet.

[1482] Step 11:

[1483] The device receives the feedback and displays it through a user interface, allowing the user to check the analysis results and the feedback content.

[1484] Step 12:

[1485] The device also provides feedback in audio format, and the user can receive feedback in text or audio format.

[1486] Step 13:

[1487] Based on the feedback provided, users can identify areas for improvement in their own comments and presentations and use this information to improve their comments in future sessions.

[1488] This specific processing flow allows users to receive comprehensive feedback not only on the content of their own statements but also on their emotional expressions, thereby improving the quality of their communication.

[1489] Example 2

[1490] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1491] In today's business and educational settings, improving the quality of speech and presentations is extremely important. However, conventional feedback systems focus on analyzing speech content, failing to consider the speaker's emotions and tone when providing feedback. Furthermore, they focus too much on eliminating expressions that violate morals or are misleading, failing to provide more specific improvement measures. To address these issues, it is necessary to analyze users' emotions and incorporate them into feedback to improve communication skills more effectively.

[1492] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1493] In this invention, the server includes means for converting received voice data into text data, means for analyzing the converted text data to detect immoral behavior, misleading expressions, easy-to-understand communication, and witty expressions, means for analyzing the user's emotions from the voice data, and means for generating feedback based on the analysis results and emotion analysis results, thereby enabling more specific and multifaceted feedback that includes not only the content of the speech but also emotional expressions.

[1494] "User" refers to any individual or organization that uses the system to record speeches or presentations and receive feedback.

[1495] "Means for recording speech" refers to a device or application for recording a user's speech in digital form as audio data.

[1496] "Means for transmitting audio data to a server" refers to a device or software that includes network functionality for transmitting recorded audio data to a remote server via the Internet.

[1497] "Means for converting voice data into text data" refers to software or hardware for converting voice data into text using voice recognition technology.

[1498] "Means for analyzing text data" refers to software or hardware that includes natural language processing technology for detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions based on the converted text data.

[1499] "Means for analyzing user emotions from voice data" refers to algorithms that analyze the tone and intonation of voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[1500] The "means for generating feedback" refers to software or hardware for automatically constructing advice or improvement suggestions to be provided to the user based on the results of text data analysis and sentiment analysis.

[1501] "Means for providing feedback to the user" refers to a display device or application for presenting the generated feedback to the user in audio or text form.

[1502] This invention is a system that allows users to record their own speech or presentation and analyze their emotions to provide more specific feedback and improve their communication skills. This system is configured using the following hardware and software.

[1503] 1. Recording Method:

[1504] A user uses a device such as a smartphone or computer to open a dedicated voice recording application (e.g., a general audio recording application) to record speech or a presentation. This application records the user's voice as digital data.

[1505] 2. Means of data transmission:

[1506] The device sends the recorded audio data to a server via an internet connection, typically Wi-Fi or a mobile data network.

[1507] 3. Audio data conversion means:

[1508] The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text, and the voice data is analyzed in the cloud by calling an API, and the returned text data is obtained.

[1509] 4. Text data analysis methods:

[1510] The server analyzes the text data using Python and NLP libraries (e.g., spaCy). The analysis includes detecting moral violations, misleading expressions, easy-to-understand communication, and witty expressions. Furthermore, it detects moral violations using a specific list of prohibited words.

[1511] 5. Emotion Engine:

[1512] The server uses an emotion engine, such as IBM Watson Tone Analyzer, to analyze the user's emotions from the voice data. The emotion engine analyzes the tone and intonation of the voice to identify emotions such as joy, sadness, anger, surprise, and fear.

[1513] 6. Feedback Generation Methods:

[1514] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[1515] 7. How to provide feedback:

[1516] The terminal receives the feedback sent from the server and provides it to the user through a user interface, which may be displayed in audio or text format and configured to be easily understood by the user.

[1517] Specific examples

[1518] For example, say a user has an important business presentation. The user opens a recording application and starts the presentation. When the recording is finished, the audio data is sent from the device to a server. The server then converts the audio data into text using Google Cloud Speech-to-Text.

[1519] The server then parses the text data and checks the following:

[1520] Eliminating meaningless expressions such as "um."

[1521] A proposal to change the inappropriate expression "yabai" to "seiji."

[1522] Suggest adding examples and statistics to increase the specificity of the data.

[1523] We check for any immoral expressions and suggest alternatives.

[1524] At the same time, the emotion engine (IBM Watson Tone Analyzer) analyzes the user's emotions. For example, if the user is nervous, it detects whether that nervousness is reflected in the voice and provides advice such as "breathing techniques to relieve tension" based on the results.

[1525] Finally, the server generates specific feedback based on the analysis results and sends it to the device in voice or text format. The device then displays the received feedback through a user interface, allowing the user to view the screen and identify areas for improvement.

[1526] Prompt Sentence Examples

[1527] markdown

[1528] A user has recorded an important business presentation. Analyze the audio data below and generate feedback based on the text data and sentiment analysis results.

[1529] Audio data: [Audio data link or file]

[1530] Information to include in your feedback:

[1531] 1. Eliminating meaningless expressions

[1532] 2. Proposing appropriate alternatives to inappropriate expressions

[1533] 3. Suggestions for improvement (adding specific data or examples, etc.)

[1534] 4. Advice based on sentiment analysis results

[1535] This system allows users to obtain specific improvement measures in terms of both content and emotion, enabling them to improve their communication skills more effectively.

[1536] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1537] Step 1:

[1538] Users install a dedicated voice recording application on their smartphone or computer. They launch the application and press the "Start Recording" button to record speech or presentations. The input is the user's voice, and the output is audio data. This audio data is recorded in digital format.

[1539] Specific behavior:

[1540] The user opens the application.

[1541] When a user speaks into the microphone, the application records the voice as digital data.

[1542] Step 2:

[1543] After the device finishes recording, it sends the recorded audio data to the server via an Internet connection. The input is the recorded audio data, and the output is the audio data sent to the server. This transmission is done automatically using the API within the application.

[1544] Specific behavior:

[1545] The device has an internet connection.

[1546] The terminal divides the voice data into packets and sends them to the server over the network.

[1547] Step 3:

[1548] The server analyzes the received voice data using a speech recognition engine such as Google Cloud Speech-to-Text and converts it into text data. The input is voice data and the output is text data. Analysis is performed on the cloud by calling an API.

[1549] Specific behavior:

[1550] The server calls the Google Cloud Speech-to-Text API to upload the audio data.

[1551] The server retrieves the returned text data.

[1552] Step 4:

[1553] The server uses Python and NLP libraries (e.g., spaCy) to perform detailed analysis of the text data. The input for the analysis is the text data, and the output is the analysis results. Specifically, it checks for the following elements: moral violations, misleading expressions, easy-to-understand communication, and witty expressions.

[1554] Specific behavior:

[1555] After the server receives the text data, it executes the Python script.

[1556] The server uses an NLP library to generate the analysis results.

[1557] Step 5:

[1558] The server uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotion from the voice data. The input is the voice data, and the output is the emotion analysis result. The emotion engine analyzes the tone and intonation of the voice to identify the emotion.

[1559] Specific behavior:

[1560] The server calls the IBM Watson Tone Analyzer API to send the audio data.

[1561] The server receives the returned emotion analysis results and reflects them in the analysis.

[1562] Step 6:

[1563] The server generates feedback based on the results of text analysis and sentiment analysis. The input is the analysis results and sentiment analysis results, and the output is feedback data. The feedback includes advice on the content of the speech and suggestions for improving emotional expression. Appropriate feedback is generated using the Google Cloud Natural Language API.

[1564] Specific behavior:

[1565] The server generates feedback data based on the analysis results.

[1566] The server converts the feedback data into a format that is easy for the user to understand.

[1567] Step 7:

[1568] The terminal receives the feedback sent from the server and provides it to the user through a user interface. The input is feedback data, and the output is feedback in voice or text format. The feedback is displayed in voice or text format.

[1569] Specific behavior:

[1570] The terminal receives the feedback data via the Internet.

[1571] The device displays the feedback data in its user interface (e.g., smartphone notifications, messages within a specific application).

[1572] Through the above processing steps, the system is able to analyze the content of the user's remarks and presentations and provide effective feedback.

[1573] (Application example 2)

[1574] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1575] Conventional speech analysis systems focus on analyzing the content of a user's speech, but do not analyze emotions, making it difficult to comprehensively improve a user's communication skills. Furthermore, because specific feedback on emotional expressions is not provided, users are unable to acquire specific measures to improve their own emotional expressions. The present invention aims to solve these problems by providing specific feedback on both the content of a user's speech and their emotional expressions, thereby comprehensively improving communication skills.

[1576] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1577] In this invention, the server includes a device for recording user utterances, a device for transmitting the recorded voice data to a remote device, a device for converting the received voice data into text data, a device for analyzing the converted text data and detecting immoral expressions, misleading expressions, easy-to-understand communication methods, and witty expressions, a device for generating feedback based on the analysis results, a device for providing the generated feedback to the user, a device for identifying the user's emotions from the analyzed voice data, and a device for providing feedback that includes suggestions for improving emotional expressions based on the identified emotion analysis results. This allows the user to receive specific feedback not only on the content of their own speech but also on their emotional expressions, thereby enabling them to improve their overall communication skills.

[1578] "User" means an individual or corporation that uses the system.

[1579] "Utterance" refers to content that is verbally expressed by a user.

[1580] A "recording device" is a device for saving a user's speech as voice data.

[1581] "Voice data" refers to data in which a user's speech is saved in digital form.

[1582] "Remote device" refers to a server or cloud that exists away from the user's local device to process data.

[1583] "Text data" is data obtained by converting voice data into text format.

[1584] "Analyzing" is the process of examining the content of data and identifying specific patterns or information.

[1585] "Moral violation" refers to the inclusion of content or expressions that are socially or ethically inappropriate.

[1586] "Misleading expressions" refer to expressions that may be interpreted differently from the meaning intended by the user.

[1587] "Easy-to-understand communication" refers to a way of expressing information so that it can be easily understood by the recipient.

[1588] "Witty expression" refers to an expression that is humorous or intellectually clever.

[1589] "Feedback" refers to evaluations and advice provided based on the user's speech and analysis results.

[1590] "Emotion" refers to a psychological state such as joy, sadness, anger, surprise, or fear that a user expresses while speaking.

[1591] An "identifying apparatus" is a device for detecting and identifying a user's emotion from voice data.

[1592] "Suggestion" is an act of explicitly notifying a specific problem or point for improvement contained in a user's utterance.

[1593] "Improvement suggestions" are specific suggestions for users to improve their speech and emotional expression based on the feedback.

[1594] The present invention is a system that analyzes both the content of user speech and emotional expressions and provides specific feedback. This system includes a series of processes: recording, transmitting the voice data, converting it to text data, analyzing the text data, analyzing emotions, and generating and providing feedback. Each step is described in detail below.

[1595] First, a user opens a dedicated recording application to record their speech. This application runs on a device such as a smartphone, tablet, PC, or robot. The user begins speaking and records it. The recorded audio data is then sent to a server via the Internet.

[1596] The server receives the voice data and then converts it into text data using a speech recognition engine (such as Google's Speech-to-Text API). This converted text data is further analyzed by an analysis engine, which performs functions such as detecting moral violations, detecting misleading expressions, analyzing easy-to-understand communication, and detecting witty expressions.

[1597] The server then uses a sentiment analysis engine to identify the user's emotions from the voice data. The sentiment analysis engine uses a generative AI model, such as IBM Watson's Tone Analyzer, to identify the user's emotions, such as joy, sadness, anger, surprise, and fear. The results of this sentiment analysis, along with text analysis, are reflected in the feedback.

[1598] The feedback generation engine generates specific feedback based on the results of text analysis and sentiment analysis. This feedback includes suggestions for improving not only the content of speech but also the expression of emotions. For example, specific advice such as "You need to be careful because you use 'um' a lot in your speech" or "We recommend that you incorporate breathing techniques to relieve tension during presentations" is provided.

[1599] Finally, the generated feedback is sent to the user's device and provided to them in voice or text format, which the user can refer to and use to improve their own speech and emotional expression.

[1600] As a concrete example, consider a user giving a business presentation. The user opens a recording application for the presentation and records the content of the presentation. Once the recording is complete, the audio data is sent to a server and converted into text data. The server analyzes the converted text and performs sentiment analysis. Based on the analysis results, specific feedback on the user's presentation content and emotional expressions is generated and provided to the user.

[1601] An example prompt is:

[1602] "I'm going to begin my presentation. I'll record everything I say for the next five minutes and provide sentiment analysis and feedback afterward."

[1603] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1604] Step 1:

[1605] The user launches a dedicated recording application. The user presses the record button in the application to start speaking, and when they finish speaking, they press the record button again to stop recording. The input is the user's speech, and the output is the recorded voice data.

[1606] Step 2:

[1607] The device sends the recorded audio data to the server using an internet connection. The input is the recorded audio data, and the output is the audio data sent to the server.

[1608] Step 3:

[1609] The server converts the received voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The input is voice data, and the output is the converted text data.

[1610] Step 4:

[1611] The server passes the converted text data to an analysis engine to detect immoral behavior, misleading expressions, easy-to-understand communication methods, and witty expressions. The analysis engine performs the analysis using a specific list of prohibited words and other analysis rules. The input is the text data, and the output is the analysis results.

[1612] Step 5:

[1613] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotion from the voice data. Analyzed emotions include joy, sadness, anger, surprise, and fear. The input is the voice data, and the output is the emotion analysis result.

[1614] Step 6:

[1615] The server generates feedback based on the results of text analysis and sentiment analysis. The feedback includes suggestions for improving content and emotional expression. For example, it includes specific examples such as "You use 'um' a lot in your comments, so be careful" or "You seemed nervous during the presentation, so I recommend trying some breathing techniques to relax." The input is the results of text analysis and sentiment analysis, and the output is feedback.

[1616] Step 7:

[1617] The server sends the generated feedback to the terminal, and the terminal presents the received feedback to the user in voice or text format. The input is the feedback, and the output is the feedback displayed on the user's terminal.

[1618] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1619] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1620] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1621] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1622] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1623] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1624] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1625] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1626] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1627] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1628] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1629] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1630] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1631] 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.

[1632] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1633] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1634] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1635] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1636] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1637] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1638] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1639] The following is further disclosed regarding the above embodiment.

[1640] (Claim 1)

[1641] means for recording user utterances;

[1642] means for transmitting the recorded voice data to a server;

[1643] means for converting received voice data into text data;

[1644] A means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions;

[1645] means for generating feedback based on the analysis results;

[1646] means for providing the generated feedback to a user;

[1647] A system including:

[1648] (Claim 2)

[1649] 10. The system of claim 1, wherein the feedback is provided in audio or text format.

[1650] (Claim 3)

[1651] 2. The system according to claim 1, wherein the analysis means detects moral violations using a specific list of prohibited words.

[1652] "Example 1"

[1653] (Claim 1)

[1654] means for recording user utterances;

[1655] means for transmitting the recorded voice data to a computer;

[1656] means for converting received voice data into text information;

[1657] A means for analyzing the converted character information and detecting moral violations, misleading expressions, easy-to-understand ways of communicating, and witty expressions;

[1658] means for generating feedback based on the analysis results;

[1659] means for providing the generated feedback to a user;

[1660] A system including:

[1661] (Claim 2)

[1662] 10. The system of claim 1, wherein the feedback is provided in the form of audio or text information.

[1663] (Claim 3)

[1664] 2. The system according to claim 1, wherein the analysis means detects moral violations using a specific list of prohibited words.

[1665] "Application Example 1"

[1666] (Claim 1)

[1667] means for recording user utterances;

[1668] means for transmitting the recorded voice data to a server;

[1669] means for converting received voice data into text data;

[1670] A means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions;

[1671] means for generating feedback based on the analysis results;

[1672] means for providing the generated feedback to a user;

[1673] A means for analyzing conversations during customer service using specific natural language processing technology and suggesting appropriate ways to serve customers;

[1674] A system including:

[1675] (Claim 2)

[1676] 10. The system of claim 1, wherein the feedback is provided in audio or text format.

[1677] (Claim 3)

[1678] 2. The system according to claim 1, wherein the analysis means detects moral violations using a specific list of prohibited words.

[1679] "Example 2: Combining Emotion Engines"

[1680] (Claim 1)

[1681] means for recording user utterances;

[1682] means for transmitting the recorded voice data to a server;

[1683] means for converting received voice data into text data;

[1684] A means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions;

[1685] A means for analyzing user emotions from voice data;

[1686] means for generating feedback based on the analysis results and the sentiment analysis results;

[1687] means for providing the generated feedback to a user;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, wherein the feedback is provided in audio or text format.

[1691] (Claim 3)

[1692] 2. The system according to claim 1, wherein the analysis means detects moral violations using a specific list of prohibited words.

[1693] "Application example 2 when combining emotion engines"

[1694] (Claim 1)

[1695] a device for recording a user's speech;

[1696] a device for transmitting the recorded audio data to a remote device;

[1697] a device for converting received voice data into text data;

[1698] a device for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions;

[1699] a device for generating feedback based on the analysis results;

[1700] a device for providing the generated feedback to a user;

[1701] a device for identifying a user's emotion from the analyzed voice data;

[1702] a device that provides feedback including suggestions and improvements for emotional expressions based on the identified emotion analysis results;

[1703] A system including:

[1704] (Claim 2)

[1705] 10. The system of claim 1, wherein the feedback is provided in audio or text format.

[1706] (Claim 3)

[1707] 2. The system of claim 1, wherein the analysis device detects moral violations using a specific list of prohibited words. [Explanation of symbols]

[1708] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for recording user utterances; means for transmitting the recorded voice data to a server; means for converting received voice data into text data; A means for analyzing the converted text data and detecting moral violations, misleading expressions, easy-to-understand communication methods, and witty expressions; means for generating feedback based on the analysis results; means for providing the generated feedback to a user; A system including:

2. The system of claim 1 , wherein the feedback is provided in audio or text format.

3. 2. The system according to claim 1, wherein the analysis means detects moral violations using a specific list of prohibited words.

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