System

Abstract

A system is provided.SOLUTION: The system includes a terminal means for recording voice, a terminal means for uploading the recorded voice data to a server, a server means for converting the uploaded voice data into text data, a server means for analyzing the converted text data by a natural language processing engine, a server means for generating feedback based on an analysis result, a server means for transmitting the generated feedback to a terminal, and a terminal means for displaying the feedback transmitted to the terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In sales and customer service, tone of voice, phrasing, and timing of responses are important points for increasing the success rate, but objectively evaluating and improving one's own performance is a difficult problem. Conventional methods rely heavily on intuition and do not provide efficient feedback, making improvement difficult. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by a system including a terminal means for recording voice, a terminal means for uploading the recorded voice data to a server, a server means for converting the uploaded voice data into text data, a server means for analyzing the converted text data with a natural language processing engine, a server means for generating feedback based on the analysis results, a server means for transmitting the generated feedback to the terminal, and a terminal means for displaying the feedback transmitted to the terminal. By including a means for a user to input the result of a deal (whether or not a deal is concluded) to the server and a means for analyzing the tone and intonation of the voice data, it is possible to obtain more objective and specific feedback than with conventional methods, thereby improving the closing rate.

[0006] The "terminal means" is a device that has the function of recording voice, uploading the recorded voice data to a server, and receiving and displaying the generated feedback.

[0007] The "server means" is a system that has the function of processing voice data uploaded from the terminal means, performing voice recognition and natural language processing, generating feedback, and transmitting it to the terminal means.

[0008] A "speech recognition engine" is a software or hardware component that analyzes voice data and converts it into text data.

[0009] A "natural language processing engine" is a group of algorithms and libraries that analyze text data and understand sentence structure, emotions, intent, etc.

[0010] "Feedback" is information generated based on the analysis results, and includes specific advice and improvements to help users improve their conversion rates.

[0011] "Results of successful or unsuccessful contract" is information indicating whether or not the user has concluded a sale of a product or service as a result of business negotiations or customer service.

[0012] "Tone and intonation" refers to characteristics related to changes in the speaker's voice, such as stress, pitch, and tempo, and is an important element in conveying emotion and intention. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service or business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. Specific embodiments for carrying out the present invention will be described below.

[0035] System configuration

[0036] This system is mainly composed of "terminal means" and "server means."

[0037] Terminal means:

[0038] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[0039] Server means:

[0040] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[0041] Program processing

[0042] Recording and uploading audio

[0043] 1. The user uses a terminal means to record the audio of business negotiations or customer service.

[0044] 2. When the recording is completed, the audio data is temporarily saved and then uploaded to the server means.

[0045] Speech recognition and text conversion

[0046] 3. The server runs the received voice data through a voice recognition engine and converts it into text data.

[0047] 4. The converted text data is stored in a database.

[0048] Text analysis and feedback generation

[0049] 5. The server then analyzes the converted text data using a natural language processing engine. Analysis items include points of change, positive and negative phrases, and tone of voice.

[0050] 6. The user enters the result of the transaction (whether successful or unsuccessful) into the server.

[0051] 7. The server generates feedback based on the analysis results of the entered successful and unsuccessful sales. For example, the feedback may include suggestions for improving wording, effective parts, and corrections to tone and intonation.

[0052] Providing feedback

[0053] 8. The server sends the generated feedback to the terminal means, and the terminal provides the feedback to the user. The user can use this feedback to improve their next business negotiation or customer service.

[0054] Specific examples

[0055] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[0056] The server uses a speech recognition engine to convert the voice data into text data. The converted text is, "Dear customer, this product is very convenient and a good value." The natural language processing engine analyzes this text and analyzes how tone and phrasing have affected it.

[0057] If the user then inputs into the server that the deal was not concluded, the server will generate feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected" or "You should have emphasized the specific benefits."

[0058] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[0059] The above is a specific embodiment for carrying out the present invention. This system allows users to objectively evaluate and improve their own presentation skills.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[0063] Step 2:

[0064] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[0065] Step 3:

[0066] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[0067] Step 4:

[0068] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[0069] Step 5:

[0070] The server stores the converted text data in a database, where it is used for subsequent analysis.

[0071] Step 6:

[0072] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[0073] Step 7:

[0074] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[0075] Step 8:

[0076] The server generates feedback based on data on successful and unsuccessful deals and text analysis results, including specific advice on areas for improvement and what was effective.

[0077] Step 9:

[0078] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[0079] Step 10:

[0080] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[0081] In this way, the entire system works in concert to help users improve their performance.

[0082] Example 1

[0083] 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."

[0084] Conventional voice recording systems for business meetings and customer service simply store the recorded audio, but lack the functionality to analyze the content in detail. This means that users cannot receive specific feedback on the success or failure of the business meeting, making it difficult for them to identify specific areas for improvement in their customer service skills or presentations. It is also difficult to analyze finer details such as tone and intonation in the audio data and provide appropriate feedback.

[0085] 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.

[0086] In this invention, the server includes a means for converting voice data into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback using a generative AI model based on the analysis results and the outcome of the deal. This allows users to record voice recordings of business negotiations or customer service, analyze the content in detail, and receive specific and appropriate feedback. Furthermore, because elements of the voice data, such as tone and intonation, are also analyzed, users can accurately identify areas for improvement in their speaking style and presentation, allowing them to effectively prepare for their next business negotiation or customer service.

[0087] The "terminal means for recording voice" is a device that a user uses to record voice during business negotiations or customer service.

[0088] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server.

[0089] The "server means for converting uploaded voice data into text data" is a device having a function for converting voice data received by the server into text information.

[0090] The "server means for analyzing the converted text data using a natural language processing engine" is a server that analyzes the data converted into character information using a specific language analysis technique.

[0091] "Server means for generating feedback using a generative AI model based on the analysis results and the results of successful or unsuccessful transactions" refers to a server that generates appropriate feedback using an artificial intelligence model based on the analyzed data and the results of negotiations entered by the user.

[0092] The "server means for transmitting generated feedback to the terminal" is a server for distributing generated feedback information to the user's device.

[0093] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server so that it can be seen by the user.

[0094] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system is mainly composed of terminal means and server means.

[0095] System configuration

[0096] Terminal means

[0097] A device used by a user. It uses hardware such as a smartphone, tablet, or PC to record audio. It also has the function of uploading the recorded data to a server. It also displays feedback received from the server.

[0098] Server Means

[0099] This is a central system that processes and analyzes voice data. It uses the Google (registered trademark) Cloud Speech-to-Text API to convert voice into text data. It also analyzes the text data using a natural language processing engine (such as NLTK or Spacy), and generates feedback using a generative AI model (such as GPT-3 (registered trademark)) based on the analysis results and the results of successful / non-successful transactions. The generated feedback is sent to the terminal means.

[0100] Program processing

[0101] Recording and uploading audio

[0102] 1. The user uses the terminal means to record the voice of the business negotiation or customer service. Press the recording start button to start recording.

[0103] 2. After recording is complete, the audio data is temporarily saved and then uploaded to the server by the device.

[0104] Speech recognition and text conversion

[0105] 3. The server stores the uploaded audio file in Google Cloud Storage.

[0106] 4. The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data, which is then stored in a database.

[0107] Text analysis and feedback generation

[0108] 5. The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy). The analysis includes points of change, positive and negative phrases, and tone of voice.

[0109] 6. Enter the results in a form where the user enters the outcome of the deal (won or not).

[0110] 7. The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analysis data. The generated feedback is sent to the terminal means.

[0111] Providing feedback

[0112] 8. The device displays feedback to the user, who can use it to improve their next sales meeting or customer service.

[0113] Specific examples

[0114] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal (smartphone). After the transaction, the user presses the recording stop button, and the voice data is saved on the terminal and uploaded to a server.

[0115] The server stores the voice data in Google Cloud Storage and converts it to text using the Google Cloud Speech-to-Text API. The converted text is "Dear customer, this product is very useful and a good value." A natural language processing engine (NLTK or Spacy) analyzes this text and analyzes how tone and phrasing influence it.

[0116] After that, when the user inputs into the server that the deal was not concluded, the server uses the analysis results and a generative AI model (GPT-3) to generate feedback such as, "Your tone was monotonous and should have been a bit more inflected. You need to emphasize the specific benefits."

[0117] The generated feedback is sent to the terminal means and confirmed by the user, and based on this feedback, improvements are made at the next business meeting.

[0118] Prompt Sentence Examples

[0119] Here is an example of a prompt for a generative AI model:

[0120] Negotiation audio:

[0121] "Dear customer, this product is very convenient and a great value."

[0122] Contract status: Unconcluded

[0123] Generate feedback.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1:

[0126] The user starts the voice recording application using the terminal means, and presses the recording start button to start recording the voice of the business negotiation or customer service.

[0127] (Specific actions): Launch the recording app on your smartphone or tablet and tap the "Start Recording" button.

[0128] Input: Tap action of the Start Recording button.

[0129] Output: Start recording audio data.

[0130] Step 2:

[0131] After the user finishes recording, the device temporarily saves the audio file.

[0132] (Specific operation): After the negotiation is over, tap the "Stop Recording" button and the audio file will be saved to the device's local storage.

[0133] Input: Tap action of the stop recording button.

[0134] Output: Audio file saved on local storage.

[0135] Step 3:

[0136] The device will automatically compress the audio file and upload it to the server. Once the upload is complete, the user will be notified.

[0137] (Specific operation): After recording stops, the device will compress the audio file in the background and upload it to the server when connected to Wi-Fi. When the upload is complete, a "Upload complete" notification will be displayed.

[0138] Input: Audio file on local storage.

[0139] Output: The audio file uploaded to the server and an "upload complete" notification.

[0140] Step 4:

[0141] The server stores the uploaded audio files in Google Cloud Storage.

[0142] (Specific operation): The server analyzes the metadata of the audio file and stores it in the appropriate folder in Google Cloud Storage.

[0143] Input: Audio files uploaded to the server.

[0144] Output: Audio files stored in Google Cloud Storage.

[0145] Step 5:

[0146] The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data.

[0147] (Specific operation): The server passes the audio file to the API and receives text data as a response. Example: "Dear customer, this product is very convenient and a good value."

[0148] Input: Audio files stored in Google Cloud Storage.

[0149] Output: Text data.

[0150] Step 6:

[0151] The server stores the converted text data in a database.

[0152] (Specific operation): The server inserts the text data into a database such as MySQL (registered trademark) and stores it along with the metadata of the corresponding audio file.

[0153] Input: Text data.

[0154] Output: Text data stored in a database.

[0155] Step 7:

[0156] The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy), which includes points of change, positive and negative phrases, and tone of voice.

[0157] (Specific operation): The text data is passed to an analysis script, and scores are calculated for each evaluation item. For example, "This product is very useful and a good value" is marked as a positive phrase.

[0158] Input: Text data stored in a database.

[0159] Output: Analysis results.

[0160] Step 8:

[0161] The user enters the results into the system's successful / unsuccessful result input form.

[0162] (Specific action): Select and enter the outcome of the deal from the web form or app interface. Example: Select "Not Sold."

[0163] Input: Closed or open result.

[0164] Output: The sales result data sent to the server.

[0165] Step 9:

[0166] The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analytical data.

[0167] (Specific behavior): The server passes the analysis data to GPT-3 as a prompt sentence and generates feedback. Example: "Your tone was monotonous, and you should have used more inflection."

[0168] Input: Analysis data and negotiation results.

[0169] Output: The generated feedback statement.

[0170] Step 10:

[0171] The server transmits the generated feedback to the terminal means.

[0172] (Specific operation): The server sends feedback data to the device via push notification.

[0173] Input: The generated feedback sentence.

[0174] Output: Feedback sent to the device.

[0175] Step 11:

[0176] The device displays the feedback to the user.

[0177] (Specific behavior): The app on your device displays feedback as a notification. For example, "Your tone was monotonous and should have been more inflected. You need to emphasize the specific benefits."

[0178] Input: The feedback statement sent to the terminal.

[0179] Output: The feedback displayed to the user.

[0180] (Application example 1)

[0181] 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."

[0182] In the past, it was difficult to understand what phrasing and tone contributed to closing deals during customer service and sales negotiations. Employees lacked a concrete way to objectively evaluate and improve their customer service skills, making it difficult to improve closing rates.

[0183] 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.

[0184] In this invention, the server includes a means for converting speech into text data, a means for analyzing the text data using a natural language processing engine, and a means for generating feedback based on the analysis results. This makes it easy to analyze speech data during customer service or business negotiations, and by providing specific feedback, it becomes possible to objectively and effectively improve the customer service skills of employees.

[0185] "Audio recording terminal means" refers to a device such as a smartphone or tablet that a user uses to record conversations such as business negotiations and customer service.

[0186] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server via the Internet.

[0187] The "server means for converting uploaded voice data into text data" is a server that has the function of automatically converting voice data into text data.

[0188] The "server means for analyzing the converted text data using a natural language processing engine" refers to a server equipped with a natural language processing engine for analyzing the text data and evaluating points of change, phrasing, intonation of tone, etc.

[0189] The "server means for generating feedback based on the analysis results" is a server having a function for generating specific feedback using the results of analysis by the natural language processing engine.

[0190] The "server means for transmitting the generated feedback to the terminal" is a server having a function for transmitting the generated feedback to the terminal used by the user.

[0191] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server on the screen.

[0192] The "terminal means for automatically recording audio during customer service" is a device that has the function of automatically recording conversations during customer service without user operation.

[0193] The "terminal means for providing feedback in real time" is a device that has the function of immediately providing the user with feedback generated by the server.

[0194] The "means for the user to input the results of a deal or non-deal to the server" is a function that includes an interface for the user to input the results of business negotiations or customer service to the server.

[0195] The "means for analyzing the tone and intonation of the audio data" is a natural language processing engine that has the function of analyzing and evaluating the tone and intonation of the recorded audio data.

[0196] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system consists of two main components: a terminal means and a server means.

[0197] System configuration

[0198] Terminal means

[0199] The terminal means is a device used by a user, and may be a smartphone, tablet, or PC. The terminal means has the following functions:

[0200] 1. Audio recording function: Record audio during business negotiations and customer service.

[0201] 2. Audio data upload function: Upload recorded audio data to the server.

[0202] 3. Feedback display function: Displays the feedback information received from the server.

[0203] 4. Automatic recording function: Automatically records audio while serving customers.

[0204] 5. Real-time feedback function: Provide feedback in real time.

[0205] Server Means

[0206] The Server Means is a central system for processing and analyzing voice data and has the following functions:

[0207] 1. Speech recognition: Converts received voice data into text data. Google Cloud Speech-to-Text and IBM Watson (registered trademark) Speech to Text are used as speech recognition engines.

[0208] 2. Natural language processing: The text data is analyzed using a natural language processing engine. Analysis items include points of change, phrasing, intonation, and speaker habits. The natural language processing engine uses the GPT model from OpenAI (registered trademark).

[0209] 3. Feedback generation: Based on the analysis results, feedback is generated on points that need improvement and effective parts.

[0210] 4. Result input reception: Provides a means for users to input the results of a transaction, whether successful or unsuccessful, to the server.

[0211] These components provide specific feedback to users, helping them improve their customer service skills.

[0212] Program processing

[0213] The program of this system is composed of the terminal means and server means described above. The processing of the program will be explained below.

[0214] Audio recording and uploading

[0215] Users use their smartphones or tablets to record conversations during business meetings or customer service. Once recording is complete, the audio data is automatically uploaded to a server via the Internet. The device used is a voice recording library such as Expo Audio.

[0216] Speech recognition and text conversion

[0217] The server receives the uploaded voice data and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text or IBM Watson Speech to Text). This converted text data is then stored in a database such as MySQL or PostgreSQL.

[0218] Natural Language Processing and Analysis

[0219] The server analyzes the stored text data using a natural language processing engine (OpenAI's GPT model), which analyzes points of change, positive and negative phrases, tone of voice, and speaker habits.

[0220] Feedback generation and provision

[0221] The server generates feedback based on the analysis results that will be useful for the next customer service or business negotiation. This feedback is sent to the terminal means and displayed to the user.

[0222] As a concrete example, consider a scenario where a user is negotiating a product sales deal in a brick-and-mortar store. The audio of the negotiation is recorded on a terminal, and once the conversation is over, the recording is automatically uploaded to a server. After a few seconds, the server completes its analysis and generates feedback, which is displayed on the terminal, such as "Your tone was monotonous; you should have used more inflection" or "You should have emphasized the specific benefits."

[0223] Example prompt sentence:

[0224] Convert the audio data recorded by the user into text data and analyze the following items.

[0225] 1. Successful turning points and their phrases

[0226] 2. What phrases should users improve and why?

[0227] 3. Tone inflection and its effects

[0228] 4. Speaker habits and how to correct them

[0229] Text data:

[0230] "Dear customer, this product is very convenient and a great value."

[0231] Example output:

[0232] Successful pivot: "It's convenient and great value" -> This phrase leaves a good impression on customers

[0233] Wording to improve: "very" -> It would be more effective to use a concrete example (it would be better to say "This feature can be used to...")

[0234] Tone modulation: monotonous -> should be modulated

[0235] Speaker's habit: Often says "eh" -> This habit should be reduced

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] When a user starts serving a customer or negotiating a business, they start recording audio on their device (such as a smartphone or tablet). The device uses a voice recording library such as Expo Audio to collect audio data of the conversation. The recorded audio data is temporarily stored on the device.

[0239] Input: Voice of customer service or business negotiations

[0240] Output: Recorded audio data

[0241] Step 2:

[0242] When the user finishes recording, the terminal means automatically uploads the audio data to the server. The terminal means converts the audio data into a format (e.g., a WAV file) via the Internet and sends it to the server using an HTTP request.

[0243] Input: Recorded audio data

[0244] Output: Audio data file uploaded to the server

[0245] Step 3:

[0246] The server then passes the received voice data file through a speech recognition engine (such as Google Cloud Speech-to-Text or IBM Watson Speech to Text) to convert the voice data into text data. The speech recognition engine analyzes the voice file and generates the corresponding text.

[0247] Input: Audio data file uploaded to the server

[0248] Output: Text data

[0249] Step 4:

[0250] The server inputs the text data converted by the speech recognition engine into a natural language processing engine (OpenAI's GPT model) for analysis. Analysis items include turning points, phrasing, tone of voice, and speaker habits. The natural language processing engine evaluates these elements in the text and obtains analysis results.

[0251] Input: Text data

[0252] Output: Analysis results (changing points, phrasing, tone, speaker habits)

[0253] Step 5:

[0254] The server generates feedback based on the analysis results. Using a generative AI model, it creates feedback on specific areas for improvement and effective parts from the analysis results. The feedback includes specific advice that will be useful for the next customer service or business negotiation.

[0255] Input: Analysis results

[0256] Output: Generated feedback

[0257] Step 6:

[0258] The server sends the generated feedback to the terminal means, and uses an HTTP response to return the feedback data to the terminal, allowing the user to view it in real time.

[0259] Input: Generated feedback

[0260] Output: Feedback sent to the terminal

[0261] Step 7:

[0262] The terminal means displays the received feedback, and the user can check the feedback content on the terminal screen and use it for the next business negotiation or customer service.

[0263] Input: Feedback sent to the device

[0264] Output: Displayed feedback

[0265] The above is a detailed flow of the processing steps of this system.

[0266] 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.

[0267] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and user emotions when a deal is concluded or not. Specific embodiments for implementing the present invention will be described below.

[0268] System configuration

[0269] This system is mainly composed of "terminal means," "server means," and "emotion engine."

[0270] Terminal means:

[0271] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[0272] Server means:

[0273] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[0274] Emotion Engine:

[0275] It is an engine for analyzing user emotions, analyzing the user's emotional state from voice and text data and reflecting the results in feedback.

[0276] Program processing

[0277] Recording and uploading audio

[0278] A user uses a terminal means to record the voice of a business negotiation or customer service. After the recording is completed, the voice data is temporarily saved and then uploaded to a server means.

[0279] Speech recognition and text conversion

[0280] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[0281] Text analysis and feedback generation

[0282] The server stores the converted text in a database and sends it to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrases, transitions, and tonal inflections.

[0283] Emotion analysis

[0284] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotions at that time based on the intonation, tempo, and choice of words of the user's voice.

[0285] Entering successful and unsuccessful deals and generating feedback

[0286] The user inputs into the server whether the outcome of the sales negotiation or customer service was a successful or unsuccessful transaction. The server generates feedback based on the successful / unsuccessful transaction data, as well as the results of text analysis and sentiment analysis. This feedback includes specific advice on areas for improvement and effective actions.

[0287] Providing feedback

[0288] The server transmits the generated feedback to the terminal means, and the terminal provides the feedback to the user, who can use it to improve their next business negotiation or customer service.

[0289] Specific examples

[0290] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal device. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to a server. The server uses a voice recognition engine to convert the voice data into text data. The converted text is something like, "Customer, this product is very convenient and a good deal."

[0291] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[0292] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected," "You should have emphasized the specific benefits," or "Emotion analysis shows that the user is nervous about this part, so you should think of a way to help them relax."

[0293] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[0294] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[0295] The processing flow will be explained below.

[0296] Step 1:

[0297] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[0298] Step 2:

[0299] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[0300] Step 3:

[0301] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[0302] Step 4:

[0303] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[0304] Step 5:

[0305] The server stores the converted text data in a database, where it is used for subsequent analysis.

[0306] Step 6:

[0307] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[0308] Step 7:

[0309] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotional state based on the intonation, tempo, and word choice of the voice.

[0310] Step 8:

[0311] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[0312] Step 9:

[0313] The server generates feedback based on data on successful and unsuccessful transactions, as well as the results of text and sentiment analysis, including specific advice on areas for improvement and what was effective.

[0314] Step 10:

[0315] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[0316] Step 11:

[0317] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[0318] Examples:

[0319] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[0320] The server uses a speech recognition engine to convert the voice data into text, which reads, "Dear customer, this product is very convenient and a good value." The natural language processing engine then analyzes this text, examining how the change in phrases, tone, and intonation affected the speech.

[0321] At the same time, the server uses an emotion engine to analyze the user's emotions from the voice data and text data, which can detect, for example, that the user is nervous.

[0322] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "You should emphasize the specific benefits," "You should use a more modulated tone in this part," or "The user seemed nervous here, so you should think of a way to relax them."

[0323] The generated feedback is sent to the device for review by the user, who can then use it to make improvements during the next meeting, such as modulating the tone, emphasizing specific benefits, or trying relaxation techniques.

[0324] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[0325] Example 2

[0326] 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."

[0327] In conventional business negotiations and customer service, it has been difficult to obtain specific and objective feedback using user voice data to improve the closing rate. In particular, there is a demand for a system that can accurately analyze the tone and intonation of voice data and the user's emotions, and provide feedback that reflects the results. However, there is a lack of technological means to solve this problem.

[0328] 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.

[0329] In this invention, the server includes a means for converting speech into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback based on the analysis results, thereby enabling the server to analyze the user's speech data and provide specific and objective feedback that takes into account tone, intonation, and emotion.

[0330] "Information processing device means for recording audio" refers to a device that has the function of allowing a user to record audio of business negotiations or customer service, and refers to a smartphone, tablet, PC, etc.

[0331] "Information processing device means for uploading recorded voice data from information processing device means to data processing device means" is a device that has the function of transmitting recorded voice data to a cloud or server via the Internet or the like.

[0332] The "data processing device means for converting uploaded voice data into text data" is a device having the function of analyzing received voice data using a voice recognition engine and converting it into text data.

[0333] The "data processing device means for analyzing the converted text data using a natural language processing engine" is a device that has the function of analyzing the text data using natural language processing technology and identifying phrasing, tone, and the like.

[0334] The "data processing device means for generating feedback based on the analysis results" is a device having a function for generating feedback including points for improvement for the user based on the results of natural language processing and sentiment analysis.

[0335] The "data processing device means for transmitting the generated feedback to the information processing device means" is a device having a function for transmitting the generated feedback to a device used by a user via the Internet or the like.

[0336] The "information processing device means for displaying feedback transmitted to the information processing device means" is a device having a function for displaying feedback transmitted from the server to the user.

[0337] The present invention is a system that analyzes voice data during detailed business negotiations and customer service, and provides feedback based on the analysis results to increase the user's success rate. A specific implementation example of this system is described below.

[0338] System configuration

[0339] The present invention mainly comprises the following elements:

[0340] 1. An information processing device for recording audio (smartphone, tablet, PC, etc.)

[0341] 2. Information processing device means for uploading the recorded voice data to a data processing device means (cloud server)

[0342] 3. Data processing means for converting uploaded voice data into text data (Google Cloud Speech-to-Text API)

[0343] 4. A data processing device means having a natural language processing engine for analyzing the converted text data (Google Cloud Natural Language API).

[0344] 5. Data processing device means for generating feedback based on the results of text analysis and sentiment analysis (IBM Watson Tone Analyzer)

[0345] 6. Data processing device means for transmitting the generated feedback to the information processing device means.

[0346] 7. Information processing device means for displaying feedback to the user (smartphone, tablet, PC, etc.)

[0347] Program processing

[0348] First, the user uses a terminal (smartphone, tablet, PC, etc.) to record the audio of a business meeting or customer service. Once the recording is complete, the audio data is saved in temporary storage on the terminal and then automatically uploaded to a server (cloud server).

[0349] The server receives the voice data uploaded from the device and sends it to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The speech recognition engine then processes the voice data, extracts each spoken part, and converts it into text data. The converted text data is stored in a database on the server.

[0350] The converted text data is then analyzed using a natural language processing engine (e.g., Google Cloud Natural Language API) to identify text syntax, positive and negative phrases, transitions, and tonal inflections.

[0351] The server then sends the text and voice data to an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. The emotion engine determines the user's emotions from the intonation, tempo, and word choice of the voice, and generates emotion data as the analysis result.

[0352] When a user enters the results of a sales meeting or customer service session into a dedicated application, whether the transaction was successful or unsuccessful, the results are immediately sent to the server. The server generates specific feedback based on the successful / unsuccessful transaction data and the text and sentiment analysis results obtained in the previous step. This feedback includes automatically generated advice and areas for improvement.

[0353] Finally, the server sends the generated feedback to a terminal device (smartphone, tablet, PC, etc.). The terminal device notifies the user in real time via an application that displays the feedback to the user. The user can check the received feedback and use it in their next business negotiation or customer service.

[0354] Specific examples

[0355] For example, suppose a user is negotiating a sales transaction for a product and has recorded the voice of the transaction on a terminal. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[0356] The converted text should read something like this: "Dear customer, this product is very useful and a great value."

[0357] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[0358] Later, when the user inputs to the server that the deal was not closed, the server generates the following feedback based on the analysis results:

[0359] "The tone was monotonous and should have been a bit more inflected."

[0360] "We need to emphasize the specific benefits"

[0361] "Emotion analysis shows that the user is nervous about this part, so we should think about ways to relax them."

[0362] The generated feedback is sent to the terminal device and reviewed by the user. Based on this feedback, the user can make improvements during the next business meeting, such as adding inflection to the tone or emphasizing specific benefits.

[0363] Example of input prompt for generative AI model

[0364] "Generate effective feedback to close deals."

[0365] "Please create feedback based on the emotion analysis results of this audio data."

[0366] "Please provide advice on how to improve my phrasing and tone during business meetings."

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1: Record and upload your audio

[0369] A user uses a terminal means (smartphone, tablet, PC, etc.) to record the audio of business negotiations or customer service. The user starts the recording application and taps the record button to start recording. When recording is complete, the user taps the stop recording button to temporarily save the audio data. Next, the terminal automatically uploads the audio data to a data processing device means (server). The uploaded audio data is saved on the server.

[0370] Input: User recorded voice data

[0371] Output: Audio data stored on the server

[0372] Step 2: Speech recognition and text conversion

[0373] The server sends the received voice data to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The server converts the voice data into text data using the speech recognition engine, and extracts the text for each utterance. This text data is stored in the server's database.

[0374] Input: Audio data

[0375] Output: Text data

[0376] Step 3: Text analysis

[0377] The server sends the stored text data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes the text syntactically, identifies positive and negative phrases, transitions, and tonal inflections, and generates analysis results, which are then stored in a database.

[0378] Input: Text data

[0379] Output: Text analysis results

[0380] Step 4: Sentiment Analysis

[0381] The server sends the text and voice data to an emotion analysis engine (for example, IBM Watson Tone Analyzer). The emotion analysis engine analyzes the user's emotions based on the intonation, tempo, and word choice of the voice, and generates emotion data. The emotion data is stored in the server's database.

[0382] Input: Text data, audio data

[0383] Output: Emotion analysis results

[0384] Step 5: Entering closed and open deals

[0385] The user inputs whether the outcome of the business negotiation or customer service is a successful or unsuccessful contract into the data processing device (server) via a dedicated application. The input result is sent to the server and stored in a database.

[0386] Input: Results of closed or open deals

[0387] Output: Save the results of successful and unsuccessful transactions to the database

[0388] Step 6: Feedback generation

[0389] The server generates feedback based on the data on successful and unsuccessful transactions, text analysis results, and sentiment analysis results. This feedback includes areas for improvement and effective advice. The generated feedback is stored in a database.

[0390] Input: Successful and unsuccessful transactions data, text analysis results, sentiment analysis results

[0391] Output: Feedback

[0392] Step 7: Provide feedback

[0393] The server sends the generated feedback to the terminal means. The terminal notifies the user in real time via an application that displays the received feedback to the user. The user can check the feedback and use it in their next business negotiation or customer service.

[0394] Input: Feedback

[0395] Output: Feedback displayed to the user

[0396] Through the above steps, the system can perform detailed analysis of speech data from business negotiations and customer service, and provide useful feedback to the user.

[0397] (Application example 2)

[0398] 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."

[0399] While conventional customer service systems can record and evaluate conversations with customers to improve sales associates' customer service skills, it is difficult to provide detailed feedback that takes into account specific phrases, tones, and even the emotional state of the customer. Furthermore, it is not possible to quickly provide specific and effective advice based on whether the customer service result is a successful or unsuccessful transaction. As a result, improvement in sales associate skills is delayed, and there are limitations to improving customer satisfaction.

[0400] 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.

[0401] In this invention, the server includes means for converting recorded voice data into text data, means for analyzing the converted text data using a natural language processing engine, means for generating feedback based on the analysis results, means for analyzing the emotional state of the user, means for analyzing the emotional state of the customer, and means for providing specific advice for improvement. This enables analysis of the phrases and tone used by store clerks when serving customers, as well as advanced feedback based on an understanding of the emotional state of the user or customer, thereby realizing the provision of specific measures for improving skills.

[0402] "Terminal means" refers to a device that allows a user to record audio and upload the recording to a server. Examples include a smartphone, tablet, or PC.

[0403] "Server means" refers to a central management system for processing and analyzing voice data, generating and sending feedback to the terminals.

[0404] "Audio data" refers to audio information recorded by a terminal means and stored in digital format.

[0405] "Text data" refers to character information generated from voice data by a voice recognition engine.

[0406] A "natural language processing engine" refers to software that analyzes text data and extracts and evaluates information such as specific phrasing, tone, and sentiment.

[0407] "Emotion analysis means" refers to software or an engine for determining the emotional state of users and customers from voice and text data.

[0408] "Feedback" refers to specific advice and information, including areas for improvement and success, generated based on the analysis results.

[0409] "Contract / Uncontract" refers to information indicating whether or not a customer service session or business negotiation resulted in a contract being concluded.

[0410] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and emotions of the user and customer when a deal is concluded or not. A specific embodiment of this system is described below.

[0411] System configuration

[0412] This system is mainly composed of "terminal means," "server means," and "emotion analysis means."

[0413] Terminal means:

[0414] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[0415] Server means:

[0416] It is a central management system that processes and analyzes voice data. Specifically, it converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal. Specific software used includes the Google Cloud Speech-to-Text API and the Google Cloud Natural Language API.

[0417] Emotion analysis means:

[0418] This is software or an engine for analyzing the emotions of users and customers. It analyzes emotional states from voice and text data and reflects the results in feedback. Specific software used includes Microsoft® Azure® Emotion API.

[0419] Program processing

[0420] The program of this system operates as follows.

[0421] 1. Record and upload your audio

[0422] The user uses a terminal to record the audio of business negotiations or customer service. After recording is complete, the audio data is temporarily saved and then uploaded to a cloud server. The specific hardware used includes a smartphone and a microphone.

[0423] 2. Speech Recognition and Text Conversion

[0424] The server then sends the received audio data to the Google Cloud Speech-to-Text API, which converts the audio into text data. This process converts the audio file into text information, which is then sent to the next analysis step.

[0425] 3. Text analysis and feedback generation

[0426] The server then sends the converted text data to the Google Cloud Natural Language API for analysis, which identifies the text's syntax, positive and negative phrasing, transitions, and tonal inflections.

[0427] 4. Emotion analysis

[0428] The server sends text and voice data to the Microsoft Azure Emotion API, which analyzes the emotions of users and customers. The emotion engine determines the user's current emotions based on the intonation, tempo, and choice of words of the user's voice.

[0429] 5. Entering successful and unsuccessful transactions and generating feedback

[0430] The user inputs into the server whether the result of the sales negotiation or customer service was a successful or unsuccessful deal. The server generates feedback based on the data on successful and unsuccessful deals and the analysis results. This feedback includes specific advice on areas for improvement and effective actions.

[0431] 6. Providing Feedback

[0432] The server sends the generated feedback to the terminal means, where the user can check it and use it for the next business negotiation or customer service.

[0433] Specific examples

[0434] For example, suppose a user is selling a new washing machine and the following conversation takes place:

[0435] Salesperson: "This washing machine is very energy efficient and a great value."

[0436] Customer: "Please tell me specifically how it saves energy."

[0437] Salesperson: "It uses 30% less electricity than previous models."

[0438] As an example of feedback generation, the following advice is provided:

[0439] It's more effective to be confident when you say "energy saving."

[0440] When customers ask for specific information, it is a good idea to provide detailed data (for example, a comparison of monthly electricity bills).

[0441] Optionally, add examples based on the customer's expected usage scenarios to generate customer interest.

[0442] Prompt Sentence Examples

[0443] I have some audio data from a customer service session. I'd like to extract the following information from this data:

[0444] 1. Customer emotional state (positive, negative, neutral)

[0445] 2. The emotional state of the store clerk

[0446] 3. Important phrases and tone changes

[0447] 4. Customer reaction (interested, indifferent, nervous)

[0448] Then, use this information to provide feedback on:

[0449] 1. Areas for improvement in the tone and phrasing of store staff

[0450] 2. Specific advice that is likely to lead to a sale

[0451] 3. Proposing solutions tailored to customer emotions

[0452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0453] Step 1:

[0454] The user uses the terminal means to record the voice.

[0455] Input: Voice of customer service or business negotiations

[0456] How it works: A user launches the application on their smartphone and taps the record button to begin recording audio.

[0457] Output: Recorded audio data

[0458] Step 2:

[0459] After recording is complete, the user uploads the audio data to a cloud server.

[0460] Input: Audio data temporarily saved on the device

[0461] How it works: When the user taps the stop recording button, the audio data is automatically uploaded to the cloud server.

[0462] Output: Audio data stored on a cloud server

[0463] Step 3:

[0464] The server sends the received voice data to the Google Cloud Speech-to-Text API, which converts the voice data into text data.

[0465] Input: Audio data stored on a cloud server

[0466] How it works: The server sends the audio file to the Google Cloud Speech-to-Text API, which performs speech recognition processing.

[0467] Output: Recording converted to text data

[0468] Step 4:

[0469] The server sends the converted text data to the Google Cloud Natural Language API for analysis.

[0470] Input: Text data generated by speech recognition

[0471] How it works: The server sends text data to the Google Cloud Natural Language API, which performs natural language processing (NLP) on the data. Analysis includes identifying positive and negative phrases, transitions, and tonal inflections.

[0472] Output: Analysis results (specific phrases, tone inflections, emotional state, etc.)

[0473] Step 5:

[0474] The server sends text and voice data to the Microsoft Azure Emotion API to analyze user and customer emotions.

[0475] Input: Audio and text data

[0476] How it works: The server sends the data to the Microsoft Azure Emotion API, which performs the emotion analysis process.

[0477] Output: User and customer sentiment analysis results

[0478] Step 6:

[0479] The user inputs the results of the business negotiation or customer service (whether the deal was concluded or not) into the server.

[0480] Input: Results of negotiations and customer service (contracts / non-contracts)

[0481] How it works: The user uses an application on their smartphone to enter the results into the server.

[0482] Output: Results of successful and unsuccessful transactions saved on the server

[0483] Step 7:

[0484] The server generates feedback based on data on successful and unsuccessful transactions and analysis results.

[0485] Input: Analysis results and data on successful and unsuccessful transactions

[0486] How it works: The server compares your data with past data and generates specific advice, including areas for improvement and success.

[0487] Output: Feedback (areas for improvement, specific advice)

[0488] Step 8:

[0489] The server transmits the generated feedback to the terminal means.

[0490] Input: Generated feedback

[0491] How it works: The server sends the feedback to the user's smartphone.

[0492] Output: Feedback displayed on the user's smartphone

[0493] 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.

[0494] 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.

[0495] 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.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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).

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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."

[0509] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service or business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. Specific embodiments for carrying out the present invention will be described below.

[0510] System configuration

[0511] This system is mainly composed of "terminal means" and "server means."

[0512] Terminal means:

[0513] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[0514] Server means:

[0515] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[0516] Program processing

[0517] Recording and uploading audio

[0518] 1. The user uses a terminal means to record the audio of business negotiations or customer service.

[0519] 2. When the recording is completed, the audio data is temporarily saved and then uploaded to the server means.

[0520] Speech recognition and text conversion

[0521] 3. The server runs the received voice data through a voice recognition engine and converts it into text data.

[0522] 4. The converted text data is stored in a database.

[0523] Text analysis and feedback generation

[0524] 5. The server then analyzes the converted text data using a natural language processing engine. Analysis items include points of change, positive and negative phrases, and tone of voice.

[0525] 6. The user enters the result of the transaction (whether successful or unsuccessful) into the server.

[0526] 7. The server generates feedback based on the analysis results of the entered successful and unsuccessful sales. For example, the feedback may include suggestions for improving wording, effective parts, and corrections to tone and intonation.

[0527] Providing feedback

[0528] 8. The server sends the generated feedback to the terminal means, and the terminal provides the feedback to the user. The user can use this feedback to improve their next business negotiation or customer service.

[0529] Specific examples

[0530] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[0531] The server uses a speech recognition engine to convert the voice data into text data. The converted text is, "Dear customer, this product is very convenient and a good value." The natural language processing engine analyzes this text and analyzes how tone and phrasing have affected it.

[0532] If the user then inputs into the server that the deal was not concluded, the server will generate feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected" or "You should have emphasized the specific benefits."

[0533] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[0534] The above is a specific embodiment for carrying out the present invention. This system allows users to objectively evaluate and improve their own presentation skills.

[0535] The processing flow will be explained below.

[0536] Step 1:

[0537] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[0538] Step 2:

[0539] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[0540] Step 3:

[0541] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[0542] Step 4:

[0543] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[0544] Step 5:

[0545] The server stores the converted text data in a database, where it is used for subsequent analysis.

[0546] Step 6:

[0547] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[0548] Step 7:

[0549] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[0550] Step 8:

[0551] The server generates feedback based on data on successful and unsuccessful deals and text analysis results, including specific advice on areas for improvement and what was effective.

[0552] Step 9:

[0553] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[0554] Step 10:

[0555] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[0556] In this way, the entire system works in concert to help users improve their performance.

[0557] Example 1

[0558] 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."

[0559] Conventional voice recording systems for business meetings and customer service simply store the recorded audio, but lack the functionality to analyze the content in detail. This means that users cannot receive specific feedback on the success or failure of the business meeting, making it difficult for them to identify specific areas for improvement in their customer service skills or presentations. It is also difficult to analyze finer details such as tone and intonation in the audio data and provide appropriate feedback.

[0560] 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.

[0561] In this invention, the server includes a means for converting voice data into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback using a generative AI model based on the analysis results and the outcome of the deal. This allows users to record voice recordings of business negotiations or customer service, analyze the content in detail, and receive specific and appropriate feedback. Furthermore, because elements of the voice data, such as tone and intonation, are also analyzed, users can accurately identify areas for improvement in their speaking style and presentation, allowing them to effectively prepare for their next business negotiation or customer service.

[0562] The "terminal means for recording voice" is a device that a user uses to record voice during business negotiations or customer service.

[0563] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server.

[0564] The "server means for converting uploaded voice data into text data" is a device having a function for converting voice data received by the server into text information.

[0565] The "server means for analyzing the converted text data using a natural language processing engine" is a server that analyzes the data converted into character information using a specific language analysis technique.

[0566] "Server means for generating feedback using a generative AI model based on the analysis results and the results of successful or unsuccessful transactions" refers to a server that generates appropriate feedback using an artificial intelligence model based on the analyzed data and the results of negotiations entered by the user.

[0567] The "server means for transmitting generated feedback to the terminal" is a server for distributing generated feedback information to the user's device.

[0568] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server so that it can be seen by the user.

[0569] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system is mainly composed of terminal means and server means.

[0570] System configuration

[0571] Terminal means

[0572] A device used by a user. It uses hardware such as a smartphone, tablet, or PC to record audio. It also has the function of uploading the recorded data to a server. It also displays feedback received from the server.

[0573] Server Means

[0574] This is the central system that processes and analyzes voice data. It uses the Google Cloud Speech-to-Text API to convert voice into text data. It also analyzes the text data using a natural language processing engine (such as NLTK or Spacy), and generates feedback using a generative AI model (such as GPT-3) based on the analysis results and the success / failure of the transaction. The generated feedback is sent to the terminal device.

[0575] Program processing

[0576] Recording and uploading audio

[0577] 1. The user uses the terminal means to record the voice of the business negotiation or customer service. Press the recording start button to start recording.

[0578] 2. After recording is complete, the audio data is temporarily saved and then uploaded to the server by the device.

[0579] Speech recognition and text conversion

[0580] 3. The server stores the uploaded audio file in Google Cloud Storage.

[0581] 4. The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data, which is then stored in a database.

[0582] Text analysis and feedback generation

[0583] 5. The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy). The analysis includes points of change, positive and negative phrases, and tone of voice.

[0584] 6. Enter the results in a form where the user enters the outcome of the deal (won or not).

[0585] 7. The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analysis data. The generated feedback is sent to the terminal means.

[0586] Providing feedback

[0587] 8. The device displays feedback to the user, who can use it to improve their next sales meeting or customer service.

[0588] Specific examples

[0589] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal (smartphone). After the transaction, the user presses the recording stop button, and the voice data is saved on the terminal and uploaded to a server.

[0590] The server stores the voice data in Google Cloud Storage and converts it to text using the Google Cloud Speech-to-Text API. The converted text is "Dear customer, this product is very useful and a good value." A natural language processing engine (NLTK or Spacy) analyzes this text and analyzes how tone and phrasing influence it.

[0591] After that, when the user inputs into the server that the deal was not concluded, the server uses the analysis results and a generative AI model (GPT-3) to generate feedback such as, "Your tone was monotonous and should have been a bit more inflected. You need to emphasize the specific benefits."

[0592] The generated feedback is sent to the terminal means and confirmed by the user, and based on this feedback, improvements are made at the next business meeting.

[0593] Prompt Sentence Examples

[0594] Here is an example of a prompt for a generative AI model:

[0595] Negotiation audio:

[0596] "Dear customer, this product is very convenient and a great value."

[0597] Contract status: Unconcluded

[0598] Generate feedback.

[0599] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0600] Step 1:

[0601] The user starts the voice recording application using the terminal means, and presses the recording start button to start recording the voice of the business negotiation or customer service.

[0602] (Specific actions): Launch the recording app on your smartphone or tablet and tap the "Start Recording" button.

[0603] Input: Tap action of the Start Recording button.

[0604] Output: Start recording audio data.

[0605] Step 2:

[0606] After the user finishes recording, the device temporarily saves the audio file.

[0607] (Specific operation): After the negotiation is over, tap the "Stop Recording" button and the audio file will be saved to the device's local storage.

[0608] Input: Tap action of the stop recording button.

[0609] Output: Audio file saved on local storage.

[0610] Step 3:

[0611] The device will automatically compress the audio file and upload it to the server. Once the upload is complete, the user will be notified.

[0612] (Specific operation): After recording stops, the device will compress the audio file in the background and upload it to the server when connected to Wi-Fi. When the upload is complete, a "Upload complete" notification will be displayed.

[0613] Input: Audio file on local storage.

[0614] Output: The audio file uploaded to the server and an "upload complete" notification.

[0615] Step 4:

[0616] The server stores the uploaded audio files in Google Cloud Storage.

[0617] (Specific operation): The server analyzes the metadata of the audio file and stores it in the appropriate folder in Google Cloud Storage.

[0618] Input: Audio files uploaded to the server.

[0619] Output: Audio files stored in Google Cloud Storage.

[0620] Step 5:

[0621] The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data.

[0622] (Specific operation): The server passes the audio file to the API and receives text data as a response. Example: "Dear customer, this product is very convenient and a good value."

[0623] Input: Audio files stored in Google Cloud Storage.

[0624] Output: Text data.

[0625] Step 6:

[0626] The server stores the converted text data in a database.

[0627] (Specific operation): The server inserts the text data into a database such as MySQL and stores it along with the metadata of the corresponding audio file.

[0628] Input: Text data.

[0629] Output: Text data stored in a database.

[0630] Step 7:

[0631] The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy), which includes points of change, positive and negative phrases, and tone of voice.

[0632] (Specific operation): The text data is passed to an analysis script, and scores are calculated for each evaluation item. For example, "This product is very useful and a good value" is marked as a positive phrase.

[0633] Input: Text data stored in a database.

[0634] Output: Analysis results.

[0635] Step 8:

[0636] The user enters the results into the system's successful / unsuccessful result input form.

[0637] (Specific action): Select and enter the outcome of the deal from the web form or app interface. Example: Select "Not Sold."

[0638] Input: Closed or open result.

[0639] Output: The sales result data sent to the server.

[0640] Step 9:

[0641] The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analytical data.

[0642] (Specific behavior): The server passes the analysis data to GPT-3 as a prompt sentence and generates feedback. Example: "Your tone was monotonous, and you should have used more inflection."

[0643] Input: Analysis data and negotiation results.

[0644] Output: The generated feedback statement.

[0645] Step 10:

[0646] The server transmits the generated feedback to the terminal means.

[0647] (Specific operation): The server sends feedback data to the device via push notification.

[0648] Input: The generated feedback sentence.

[0649] Output: Feedback sent to the device.

[0650] Step 11:

[0651] The device displays the feedback to the user.

[0652] (Specific behavior): The app on your device displays feedback as a notification. For example, "Your tone was monotonous and should have been more inflected. You need to emphasize the specific benefits."

[0653] Input: The feedback statement sent to the terminal.

[0654] Output: The feedback displayed to the user.

[0655] (Application example 1)

[0656] 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."

[0657] In the past, it was difficult to understand what phrasing and tone contributed to closing deals during customer service and sales negotiations. Employees lacked a concrete way to objectively evaluate and improve their customer service skills, making it difficult to improve closing rates.

[0658] 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.

[0659] In this invention, the server includes a means for converting speech into text data, a means for analyzing the text data using a natural language processing engine, and a means for generating feedback based on the analysis results. This makes it easy to analyze speech data during customer service or business negotiations, and by providing specific feedback, it becomes possible to objectively and effectively improve the customer service skills of employees.

[0660] "Audio recording terminal means" refers to a device such as a smartphone or tablet that a user uses to record conversations such as business negotiations and customer service.

[0661] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server via the Internet.

[0662] The "server means for converting uploaded voice data into text data" is a server that has the function of automatically converting voice data into text data.

[0663] The "server means for analyzing the converted text data using a natural language processing engine" refers to a server equipped with a natural language processing engine for analyzing the text data and evaluating points of change, phrasing, intonation of tone, etc.

[0664] The "server means for generating feedback based on the analysis results" is a server having a function for generating specific feedback using the results of analysis by the natural language processing engine.

[0665] The "server means for transmitting the generated feedback to the terminal" is a server having a function for transmitting the generated feedback to the terminal used by the user.

[0666] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server on the screen.

[0667] The "terminal means for automatically recording audio during customer service" is a device that has the function of automatically recording conversations during customer service without user operation.

[0668] The "terminal means for providing feedback in real time" is a device that has the function of immediately providing the user with feedback generated by the server.

[0669] The "means for the user to input the results of a deal or non-deal to the server" is a function that includes an interface for the user to input the results of business negotiations or customer service to the server.

[0670] The "means for analyzing the tone and intonation of the audio data" is a natural language processing engine that has the function of analyzing and evaluating the tone and intonation of the recorded audio data.

[0671] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system consists of two main components: a terminal means and a server means.

[0672] System configuration

[0673] Terminal means

[0674] The terminal means is a device used by a user, and may be a smartphone, tablet, or PC. The terminal means has the following functions:

[0675] 1. Audio recording function: Record audio during business negotiations and customer service.

[0676] 2. Audio data upload function: Upload recorded audio data to the server.

[0677] 3. Feedback display function: Displays the feedback information received from the server.

[0678] 4. Automatic recording function: Automatically records audio while serving customers.

[0679] 5. Real-time feedback function: Provide feedback in real time.

[0680] Server Means

[0681] The Server Means is a central system for processing and analyzing voice data and has the following functions:

[0682] 1. Speech recognition: Converts received voice data into text data. Google Cloud Speech-to-Text and IBM Watson Speech to Text are used as speech recognition engines.

[0683] 2. Natural language processing: The text data is analyzed using a natural language processing engine. Analysis items include points of change, phrasing, intonation, and speaker habits. The natural language processing engine uses OpenAI's GPT model.

[0684] 3. Feedback generation: Based on the analysis results, feedback is generated on points that need improvement and effective parts.

[0685] 4. Result input reception: Provides a means for users to input the results of a transaction, whether successful or unsuccessful, to the server.

[0686] These components provide specific feedback to users, helping them improve their customer service skills.

[0687] Program processing

[0688] The program of this system is composed of the terminal means and server means described above. The processing of the program will be explained below.

[0689] Audio recording and uploading

[0690] Users use their smartphones or tablets to record conversations during business meetings or customer service. Once recording is complete, the audio data is automatically uploaded to a server via the Internet. The device used is a voice recording library such as Expo Audio.

[0691] Speech recognition and text conversion

[0692] The server receives the uploaded voice data and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text or IBM Watson Speech to Text). This converted text data is then stored in a database such as MySQL or PostgreSQL.

[0693] Natural Language Processing and Analysis

[0694] The server analyzes the stored text data using a natural language processing engine (OpenAI's GPT model), which analyzes points of change, positive and negative phrases, tone of voice, and speaker habits.

[0695] Feedback generation and provision

[0696] The server generates feedback based on the analysis results that will be useful for the next customer service or business negotiation. This feedback is sent to the terminal means and displayed to the user.

[0697] As a concrete example, consider a scenario where a user is negotiating a product sales deal in a brick-and-mortar store. The audio of the negotiation is recorded on a terminal, and once the conversation is over, the recording is automatically uploaded to a server. After a few seconds, the server completes its analysis and generates feedback, which is displayed on the terminal, such as "Your tone was monotonous; you should have used more inflection" or "You should have emphasized the specific benefits."

[0698] Example prompt sentence:

[0699] Convert the audio data recorded by the user into text data and analyze the following items.

[0700] 1. Successful turning points and their phrases

[0701] 2. What phrases should users improve and why?

[0702] 3. Tone inflection and its effects

[0703] 4. Speaker habits and how to correct them

[0704] Text data:

[0705] "Dear customer, this product is very convenient and a great value."

[0706] Example output:

[0707] Successful pivot: "It's convenient and great value" -> This phrase leaves a good impression on customers

[0708] Wording to improve: "very" -> It would be more effective to use a concrete example (it would be better to say "This feature can be used to...")

[0709] Tone modulation: monotonous -> should be modulated

[0710] Speaker's habit: Often says "eh" -> This habit should be reduced

[0711] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0712] Step 1:

[0713] When a user starts serving a customer or negotiating a business, they start recording audio on their device (such as a smartphone or tablet). The device uses a voice recording library such as Expo Audio to collect audio data of the conversation. The recorded audio data is temporarily stored on the device.

[0714] Input: Voice of customer service or business negotiations

[0715] Output: Recorded audio data

[0716] Step 2:

[0717] When the user finishes recording, the terminal means automatically uploads the audio data to the server. The terminal means converts the audio data into a format (e.g., a WAV file) via the Internet and sends it to the server using an HTTP request.

[0718] Input: Recorded audio data

[0719] Output: Audio data file uploaded to the server

[0720] Step 3:

[0721] The server then passes the received voice data file through a speech recognition engine (such as Google Cloud Speech-to-Text or IBM Watson Speech to Text) to convert the voice data into text data. The speech recognition engine analyzes the voice file and generates the corresponding text.

[0722] Input: Audio data file uploaded to the server

[0723] Output: Text data

[0724] Step 4:

[0725] The server inputs the text data converted by the speech recognition engine into a natural language processing engine (OpenAI's GPT model) for analysis. Analysis items include turning points, phrasing, tone of voice, and speaker habits. The natural language processing engine evaluates these elements in the text and obtains analysis results.

[0726] Input: Text data

[0727] Output: Analysis results (changing points, phrasing, tone, speaker habits)

[0728] Step 5:

[0729] The server generates feedback based on the analysis results. Using a generative AI model, it creates feedback on specific areas for improvement and effective parts from the analysis results. The feedback includes specific advice that will be useful for the next customer service or business negotiation.

[0730] Input: Analysis results

[0731] Output: Generated feedback

[0732] Step 6:

[0733] The server sends the generated feedback to the terminal means, and uses an HTTP response to return the feedback data to the terminal, allowing the user to view it in real time.

[0734] Input: Generated feedback

[0735] Output: Feedback sent to the terminal

[0736] Step 7:

[0737] The terminal means displays the received feedback, and the user can check the feedback content on the terminal screen and use it for the next business negotiation or customer service.

[0738] Input: Feedback sent to the device

[0739] Output: Displayed feedback

[0740] The above is a detailed flow of the processing steps of this system.

[0741] 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.

[0742] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and user emotions when a deal is concluded or not. Specific embodiments for implementing the present invention will be described below.

[0743] System configuration

[0744] This system is mainly composed of "terminal means," "server means," and "emotion engine."

[0745] Terminal means:

[0746] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[0747] Server means:

[0748] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[0749] Emotion Engine:

[0750] It is an engine for analyzing user emotions, analyzing the user's emotional state from voice and text data and reflecting the results in feedback.

[0751] Program processing

[0752] Recording and uploading audio

[0753] A user uses a terminal means to record the voice of a business negotiation or customer service. After the recording is completed, the voice data is temporarily saved and then uploaded to a server means.

[0754] Speech recognition and text conversion

[0755] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[0756] Text analysis and feedback generation

[0757] The server stores the converted text in a database and sends it to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrases, transitions, and tonal inflections.

[0758] Emotion analysis

[0759] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotions at that time based on the intonation, tempo, and choice of words of the user's voice.

[0760] Entering successful and unsuccessful deals and generating feedback

[0761] The user inputs into the server whether the outcome of the sales negotiation or customer service was a successful or unsuccessful transaction. The server generates feedback based on the successful / unsuccessful transaction data, as well as the results of text analysis and sentiment analysis. This feedback includes specific advice on areas for improvement and effective actions.

[0762] Providing feedback

[0763] The server transmits the generated feedback to the terminal means, and the terminal provides the feedback to the user, who can use it to improve their next business negotiation or customer service.

[0764] Specific examples

[0765] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal device. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to a server. The server uses a voice recognition engine to convert the voice data into text data. The converted text is something like, "Customer, this product is very convenient and a good deal."

[0766] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[0767] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected," "You should have emphasized the specific benefits," or "Emotion analysis shows that the user is nervous about this part, so you should think of a way to help them relax."

[0768] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[0769] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[0770] The processing flow will be explained below.

[0771] Step 1:

[0772] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[0773] Step 2:

[0774] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[0775] Step 3:

[0776] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[0777] Step 4:

[0778] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[0779] Step 5:

[0780] The server stores the converted text data in a database, where it is used for subsequent analysis.

[0781] Step 6:

[0782] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[0783] Step 7:

[0784] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotional state based on the intonation, tempo, and word choice of the voice.

[0785] Step 8:

[0786] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[0787] Step 9:

[0788] The server generates feedback based on data on successful and unsuccessful transactions, as well as the results of text and sentiment analysis, including specific advice on areas for improvement and what was effective.

[0789] Step 10:

[0790] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[0791] Step 11:

[0792] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[0793] Examples:

[0794] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[0795] The server uses a speech recognition engine to convert the voice data into text, which reads, "Dear customer, this product is very convenient and a good value." The natural language processing engine then analyzes this text, examining how the change in phrases, tone, and intonation affected the speech.

[0796] At the same time, the server uses an emotion engine to analyze the user's emotions from the voice data and text data, which can detect, for example, that the user is nervous.

[0797] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "You should emphasize the specific benefits," "You should use a more modulated tone in this part," or "The user seemed nervous here, so you should think of a way to relax them."

[0798] The generated feedback is sent to the device for review by the user, who can then use it to make improvements during the next meeting, such as modulating the tone, emphasizing specific benefits, or trying relaxation techniques.

[0799] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[0800] Example 2

[0801] 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."

[0802] In conventional business negotiations and customer service, it has been difficult to obtain specific and objective feedback using user voice data to improve the closing rate. In particular, there is a demand for a system that can accurately analyze the tone and intonation of voice data and the user's emotions, and provide feedback that reflects the results. However, there is a lack of technological means to solve this problem.

[0803] 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.

[0804] In this invention, the server includes a means for converting speech into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback based on the analysis results, thereby enabling the server to analyze the user's speech data and provide specific and objective feedback that takes into account tone, intonation, and emotion.

[0805] "Information processing device means for recording audio" refers to a device that has the function of allowing a user to record audio of business negotiations or customer service, and refers to a smartphone, tablet, PC, etc.

[0806] "Information processing device means for uploading recorded voice data from information processing device means to data processing device means" is a device that has the function of transmitting recorded voice data to a cloud or server via the Internet or the like.

[0807] The "data processing device means for converting uploaded voice data into text data" is a device having the function of analyzing received voice data using a voice recognition engine and converting it into text data.

[0808] The "data processing device means for analyzing the converted text data using a natural language processing engine" is a device that has the function of analyzing the text data using natural language processing technology and identifying phrasing, tone, and the like.

[0809] The "data processing device means for generating feedback based on the analysis results" is a device having a function for generating feedback including points for improvement for the user based on the results of natural language processing and sentiment analysis.

[0810] The "data processing device means for transmitting the generated feedback to the information processing device means" is a device having a function for transmitting the generated feedback to a device used by a user via the Internet or the like.

[0811] The "information processing device means for displaying feedback transmitted to the information processing device means" is a device having a function for displaying feedback transmitted from the server to the user.

[0812] The present invention is a system that analyzes voice data during detailed business negotiations and customer service, and provides feedback based on the analysis results to increase the user's success rate. A specific implementation example of this system is described below.

[0813] System configuration

[0814] The present invention mainly comprises the following elements:

[0815] 1. An information processing device for recording audio (smartphone, tablet, PC, etc.)

[0816] 2. Information processing device means for uploading the recorded voice data to a data processing device means (cloud server)

[0817] 3. Data processing means for converting uploaded voice data into text data (Google Cloud Speech-to-Text API)

[0818] 4. A data processing device means having a natural language processing engine for analyzing the converted text data (Google Cloud Natural Language API).

[0819] 5. Data processing device means for generating feedback based on the results of text analysis and sentiment analysis (IBM Watson Tone Analyzer)

[0820] 6. Data processing device means for transmitting the generated feedback to the information processing device means.

[0821] 7. Information processing device means for displaying feedback to the user (smartphone, tablet, PC, etc.)

[0822] Program processing

[0823] First, the user uses a terminal (smartphone, tablet, PC, etc.) to record the audio of a business meeting or customer service. Once the recording is complete, the audio data is saved in temporary storage on the terminal and then automatically uploaded to a server (cloud server).

[0824] The server receives the voice data uploaded from the device and sends it to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The speech recognition engine then processes the voice data, extracts each spoken part, and converts it into text data. The converted text data is stored in a database on the server.

[0825] The converted text data is then analyzed using a natural language processing engine (e.g., Google Cloud Natural Language API) to identify text syntax, positive and negative phrases, transitions, and tonal inflections.

[0826] The server then sends the text and voice data to an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. The emotion engine determines the user's emotions from the intonation, tempo, and word choice of the voice, and generates emotion data as the analysis result.

[0827] When a user enters the results of a sales meeting or customer service session into a dedicated application, whether the transaction was successful or unsuccessful, the results are immediately sent to the server. The server generates specific feedback based on the successful / unsuccessful transaction data and the text and sentiment analysis results obtained in the previous step. This feedback includes automatically generated advice and areas for improvement.

[0828] Finally, the server sends the generated feedback to a terminal device (smartphone, tablet, PC, etc.). The terminal device notifies the user in real time via an application that displays the feedback to the user. The user can check the received feedback and use it in their next business negotiation or customer service.

[0829] Specific examples

[0830] For example, suppose a user is negotiating a sales transaction for a product and has recorded the voice of the transaction on a terminal. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[0831] The converted text should read something like this: "Dear customer, this product is very useful and a great value."

[0832] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[0833] Later, when the user inputs to the server that the deal was not closed, the server generates the following feedback based on the analysis results:

[0834] "The tone was monotonous and should have been a bit more inflected."

[0835] "We need to emphasize the specific benefits"

[0836] "Emotion analysis shows that the user is nervous about this part, so we should think about ways to relax them."

[0837] The generated feedback is sent to the terminal device and reviewed by the user. Based on this feedback, the user can make improvements during the next business meeting, such as adding inflection to the tone or emphasizing specific benefits.

[0838] Example of input prompt for generative AI model

[0839] "Generate effective feedback to close deals."

[0840] "Please create feedback based on the emotion analysis results of this audio data."

[0841] "Please provide advice on how to improve my phrasing and tone during business meetings."

[0842] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0843] Step 1: Record and upload your audio

[0844] A user uses a terminal means (smartphone, tablet, PC, etc.) to record the audio of business negotiations or customer service. The user starts the recording application and taps the record button to start recording. When recording is complete, the user taps the stop recording button to temporarily save the audio data. Next, the terminal automatically uploads the audio data to a data processing device means (server). The uploaded audio data is saved on the server.

[0845] Input: User recorded voice data

[0846] Output: Audio data stored on the server

[0847] Step 2: Speech recognition and text conversion

[0848] The server sends the received voice data to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The server converts the voice data into text data using the speech recognition engine, and extracts the text for each utterance. This text data is stored in the server's database.

[0849] Input: Audio data

[0850] Output: Text data

[0851] Step 3: Text analysis

[0852] The server sends the stored text data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes the text syntactically, identifies positive and negative phrases, transitions, and tonal inflections, and generates analysis results, which are then stored in a database.

[0853] Input: Text data

[0854] Output: Text analysis results

[0855] Step 4: Sentiment Analysis

[0856] The server sends the text and voice data to an emotion analysis engine (for example, IBM Watson Tone Analyzer). The emotion analysis engine analyzes the user's emotions based on the intonation, tempo, and word choice of the voice, and generates emotion data. The emotion data is stored in the server's database.

[0857] Input: Text data, audio data

[0858] Output: Emotion analysis results

[0859] Step 5: Entering closed and open deals

[0860] The user inputs whether the outcome of the business negotiation or customer service is a successful or unsuccessful contract into the data processing device (server) via a dedicated application. The input result is sent to the server and stored in a database.

[0861] Input: Results of closed or open deals

[0862] Output: Save the results of successful and unsuccessful transactions to the database

[0863] Step 6: Feedback generation

[0864] The server generates feedback based on the data on successful and unsuccessful transactions, text analysis results, and sentiment analysis results. This feedback includes areas for improvement and effective advice. The generated feedback is stored in a database.

[0865] Input: Successful and unsuccessful transactions data, text analysis results, sentiment analysis results

[0866] Output: Feedback

[0867] Step 7: Provide feedback

[0868] The server sends the generated feedback to the terminal means. The terminal notifies the user in real time via an application that displays the received feedback to the user. The user can check the feedback and use it in their next business negotiation or customer service.

[0869] Input: Feedback

[0870] Output: Feedback displayed to the user

[0871] Through the above steps, the system can perform detailed analysis of speech data from business negotiations and customer service, and provide useful feedback to the user.

[0872] (Application example 2)

[0873] 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."

[0874] While conventional customer service systems can record and evaluate conversations with customers to improve sales associates' customer service skills, it is difficult to provide detailed feedback that takes into account specific phrases, tones, and even the emotional state of the customer. Furthermore, it is not possible to quickly provide specific and effective advice based on whether the customer service result is a successful or unsuccessful transaction. As a result, improvement in sales associate skills is delayed, and there are limitations to improving customer satisfaction.

[0875] 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.

[0876] In this invention, the server includes means for converting recorded voice data into text data, means for analyzing the converted text data using a natural language processing engine, means for generating feedback based on the analysis results, means for analyzing the emotional state of the user, means for analyzing the emotional state of the customer, and means for providing specific advice for improvement. This enables analysis of the phrases and tone used by store clerks when serving customers, as well as advanced feedback based on an understanding of the emotional state of the user or customer, thereby realizing the provision of specific measures for improving skills.

[0877] "Terminal means" refers to a device that allows a user to record audio and upload the recording to a server. Examples include a smartphone, tablet, or PC.

[0878] "Server means" refers to a central management system for processing and analyzing voice data, generating and sending feedback to the terminals.

[0879] "Audio data" refers to audio information recorded by a terminal means and stored in digital format.

[0880] "Text data" refers to character information generated from voice data by a voice recognition engine.

[0881] A "natural language processing engine" refers to software that analyzes text data and extracts and evaluates information such as specific phrasing, tone, and sentiment.

[0882] "Emotion analysis means" refers to software or an engine for determining the emotional state of users and customers from voice and text data.

[0883] "Feedback" refers to specific advice and information, including areas for improvement and success, generated based on the analysis results.

[0884] "Contract / Uncontract" refers to information indicating whether or not a customer service session or business negotiation resulted in a contract being concluded.

[0885] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and emotions of the user and customer when a deal is concluded or not. A specific embodiment of this system is described below.

[0886] System configuration

[0887] This system is mainly composed of "terminal means," "server means," and "emotion analysis means."

[0888] Terminal means:

[0889] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[0890] Server means:

[0891] It is a central management system that processes and analyzes voice data. Specifically, it converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal. Specific software used includes the Google Cloud Speech-to-Text API and the Google Cloud Natural Language API.

[0892] Emotion analysis means:

[0893] It is software or an engine for analyzing the emotions of users and customers. It analyzes the emotional state from voice and text data and reflects the results in feedback. Specific software used includes the Microsoft Azure Emotion API.

[0894] Program processing

[0895] The program of this system operates as follows.

[0896] 1. Record and upload your audio

[0897] The user uses a terminal to record the audio of business negotiations or customer service. After recording is complete, the audio data is temporarily saved and then uploaded to a cloud server. The specific hardware used includes a smartphone and a microphone.

[0898] 2. Speech Recognition and Text Conversion

[0899] The server then sends the received audio data to the Google Cloud Speech-to-Text API, which converts the audio into text data. This process converts the audio file into text information, which is then sent to the next analysis step.

[0900] 3. Text analysis and feedback generation

[0901] The server then sends the converted text data to the Google Cloud Natural Language API for analysis, which identifies the text's syntax, positive and negative phrasing, transitions, and tonal inflections.

[0902] 4. Emotion analysis

[0903] The server sends text and voice data to the Microsoft Azure Emotion API, which analyzes the emotions of users and customers. The emotion engine determines the user's current emotions based on the intonation, tempo, and choice of words of the user's voice.

[0904] 5. Entering successful and unsuccessful transactions and generating feedback

[0905] The user inputs into the server whether the result of the sales negotiation or customer service was a successful or unsuccessful deal. The server generates feedback based on the data on successful and unsuccessful deals and the analysis results. This feedback includes specific advice on areas for improvement and effective actions.

[0906] 6. Providing Feedback

[0907] The server sends the generated feedback to the terminal means, where the user can check it and use it for the next business negotiation or customer service.

[0908] Specific examples

[0909] For example, suppose a user is selling a new washing machine and the following conversation takes place:

[0910] Salesperson: "This washing machine is very energy efficient and a great value."

[0911] Customer: "Please tell me specifically how it saves energy."

[0912] Salesperson: "It uses 30% less electricity than previous models."

[0913] As an example of feedback generation, the following advice is provided:

[0914] It's more effective to be confident when you say "energy saving."

[0915] When customers ask for specific information, it is a good idea to provide detailed data (for example, a comparison of monthly electricity bills).

[0916] Optionally, add examples based on the customer's expected usage scenarios to generate customer interest.

[0917] Prompt Sentence Examples

[0918] I have some audio data from a customer service session. I'd like to extract the following information from this data:

[0919] 1. Customer emotional state (positive, negative, neutral)

[0920] 2. The emotional state of the store clerk

[0921] 3. Important phrases and tone changes

[0922] 4. Customer reaction (interested, indifferent, nervous)

[0923] Then, use this information to provide feedback on:

[0924] 1. Areas for improvement in the tone and phrasing of store staff

[0925] 2. Specific advice that is likely to lead to a sale

[0926] 3. Proposing solutions tailored to customer emotions

[0927] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0928] Step 1:

[0929] The user uses the terminal means to record the voice.

[0930] Input: Voice of customer service or business negotiations

[0931] How it works: A user launches the application on their smartphone and taps the record button to begin recording audio.

[0932] Output: Recorded audio data

[0933] Step 2:

[0934] After recording is complete, the user uploads the audio data to a cloud server.

[0935] Input: Audio data temporarily saved on the device

[0936] How it works: When the user taps the stop recording button, the audio data is automatically uploaded to the cloud server.

[0937] Output: Audio data stored on a cloud server

[0938] Step 3:

[0939] The server sends the received voice data to the Google Cloud Speech-to-Text API, which converts the voice data into text data.

[0940] Input: Audio data stored on a cloud server

[0941] How it works: The server sends the audio file to the Google Cloud Speech-to-Text API, which performs speech recognition processing.

[0942] Output: Recording converted to text data

[0943] Step 4:

[0944] The server sends the converted text data to the Google Cloud Natural Language API for analysis.

[0945] Input: Text data generated by speech recognition

[0946] How it works: The server sends text data to the Google Cloud Natural Language API, which performs natural language processing (NLP) on the data. Analysis includes identifying positive and negative phrases, transitions, and tonal inflections.

[0947] Output: Analysis results (specific phrases, tone inflections, emotional state, etc.)

[0948] Step 5:

[0949] The server sends text and voice data to the Microsoft Azure Emotion API to analyze user and customer emotions.

[0950] Input: Audio and text data

[0951] How it works: The server sends the data to the Microsoft Azure Emotion API, which performs the emotion analysis process.

[0952] Output: User and customer sentiment analysis results

[0953] Step 6:

[0954] The user inputs the results of the business negotiation or customer service (whether the deal was concluded or not) into the server.

[0955] Input: Results of negotiations and customer service (contracts / non-contracts)

[0956] How it works: The user uses an application on their smartphone to enter the results into the server.

[0957] Output: Results of successful and unsuccessful transactions saved on the server

[0958] Step 7:

[0959] The server generates feedback based on data on successful and unsuccessful transactions and analysis results.

[0960] Input: Analysis results and data on successful and unsuccessful transactions

[0961] How it works: The server compares your data with past data and generates specific advice, including areas for improvement and success.

[0962] Output: Feedback (areas for improvement, specific advice)

[0963] Step 8:

[0964] The server transmits the generated feedback to the terminal means.

[0965] Input: Generated feedback

[0966] How it works: The server sends the feedback to the user's smartphone.

[0967] Output: Feedback displayed on the user's smartphone

[0968] 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.

[0969] 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.

[0970] 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.

[0971] [Third embodiment]

[0972] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0973] 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.

[0974] 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).

[0975] 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.

[0976] 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.

[0977] 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).

[0978] 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.

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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.

[0983] 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."

[0984] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service or business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. Specific embodiments for carrying out the present invention will be described below.

[0985] System configuration

[0986] This system is mainly composed of "terminal means" and "server means."

[0987] Terminal means:

[0988] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[0989] Server means:

[0990] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[0991] Program processing

[0992] Recording and uploading audio

[0993] 1. The user uses a terminal means to record the audio of business negotiations or customer service.

[0994] 2. When the recording is completed, the audio data is temporarily saved and then uploaded to the server means.

[0995] Speech recognition and text conversion

[0996] 3. The server runs the received voice data through a voice recognition engine and converts it into text data.

[0997] 4. The converted text data is stored in a database.

[0998] Text analysis and feedback generation

[0999] 5. The server then analyzes the converted text data using a natural language processing engine. Analysis items include points of change, positive and negative phrases, and tone of voice.

[1000] 6. The user enters the result of the transaction (whether successful or unsuccessful) into the server.

[1001] 7. The server generates feedback based on the analysis results of the entered successful and unsuccessful sales. For example, the feedback may include suggestions for improving wording, effective parts, and corrections to tone and intonation.

[1002] Providing feedback

[1003] 8. The server sends the generated feedback to the terminal means, and the terminal provides the feedback to the user. The user can use this feedback to improve their next business negotiation or customer service.

[1004] Specific examples

[1005] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[1006] The server uses a speech recognition engine to convert the voice data into text data. The converted text is, "Dear customer, this product is very convenient and a good value." The natural language processing engine analyzes this text and analyzes how tone and phrasing have affected it.

[1007] If the user then inputs into the server that the deal was not concluded, the server will generate feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected" or "You should have emphasized the specific benefits."

[1008] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[1009] The above is a specific embodiment for carrying out the present invention. This system allows users to objectively evaluate and improve their own presentation skills.

[1010] The processing flow will be explained below.

[1011] Step 1:

[1012] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[1013] Step 2:

[1014] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[1015] Step 3:

[1016] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[1017] Step 4:

[1018] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[1019] Step 5:

[1020] The server stores the converted text data in a database, where it is used for subsequent analysis.

[1021] Step 6:

[1022] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[1023] Step 7:

[1024] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[1025] Step 8:

[1026] The server generates feedback based on data on successful and unsuccessful deals and text analysis results, including specific advice on areas for improvement and what was effective.

[1027] Step 9:

[1028] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[1029] Step 10:

[1030] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[1031] In this way, the entire system works in concert to help users improve their performance.

[1032] Example 1

[1033] 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."

[1034] Conventional voice recording systems for business meetings and customer service simply store the recorded audio, but lack the functionality to analyze the content in detail. This means that users cannot receive specific feedback on the success or failure of the business meeting, making it difficult for them to identify specific areas for improvement in their customer service skills or presentations. It is also difficult to analyze finer details such as tone and intonation in the audio data and provide appropriate feedback.

[1035] 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.

[1036] In this invention, the server includes a means for converting voice data into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback using a generative AI model based on the analysis results and the outcome of the deal. This allows users to record voice recordings of business negotiations or customer service, analyze the content in detail, and receive specific and appropriate feedback. Furthermore, because elements of the voice data, such as tone and intonation, are also analyzed, users can accurately identify areas for improvement in their speaking style and presentation, allowing them to effectively prepare for their next business negotiation or customer service.

[1037] The "terminal means for recording voice" is a device that a user uses to record voice during business negotiations or customer service.

[1038] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server.

[1039] The "server means for converting uploaded voice data into text data" is a device having a function for converting voice data received by the server into text information.

[1040] The "server means for analyzing the converted text data using a natural language processing engine" is a server that analyzes the data converted into character information using a specific language analysis technique.

[1041] "Server means for generating feedback using a generative AI model based on the analysis results and the results of successful or unsuccessful transactions" refers to a server that generates appropriate feedback using an artificial intelligence model based on the analyzed data and the results of negotiations entered by the user.

[1042] The "server means for transmitting generated feedback to the terminal" is a server for distributing generated feedback information to the user's device.

[1043] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server so that it can be seen by the user.

[1044] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system is mainly composed of terminal means and server means.

[1045] System configuration

[1046] Terminal means

[1047] A device used by a user. It uses hardware such as a smartphone, tablet, or PC to record audio. It also has the function of uploading the recorded data to a server. It also displays feedback received from the server.

[1048] Server Means

[1049] This is the central system that processes and analyzes voice data. It uses the Google Cloud Speech-to-Text API to convert voice into text data. It also analyzes the text data using a natural language processing engine (such as NLTK or Spacy), and generates feedback using a generative AI model (such as GPT-3) based on the analysis results and the success / failure of the transaction. The generated feedback is sent to the terminal device.

[1050] Program processing

[1051] Recording and uploading audio

[1052] 1. The user uses the terminal means to record the voice of the business negotiation or customer service. Press the recording start button to start recording.

[1053] 2. After recording is complete, the audio data is temporarily saved and then uploaded to the server by the device.

[1054] Speech recognition and text conversion

[1055] 3. The server stores the uploaded audio file in Google Cloud Storage.

[1056] 4. The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data, which is then stored in a database.

[1057] Text analysis and feedback generation

[1058] 5. The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy). The analysis includes points of change, positive and negative phrases, and tone of voice.

[1059] 6. Enter the results in a form where the user enters the outcome of the deal (won or not).

[1060] 7. The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analysis data. The generated feedback is sent to the terminal means.

[1061] Providing feedback

[1062] 8. The device displays feedback to the user, who can use it to improve their next sales meeting or customer service.

[1063] Specific examples

[1064] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal (smartphone). After the transaction, the user presses the recording stop button, and the voice data is saved on the terminal and uploaded to a server.

[1065] The server stores the voice data in Google Cloud Storage and converts it to text using the Google Cloud Speech-to-Text API. The converted text is "Dear customer, this product is very useful and a good value." A natural language processing engine (NLTK or Spacy) analyzes this text and analyzes how tone and phrasing influence it.

[1066] After that, when the user inputs into the server that the deal was not concluded, the server uses the analysis results and a generative AI model (GPT-3) to generate feedback such as, "Your tone was monotonous and should have been a bit more inflected. You need to emphasize the specific benefits."

[1067] The generated feedback is sent to the terminal means and confirmed by the user, and based on this feedback, improvements are made at the next business meeting.

[1068] Prompt Sentence Examples

[1069] Here is an example of a prompt for a generative AI model:

[1070] Negotiation audio:

[1071] "Dear customer, this product is very convenient and a great value."

[1072] Contract status: Unconcluded

[1073] Generate feedback.

[1074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1075] Step 1:

[1076] The user starts the voice recording application using the terminal means, and presses the recording start button to start recording the voice of the business negotiation or customer service.

[1077] (Specific actions): Launch the recording app on your smartphone or tablet and tap the "Start Recording" button.

[1078] Input: Tap action of the Start Recording button.

[1079] Output: Start recording audio data.

[1080] Step 2:

[1081] After the user finishes recording, the device temporarily saves the audio file.

[1082] (Specific operation): After the negotiation is over, tap the "Stop Recording" button and the audio file will be saved to the device's local storage.

[1083] Input: Tap action of the stop recording button.

[1084] Output: Audio file saved on local storage.

[1085] Step 3:

[1086] The device will automatically compress the audio file and upload it to the server. Once the upload is complete, the user will be notified.

[1087] (Specific operation): After recording stops, the device will compress the audio file in the background and upload it to the server when connected to Wi-Fi. When the upload is complete, a "Upload complete" notification will be displayed.

[1088] Input: Audio file on local storage.

[1089] Output: The audio file uploaded to the server and an "upload complete" notification.

[1090] Step 4:

[1091] The server stores the uploaded audio files in Google Cloud Storage.

[1092] (Specific operation): The server analyzes the metadata of the audio file and stores it in the appropriate folder in Google Cloud Storage.

[1093] Input: Audio files uploaded to the server.

[1094] Output: Audio files stored in Google Cloud Storage.

[1095] Step 5:

[1096] The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data.

[1097] (Specific operation): The server passes the audio file to the API and receives text data as a response. Example: "Dear customer, this product is very convenient and a good value."

[1098] Input: Audio files stored in Google Cloud Storage.

[1099] Output: Text data.

[1100] Step 6:

[1101] The server stores the converted text data in a database.

[1102] (Specific operation): The server inserts the text data into a database such as MySQL and stores it along with the metadata of the corresponding audio file.

[1103] Input: Text data.

[1104] Output: Text data stored in a database.

[1105] Step 7:

[1106] The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy), which includes points of change, positive and negative phrases, and tone of voice.

[1107] (Specific operation): The text data is passed to an analysis script, and scores are calculated for each evaluation item. For example, "This product is very useful and a good value" is marked as a positive phrase.

[1108] Input: Text data stored in a database.

[1109] Output: Analysis results.

[1110] Step 8:

[1111] The user enters the results into the system's successful / unsuccessful result input form.

[1112] (Specific action): Select and enter the outcome of the deal from the web form or app interface. Example: Select "Not Sold."

[1113] Input: Closed or open result.

[1114] Output: The sales result data sent to the server.

[1115] Step 9:

[1116] The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analytical data.

[1117] (Specific behavior): The server passes the analysis data to GPT-3 as a prompt sentence and generates feedback. Example: "Your tone was monotonous, and you should have used more inflection."

[1118] Input: Analysis data and negotiation results.

[1119] Output: The generated feedback statement.

[1120] Step 10:

[1121] The server transmits the generated feedback to the terminal means.

[1122] (Specific operation): The server sends feedback data to the device via push notification.

[1123] Input: The generated feedback sentence.

[1124] Output: Feedback sent to the device.

[1125] Step 11:

[1126] The device displays the feedback to the user.

[1127] (Specific behavior): The app on your device displays feedback as a notification. For example, "Your tone was monotonous and should have been more inflected. You need to emphasize the specific benefits."

[1128] Input: The feedback statement sent to the terminal.

[1129] Output: The feedback displayed to the user.

[1130] (Application example 1)

[1131] 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."

[1132] In the past, it was difficult to understand what phrasing and tone contributed to closing deals during customer service and sales negotiations. Employees lacked a concrete way to objectively evaluate and improve their customer service skills, making it difficult to improve closing rates.

[1133] 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.

[1134] In this invention, the server includes a means for converting speech into text data, a means for analyzing the text data using a natural language processing engine, and a means for generating feedback based on the analysis results. This makes it easy to analyze speech data during customer service or business negotiations, and by providing specific feedback, it becomes possible to objectively and effectively improve the customer service skills of employees.

[1135] "Audio recording terminal means" refers to a device such as a smartphone or tablet that a user uses to record conversations such as business negotiations and customer service.

[1136] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server via the Internet.

[1137] The "server means for converting uploaded voice data into text data" is a server that has the function of automatically converting voice data into text data.

[1138] The "server means for analyzing the converted text data using a natural language processing engine" refers to a server equipped with a natural language processing engine for analyzing the text data and evaluating points of change, phrasing, intonation of tone, etc.

[1139] The "server means for generating feedback based on the analysis results" is a server having a function for generating specific feedback using the results of analysis by the natural language processing engine.

[1140] The "server means for transmitting the generated feedback to the terminal" is a server having a function for transmitting the generated feedback to the terminal used by the user.

[1141] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server on the screen.

[1142] The "terminal means for automatically recording audio during customer service" is a device that has the function of automatically recording conversations during customer service without user operation.

[1143] The "terminal means for providing feedback in real time" is a device that has the function of immediately providing the user with feedback generated by the server.

[1144] The "means for the user to input the results of a deal or non-deal to the server" is a function that includes an interface for the user to input the results of business negotiations or customer service to the server.

[1145] The "means for analyzing the tone and intonation of the audio data" is a natural language processing engine that has the function of analyzing and evaluating the tone and intonation of the recorded audio data.

[1146] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system consists of two main components: a terminal means and a server means.

[1147] System configuration

[1148] Terminal means

[1149] The terminal means is a device used by a user, and may be a smartphone, tablet, or PC. The terminal means has the following functions:

[1150] 1. Audio recording function: Record audio during business negotiations and customer service.

[1151] 2. Audio data upload function: Upload recorded audio data to the server.

[1152] 3. Feedback display function: Displays the feedback information received from the server.

[1153] 4. Automatic recording function: Automatically records audio while serving customers.

[1154] 5. Real-time feedback function: Provide feedback in real time.

[1155] Server Means

[1156] The Server Means is a central system for processing and analyzing voice data and has the following functions:

[1157] 1. Speech recognition: Converts received voice data into text data. Google Cloud Speech-to-Text and IBM Watson Speech to Text are used as speech recognition engines.

[1158] 2. Natural language processing: The text data is analyzed using a natural language processing engine. Analysis items include points of change, phrasing, intonation, and speaker habits. The natural language processing engine uses OpenAI's GPT model.

[1159] 3. Feedback generation: Based on the analysis results, feedback is generated on points that need improvement and effective parts.

[1160] 4. Result input reception: Provides a means for users to input the results of a transaction, whether successful or unsuccessful, to the server.

[1161] These components provide specific feedback to users, helping them improve their customer service skills.

[1162] Program processing

[1163] The program of this system is composed of the terminal means and server means described above. The processing of the program will be explained below.

[1164] Audio recording and uploading

[1165] Users use their smartphones or tablets to record conversations during business meetings or customer service. Once recording is complete, the audio data is automatically uploaded to a server via the Internet. The device used is a voice recording library such as Expo Audio.

[1166] Speech recognition and text conversion

[1167] The server receives the uploaded voice data and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text or IBM Watson Speech to Text). This converted text data is then stored in a database such as MySQL or PostgreSQL.

[1168] Natural Language Processing and Analysis

[1169] The server analyzes the stored text data using a natural language processing engine (OpenAI's GPT model), which analyzes points of change, positive and negative phrases, tone of voice, and speaker habits.

[1170] Feedback generation and provision

[1171] The server generates feedback based on the analysis results that will be useful for the next customer service or business negotiation. This feedback is sent to the terminal means and displayed to the user.

[1172] As a concrete example, consider a scenario where a user is negotiating a product sales deal in a brick-and-mortar store. The audio of the negotiation is recorded on a terminal, and once the conversation is over, the recording is automatically uploaded to a server. After a few seconds, the server completes its analysis and generates feedback, which is displayed on the terminal, such as "Your tone was monotonous; you should have used more inflection" or "You should have emphasized the specific benefits."

[1173] Example prompt sentence:

[1174] Convert the audio data recorded by the user into text data and analyze the following items.

[1175] 1. Successful turning points and their phrases

[1176] 2. What phrases should users improve and why?

[1177] 3. Tone inflection and its effects

[1178] 4. Speaker habits and how to correct them

[1179] Text data:

[1180] "Dear customer, this product is very convenient and a great value."

[1181] Example output:

[1182] Successful pivot: "It's convenient and great value" -> This phrase leaves a good impression on customers

[1183] Wording to improve: "very" -> It would be more effective to use a concrete example (it would be better to say "This feature can be used to...")

[1184] Tone modulation: monotonous -> should be modulated

[1185] Speaker's habit: Often says "eh" -> This habit should be reduced

[1186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1187] Step 1:

[1188] When a user starts serving a customer or negotiating a business, they start recording audio on their device (such as a smartphone or tablet). The device uses a voice recording library such as Expo Audio to collect audio data of the conversation. The recorded audio data is temporarily stored on the device.

[1189] Input: Voice of customer service or business negotiations

[1190] Output: Recorded audio data

[1191] Step 2:

[1192] When the user finishes recording, the terminal means automatically uploads the audio data to the server. The terminal means converts the audio data into a format (e.g., a WAV file) via the Internet and sends it to the server using an HTTP request.

[1193] Input: Recorded audio data

[1194] Output: Audio data file uploaded to the server

[1195] Step 3:

[1196] The server then passes the received voice data file through a speech recognition engine (such as Google Cloud Speech-to-Text or IBM Watson Speech to Text) to convert the voice data into text data. The speech recognition engine analyzes the voice file and generates the corresponding text.

[1197] Input: Audio data file uploaded to the server

[1198] Output: Text data

[1199] Step 4:

[1200] The server inputs the text data converted by the speech recognition engine into a natural language processing engine (OpenAI's GPT model) for analysis. Analysis items include turning points, phrasing, tone of voice, and speaker habits. The natural language processing engine evaluates these elements in the text and obtains analysis results.

[1201] Input: Text data

[1202] Output: Analysis results (changing points, phrasing, tone, speaker habits)

[1203] Step 5:

[1204] The server generates feedback based on the analysis results. Using a generative AI model, it creates feedback on specific areas for improvement and effective parts from the analysis results. The feedback includes specific advice that will be useful for the next customer service or business negotiation.

[1205] Input: Analysis results

[1206] Output: Generated feedback

[1207] Step 6:

[1208] The server sends the generated feedback to the terminal means, and uses an HTTP response to return the feedback data to the terminal, allowing the user to view it in real time.

[1209] Input: Generated feedback

[1210] Output: Feedback sent to the terminal

[1211] Step 7:

[1212] The terminal means displays the received feedback, and the user can check the feedback content on the terminal screen and use it for the next business negotiation or customer service.

[1213] Input: Feedback sent to the device

[1214] Output: Displayed feedback

[1215] The above is a detailed flow of the processing steps of this system.

[1216] 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.

[1217] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and user emotions when a deal is concluded or not. Specific embodiments for implementing the present invention will be described below.

[1218] System configuration

[1219] This system is mainly composed of "terminal means," "server means," and "emotion engine."

[1220] Terminal means:

[1221] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[1222] Server means:

[1223] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[1224] Emotion Engine:

[1225] It is an engine for analyzing user emotions, analyzing the user's emotional state from voice and text data and reflecting the results in feedback.

[1226] Program processing

[1227] Recording and uploading audio

[1228] A user uses a terminal means to record the voice of a business negotiation or customer service. After the recording is completed, the voice data is temporarily saved and then uploaded to a server means.

[1229] Speech recognition and text conversion

[1230] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[1231] Text analysis and feedback generation

[1232] The server stores the converted text in a database and sends it to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrases, transitions, and tonal inflections.

[1233] Emotion analysis

[1234] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotions at that time based on the intonation, tempo, and choice of words of the user's voice.

[1235] Entering successful and unsuccessful deals and generating feedback

[1236] The user inputs into the server whether the outcome of the sales negotiation or customer service was a successful or unsuccessful transaction. The server generates feedback based on the successful / unsuccessful transaction data, as well as the results of text analysis and sentiment analysis. This feedback includes specific advice on areas for improvement and effective actions.

[1237] Providing feedback

[1238] The server transmits the generated feedback to the terminal means, and the terminal provides the feedback to the user, who can use it to improve their next business negotiation or customer service.

[1239] Specific examples

[1240] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal device. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to a server. The server uses a voice recognition engine to convert the voice data into text data. The converted text is something like, "Customer, this product is very convenient and a good deal."

[1241] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[1242] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected," "You should have emphasized the specific benefits," or "Emotion analysis shows that the user is nervous about this part, so you should think of a way to help them relax."

[1243] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[1244] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[1245] The processing flow will be explained below.

[1246] Step 1:

[1247] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[1248] Step 2:

[1249] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[1250] Step 3:

[1251] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[1252] Step 4:

[1253] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[1254] Step 5:

[1255] The server stores the converted text data in a database, where it is used for subsequent analysis.

[1256] Step 6:

[1257] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[1258] Step 7:

[1259] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotional state based on the intonation, tempo, and word choice of the voice.

[1260] Step 8:

[1261] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[1262] Step 9:

[1263] The server generates feedback based on data on successful and unsuccessful transactions, as well as the results of text and sentiment analysis, including specific advice on areas for improvement and what was effective.

[1264] Step 10:

[1265] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[1266] Step 11:

[1267] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[1268] Examples:

[1269] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[1270] The server uses a speech recognition engine to convert the voice data into text, which reads, "Dear customer, this product is very convenient and a good value." The natural language processing engine then analyzes this text, examining how the change in phrases, tone, and intonation affected the speech.

[1271] At the same time, the server uses an emotion engine to analyze the user's emotions from the voice data and text data, which can detect, for example, that the user is nervous.

[1272] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "You should emphasize the specific benefits," "You should use a more modulated tone in this part," or "The user seemed nervous here, so you should think of a way to relax them."

[1273] The generated feedback is sent to the device for review by the user, who can then use it to make improvements during the next meeting, such as modulating the tone, emphasizing specific benefits, or trying relaxation techniques.

[1274] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[1275] Example 2

[1276] 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."

[1277] In conventional business negotiations and customer service, it has been difficult to obtain specific and objective feedback using user voice data to improve the closing rate. In particular, there is a demand for a system that can accurately analyze the tone and intonation of voice data and the user's emotions, and provide feedback that reflects the results. However, there is a lack of technological means to solve this problem.

[1278] 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.

[1279] In this invention, the server includes a means for converting speech into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback based on the analysis results, thereby enabling the server to analyze the user's speech data and provide specific and objective feedback that takes into account tone, intonation, and emotion.

[1280] "Information processing device means for recording audio" refers to a device that has the function of allowing a user to record audio of business negotiations or customer service, and refers to a smartphone, tablet, PC, etc.

[1281] "Information processing device means for uploading recorded voice data from information processing device means to data processing device means" is a device that has the function of transmitting recorded voice data to a cloud or server via the Internet or the like.

[1282] The "data processing device means for converting uploaded voice data into text data" is a device having the function of analyzing received voice data using a voice recognition engine and converting it into text data.

[1283] The "data processing device means for analyzing the converted text data using a natural language processing engine" is a device that has the function of analyzing the text data using natural language processing technology and identifying phrasing, tone, and the like.

[1284] The "data processing device means for generating feedback based on the analysis results" is a device having a function for generating feedback including points for improvement for the user based on the results of natural language processing and sentiment analysis.

[1285] The "data processing device means for transmitting the generated feedback to the information processing device means" is a device having a function for transmitting the generated feedback to a device used by a user via the Internet or the like.

[1286] The "information processing device means for displaying feedback transmitted to the information processing device means" is a device having a function for displaying feedback transmitted from the server to the user.

[1287] The present invention is a system that analyzes voice data during detailed business negotiations and customer service, and provides feedback based on the analysis results to increase the user's success rate. A specific implementation example of this system is described below.

[1288] System configuration

[1289] The present invention mainly comprises the following elements:

[1290] 1. An information processing device for recording audio (smartphone, tablet, PC, etc.)

[1291] 2. Information processing device means for uploading the recorded voice data to a data processing device means (cloud server)

[1292] 3. Data processing means for converting uploaded voice data into text data (Google Cloud Speech-to-Text API)

[1293] 4. A data processing device means having a natural language processing engine for analyzing the converted text data (Google Cloud Natural Language API).

[1294] 5. Data processing device means for generating feedback based on the results of text analysis and sentiment analysis (IBM Watson Tone Analyzer)

[1295] 6. Data processing device means for transmitting the generated feedback to the information processing device means.

[1296] 7. Information processing device means for displaying feedback to the user (smartphone, tablet, PC, etc.)

[1297] Program processing

[1298] First, the user uses a terminal (smartphone, tablet, PC, etc.) to record the audio of a business meeting or customer service. Once the recording is complete, the audio data is saved in temporary storage on the terminal and then automatically uploaded to a server (cloud server).

[1299] The server receives the voice data uploaded from the device and sends it to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The speech recognition engine then processes the voice data, extracts each spoken part, and converts it into text data. The converted text data is stored in a database on the server.

[1300] The converted text data is then analyzed using a natural language processing engine (e.g., Google Cloud Natural Language API) to identify text syntax, positive and negative phrases, transitions, and tonal inflections.

[1301] The server then sends the text and voice data to an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. The emotion engine determines the user's emotions from the intonation, tempo, and word choice of the voice, and generates emotion data as the analysis result.

[1302] When a user enters the results of a sales meeting or customer service session into a dedicated application, whether the transaction was successful or unsuccessful, the results are immediately sent to the server. The server generates specific feedback based on the successful / unsuccessful transaction data and the text and sentiment analysis results obtained in the previous step. This feedback includes automatically generated advice and areas for improvement.

[1303] Finally, the server sends the generated feedback to a terminal device (smartphone, tablet, PC, etc.). The terminal device notifies the user in real time via an application that displays the feedback to the user. The user can check the received feedback and use it in their next business negotiation or customer service.

[1304] Specific examples

[1305] For example, suppose a user is negotiating a sales transaction for a product and has recorded the voice of the transaction on a terminal. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[1306] The converted text should read something like this: "Dear customer, this product is very useful and a great value."

[1307] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[1308] Later, when the user inputs to the server that the deal was not closed, the server generates the following feedback based on the analysis results:

[1309] "The tone was monotonous and should have been a bit more inflected."

[1310] "We need to emphasize the specific benefits"

[1311] "Emotion analysis shows that the user is nervous about this part, so we should think about ways to relax them."

[1312] The generated feedback is sent to the terminal device and reviewed by the user. Based on this feedback, the user can make improvements during the next business meeting, such as adding inflection to the tone or emphasizing specific benefits.

[1313] Example of input prompt for generative AI model

[1314] "Generate effective feedback to close deals."

[1315] "Please create feedback based on the emotion analysis results of this audio data."

[1316] "Please provide advice on how to improve my phrasing and tone during business meetings."

[1317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1318] Step 1: Record and upload your audio

[1319] A user uses a terminal means (smartphone, tablet, PC, etc.) to record the audio of business negotiations or customer service. The user starts the recording application and taps the record button to start recording. When recording is complete, the user taps the stop recording button to temporarily save the audio data. Next, the terminal automatically uploads the audio data to a data processing device means (server). The uploaded audio data is saved on the server.

[1320] Input: User recorded voice data

[1321] Output: Audio data stored on the server

[1322] Step 2: Speech recognition and text conversion

[1323] The server sends the received voice data to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The server converts the voice data into text data using the speech recognition engine, and extracts the text for each utterance. This text data is stored in the server's database.

[1324] Input: Audio data

[1325] Output: Text data

[1326] Step 3: Text analysis

[1327] The server sends the stored text data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes the text syntactically, identifies positive and negative phrases, transitions, and tonal inflections, and generates analysis results, which are then stored in a database.

[1328] Input: Text data

[1329] Output: Text analysis results

[1330] Step 4: Sentiment Analysis

[1331] The server sends the text and voice data to an emotion analysis engine (for example, IBM Watson Tone Analyzer). The emotion analysis engine analyzes the user's emotions based on the intonation, tempo, and word choice of the voice, and generates emotion data. The emotion data is stored in the server's database.

[1332] Input: Text data, audio data

[1333] Output: Emotion analysis results

[1334] Step 5: Entering closed and open deals

[1335] The user inputs whether the outcome of the business negotiation or customer service is a successful or unsuccessful contract into the data processing device (server) via a dedicated application. The input result is sent to the server and stored in a database.

[1336] Input: Results of closed or open deals

[1337] Output: Save the results of successful and unsuccessful transactions to the database

[1338] Step 6: Feedback generation

[1339] The server generates feedback based on the data on successful and unsuccessful transactions, text analysis results, and sentiment analysis results. This feedback includes areas for improvement and effective advice. The generated feedback is stored in a database.

[1340] Input: Successful and unsuccessful transactions data, text analysis results, sentiment analysis results

[1341] Output: Feedback

[1342] Step 7: Provide feedback

[1343] The server sends the generated feedback to the terminal means. The terminal notifies the user in real time via an application that displays the received feedback to the user. The user can check the feedback and use it in their next business negotiation or customer service.

[1344] Input: Feedback

[1345] Output: Feedback displayed to the user

[1346] Through the above steps, the system can perform detailed analysis of speech data from business negotiations and customer service, and provide useful feedback to the user.

[1347] (Application example 2)

[1348] 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."

[1349] While conventional customer service systems can record and evaluate conversations with customers to improve sales associates' customer service skills, it is difficult to provide detailed feedback that takes into account specific phrases, tones, and even the emotional state of the customer. Furthermore, it is not possible to quickly provide specific and effective advice based on whether the customer service result is a successful or unsuccessful transaction. As a result, improvement in sales associate skills is delayed, and there are limitations to improving customer satisfaction.

[1350] 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.

[1351] In this invention, the server includes means for converting recorded voice data into text data, means for analyzing the converted text data using a natural language processing engine, means for generating feedback based on the analysis results, means for analyzing the emotional state of the user, means for analyzing the emotional state of the customer, and means for providing specific advice for improvement. This enables analysis of the phrases and tone used by store clerks when serving customers, as well as advanced feedback based on an understanding of the emotional state of the user or customer, thereby realizing the provision of specific measures for improving skills.

[1352] "Terminal means" refers to a device that allows a user to record audio and upload the recording to a server. Examples include a smartphone, tablet, or PC.

[1353] "Server means" refers to a central management system for processing and analyzing voice data, generating and sending feedback to the terminals.

[1354] "Audio data" refers to audio information recorded by a terminal means and stored in digital format.

[1355] "Text data" refers to character information generated from voice data by a voice recognition engine.

[1356] A "natural language processing engine" refers to software that analyzes text data and extracts and evaluates information such as specific phrasing, tone, and sentiment.

[1357] "Emotion analysis means" refers to software or an engine for determining the emotional state of users and customers from voice and text data.

[1358] "Feedback" refers to specific advice and information, including areas for improvement and success, generated based on the analysis results.

[1359] "Contract / Uncontract" refers to information indicating whether or not a customer service session or business negotiation resulted in a contract being concluded.

[1360] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and emotions of the user and customer when a deal is concluded or not. A specific embodiment of this system is described below.

[1361] System configuration

[1362] This system is mainly composed of "terminal means," "server means," and "emotion analysis means."

[1363] Terminal means:

[1364] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[1365] Server means:

[1366] It is a central management system that processes and analyzes voice data. Specifically, it converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal. Specific software used includes the Google Cloud Speech-to-Text API and the Google Cloud Natural Language API.

[1367] Emotion analysis means:

[1368] It is software or an engine for analyzing the emotions of users and customers. It analyzes the emotional state from voice and text data and reflects the results in feedback. Specific software used includes the Microsoft Azure Emotion API.

[1369] Program processing

[1370] The program of this system operates as follows.

[1371] 1. Record and upload your audio

[1372] The user uses a terminal to record the audio of business negotiations or customer service. After recording is complete, the audio data is temporarily saved and then uploaded to a cloud server. The specific hardware used includes a smartphone and a microphone.

[1373] 2. Speech Recognition and Text Conversion

[1374] The server then sends the received audio data to the Google Cloud Speech-to-Text API, which converts the audio into text data. This process converts the audio file into text information, which is then sent to the next analysis step.

[1375] 3. Text analysis and feedback generation

[1376] The server then sends the converted text data to the Google Cloud Natural Language API for analysis, which identifies the text's syntax, positive and negative phrasing, transitions, and tonal inflections.

[1377] 4. Emotion analysis

[1378] The server sends text and voice data to the Microsoft Azure Emotion API, which analyzes the emotions of users and customers. The emotion engine determines the user's current emotions based on the intonation, tempo, and choice of words of the user's voice.

[1379] 5. Entering successful and unsuccessful transactions and generating feedback

[1380] The user inputs into the server whether the result of the sales negotiation or customer service was a successful or unsuccessful deal. The server generates feedback based on the data on successful and unsuccessful deals and the analysis results. This feedback includes specific advice on areas for improvement and effective actions.

[1381] 6. Providing Feedback

[1382] The server sends the generated feedback to the terminal means, where the user can check it and use it for the next business negotiation or customer service.

[1383] Specific examples

[1384] For example, suppose a user is selling a new washing machine and the following conversation takes place:

[1385] Salesperson: "This washing machine is very energy efficient and a great value."

[1386] Customer: "Please tell me specifically how it saves energy."

[1387] Salesperson: "It uses 30% less electricity than previous models."

[1388] As an example of feedback generation, the following advice is provided:

[1389] It's more effective to be confident when you say "energy saving."

[1390] When customers ask for specific information, it is a good idea to provide detailed data (for example, a comparison of monthly electricity bills).

[1391] Optionally, add examples based on the customer's expected usage scenarios to generate customer interest.

[1392] Prompt Sentence Examples

[1393] I have some audio data from a customer service session. I'd like to extract the following information from this data:

[1394] 1. Customer emotional state (positive, negative, neutral)

[1395] 2. The emotional state of the store clerk

[1396] 3. Important phrases and tone changes

[1397] 4. Customer reaction (interested, indifferent, nervous)

[1398] Then, use this information to provide feedback on:

[1399] 1. Areas for improvement in the tone and phrasing of store staff

[1400] 2. Specific advice that is likely to lead to a sale

[1401] 3. Proposing solutions tailored to customer emotions

[1402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1403] Step 1:

[1404] The user uses the terminal means to record the voice.

[1405] Input: Voice of customer service or business negotiations

[1406] How it works: A user launches the application on their smartphone and taps the record button to begin recording audio.

[1407] Output: Recorded audio data

[1408] Step 2:

[1409] After recording is complete, the user uploads the audio data to a cloud server.

[1410] Input: Audio data temporarily saved on the device

[1411] How it works: When the user taps the stop recording button, the audio data is automatically uploaded to the cloud server.

[1412] Output: Audio data stored on a cloud server

[1413] Step 3:

[1414] The server sends the received voice data to the Google Cloud Speech-to-Text API, which converts the voice data into text data.

[1415] Input: Audio data stored on a cloud server

[1416] How it works: The server sends the audio file to the Google Cloud Speech-to-Text API, which performs speech recognition processing.

[1417] Output: Recording converted to text data

[1418] Step 4:

[1419] The server sends the converted text data to the Google Cloud Natural Language API for analysis.

[1420] Input: Text data generated by speech recognition

[1421] How it works: The server sends text data to the Google Cloud Natural Language API, which performs natural language processing (NLP) on the data. Analysis includes identifying positive and negative phrases, transitions, and tonal inflections.

[1422] Output: Analysis results (specific phrases, tone inflections, emotional state, etc.)

[1423] Step 5:

[1424] The server sends text and voice data to the Microsoft Azure Emotion API to analyze user and customer emotions.

[1425] Input: Audio and text data

[1426] How it works: The server sends the data to the Microsoft Azure Emotion API, which performs the emotion analysis process.

[1427] Output: User and customer sentiment analysis results

[1428] Step 6:

[1429] The user inputs the results of the business negotiation or customer service (whether the deal was concluded or not) into the server.

[1430] Input: Results of negotiations and customer service (contracts / non-contracts)

[1431] How it works: The user uses an application on their smartphone to enter the results into the server.

[1432] Output: Results of successful and unsuccessful transactions saved on the server

[1433] Step 7:

[1434] The server generates feedback based on data on successful and unsuccessful transactions and analysis results.

[1435] Input: Analysis results and data on successful and unsuccessful transactions

[1436] How it works: The server compares your data with past data and generates specific advice, including areas for improvement and success.

[1437] Output: Feedback (areas for improvement, specific advice)

[1438] Step 8:

[1439] The server transmits the generated feedback to the terminal means.

[1440] Input: Generated feedback

[1441] How it works: The server sends the feedback to the user's smartphone.

[1442] Output: Feedback displayed on the user's smartphone

[1443] 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.

[1444] 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.

[1445] 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.

[1446] [Fourth embodiment]

[1447] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1448] 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.

[1449] 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).

[1450] 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.

[1451] 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.

[1452] 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).

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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.

[1459] 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."

[1460] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service or business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. Specific embodiments for carrying out the present invention will be described below.

[1461] System configuration

[1462] This system is mainly composed of "terminal means" and "server means."

[1463] Terminal means:

[1464] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[1465] Server means:

[1466] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[1467] Program processing

[1468] Recording and uploading audio

[1469] 1. The user uses a terminal means to record the audio of business negotiations or customer service.

[1470] 2. When the recording is completed, the audio data is temporarily saved and then uploaded to the server means.

[1471] Speech recognition and text conversion

[1472] 3. The server runs the received voice data through a voice recognition engine and converts it into text data.

[1473] 4. The converted text data is stored in a database.

[1474] Text analysis and feedback generation

[1475] 5. The server then analyzes the converted text data using a natural language processing engine. Analysis items include points of change, positive and negative phrases, and tone of voice.

[1476] 6. The user enters the result of the transaction (whether successful or unsuccessful) into the server.

[1477] 7. The server generates feedback based on the analysis results of the entered successful and unsuccessful sales. For example, the feedback may include suggestions for improving wording, effective parts, and corrections to tone and intonation.

[1478] Providing feedback

[1479] 8. The server sends the generated feedback to the terminal means, and the terminal provides the feedback to the user. The user can use this feedback to improve their next business negotiation or customer service.

[1480] Specific examples

[1481] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[1482] The server uses a speech recognition engine to convert the voice data into text data. The converted text is, "Dear customer, this product is very convenient and a good value." The natural language processing engine analyzes this text and analyzes how tone and phrasing have affected it.

[1483] If the user then inputs into the server that the deal was not concluded, the server will generate feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected" or "You should have emphasized the specific benefits."

[1484] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[1485] The above is a specific embodiment for carrying out the present invention. This system allows users to objectively evaluate and improve their own presentation skills.

[1486] The processing flow will be explained below.

[1487] Step 1:

[1488] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[1489] Step 2:

[1490] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[1491] Step 3:

[1492] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[1493] Step 4:

[1494] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[1495] Step 5:

[1496] The server stores the converted text data in a database, where it is used for subsequent analysis.

[1497] Step 6:

[1498] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[1499] Step 7:

[1500] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[1501] Step 8:

[1502] The server generates feedback based on data on successful and unsuccessful deals and text analysis results, including specific advice on areas for improvement and what was effective.

[1503] Step 9:

[1504] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[1505] Step 10:

[1506] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[1507] In this way, the entire system works in concert to help users improve their performance.

[1508] Example 1

[1509] 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."

[1510] Conventional voice recording systems for business meetings and customer service simply store the recorded audio, but lack the functionality to analyze the content in detail. This means that users cannot receive specific feedback on the success or failure of the business meeting, making it difficult for them to identify specific areas for improvement in their customer service skills or presentations. It is also difficult to analyze finer details such as tone and intonation in the audio data and provide appropriate feedback.

[1511] 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.

[1512] In this invention, the server includes a means for converting voice data into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback using a generative AI model based on the analysis results and the outcome of the deal. This allows users to record voice recordings of business negotiations or customer service, analyze the content in detail, and receive specific and appropriate feedback. Furthermore, because elements of the voice data, such as tone and intonation, are also analyzed, users can accurately identify areas for improvement in their speaking style and presentation, allowing them to effectively prepare for their next business negotiation or customer service.

[1513] The "terminal means for recording voice" is a device that a user uses to record voice during business negotiations or customer service.

[1514] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server.

[1515] The "server means for converting uploaded voice data into text data" is a device having a function for converting voice data received by the server into text information.

[1516] The "server means for analyzing the converted text data using a natural language processing engine" is a server that analyzes the data converted into character information using a specific language analysis technique.

[1517] "Server means for generating feedback using a generative AI model based on the analysis results and the results of successful or unsuccessful transactions" refers to a server that generates appropriate feedback using an artificial intelligence model based on the analyzed data and the results of negotiations entered by the user.

[1518] The "server means for transmitting generated feedback to the terminal" is a server for distributing generated feedback information to the user's device.

[1519] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server so that it can be seen by the user.

[1520] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system is mainly composed of terminal means and server means.

[1521] System configuration

[1522] Terminal means

[1523] A device used by a user. It uses hardware such as a smartphone, tablet, or PC to record audio. It also has the function of uploading the recorded data to a server. It also displays feedback received from the server.

[1524] Server Means

[1525] This is the central system that processes and analyzes voice data. It uses the Google Cloud Speech-to-Text API to convert voice into text data. It also analyzes the text data using a natural language processing engine (such as NLTK or Spacy), and generates feedback using a generative AI model (such as GPT-3) based on the analysis results and the success / failure of the transaction. The generated feedback is sent to the terminal device.

[1526] Program processing

[1527] Recording and uploading audio

[1528] 1. The user uses the terminal means to record the voice of the business negotiation or customer service. Press the recording start button to start recording.

[1529] 2. After recording is complete, the audio data is temporarily saved and then uploaded to the server by the device.

[1530] Speech recognition and text conversion

[1531] 3. The server stores the uploaded audio file in Google Cloud Storage.

[1532] 4. The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data, which is then stored in a database.

[1533] Text analysis and feedback generation

[1534] 5. The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy). The analysis includes points of change, positive and negative phrases, and tone of voice.

[1535] 6. Enter the results in a form where the user enters the outcome of the deal (won or not).

[1536] 7. The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analysis data. The generated feedback is sent to the terminal means.

[1537] Providing feedback

[1538] 8. The device displays feedback to the user, who can use it to improve their next sales meeting or customer service.

[1539] Specific examples

[1540] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal (smartphone). After the transaction, the user presses the recording stop button, and the voice data is saved on the terminal and uploaded to a server.

[1541] The server stores the voice data in Google Cloud Storage and converts it to text using the Google Cloud Speech-to-Text API. The converted text is "Dear customer, this product is very useful and a good value." A natural language processing engine (NLTK or Spacy) analyzes this text and analyzes how tone and phrasing influence it.

[1542] After that, when the user inputs into the server that the deal was not concluded, the server uses the analysis results and a generative AI model (GPT-3) to generate feedback such as, "Your tone was monotonous and should have been a bit more inflected. You need to emphasize the specific benefits."

[1543] The generated feedback is sent to the terminal means and confirmed by the user, and based on this feedback, improvements are made at the next business meeting.

[1544] Prompt Sentence Examples

[1545] Here is an example of a prompt for a generative AI model:

[1546] Negotiation audio:

[1547] "Dear customer, this product is very convenient and a great value."

[1548] Contract status: Unconcluded

[1549] Generate feedback.

[1550] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1551] Step 1:

[1552] The user starts the voice recording application using the terminal means, and presses the recording start button to start recording the voice of the business negotiation or customer service.

[1553] (Specific actions): Launch the recording app on your smartphone or tablet and tap the "Start Recording" button.

[1554] Input: Tap action of the Start Recording button.

[1555] Output: Start recording audio data.

[1556] Step 2:

[1557] After the user finishes recording, the device temporarily saves the audio file.

[1558] (Specific operation): After the negotiation is over, tap the "Stop Recording" button and the audio file will be saved to the device's local storage.

[1559] Input: Tap action of the stop recording button.

[1560] Output: Audio file saved on local storage.

[1561] Step 3:

[1562] The device will automatically compress the audio file and upload it to the server. Once the upload is complete, the user will be notified.

[1563] (Specific operation): After recording stops, the device will compress the audio file in the background and upload it to the server when connected to Wi-Fi. When the upload is complete, a "Upload complete" notification will be displayed.

[1564] Input: Audio file on local storage.

[1565] Output: The audio file uploaded to the server and an "upload complete" notification.

[1566] Step 4:

[1567] The server stores the uploaded audio files in Google Cloud Storage.

[1568] (Specific operation): The server analyzes the metadata of the audio file and stores it in the appropriate folder in Google Cloud Storage.

[1569] Input: Audio files uploaded to the server.

[1570] Output: Audio files stored in Google Cloud Storage.

[1571] Step 5:

[1572] The server uses the Google Cloud Speech-to-Text API to convert the audio file into text data.

[1573] (Specific operation): The server passes the audio file to the API and receives text data as a response. Example: "Dear customer, this product is very convenient and a good value."

[1574] Input: Audio files stored in Google Cloud Storage.

[1575] Output: Text data.

[1576] Step 6:

[1577] The server stores the converted text data in a database.

[1578] (Specific operation): The server inserts the text data into a database such as MySQL and stores it along with the metadata of the corresponding audio file.

[1579] Input: Text data.

[1580] Output: Text data stored in a database.

[1581] Step 7:

[1582] The server analyzes the stored text data using a natural language processing engine (NLTK or Spacy), which includes points of change, positive and negative phrases, and tone of voice.

[1583] (Specific operation): The text data is passed to an analysis script, and scores are calculated for each evaluation item. For example, "This product is very useful and a good value" is marked as a positive phrase.

[1584] Input: Text data stored in a database.

[1585] Output: Analysis results.

[1586] Step 8:

[1587] The user enters the results into the system's successful / unsuccessful result input form.

[1588] (Specific action): Select and enter the outcome of the deal from the web form or app interface. Example: Select "Not Sold."

[1589] Input: Closed or open result.

[1590] Output: The sales result data sent to the server.

[1591] Step 9:

[1592] The server generates feedback using a generative AI model (such as GPT-3) based on the results of successful and unsuccessful transactions and analytical data.

[1593] (Specific behavior): The server passes the analysis data to GPT-3 as a prompt sentence and generates feedback. Example: "Your tone was monotonous, and you should have used more inflection."

[1594] Input: Analysis data and negotiation results.

[1595] Output: The generated feedback statement.

[1596] Step 10:

[1597] The server transmits the generated feedback to the terminal means.

[1598] (Specific operation): The server sends feedback data to the device via push notification.

[1599] Input: The generated feedback sentence.

[1600] Output: Feedback sent to the device.

[1601] Step 11:

[1602] The device displays the feedback to the user.

[1603] (Specific behavior): The app on your device displays feedback as a notification. For example, "Your tone was monotonous and should have been more inflected. You need to emphasize the specific benefits."

[1604] Input: The feedback statement sent to the terminal.

[1605] Output: The feedback displayed to the user.

[1606] (Application example 1)

[1607] 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."

[1608] In the past, it was difficult to understand what phrasing and tone contributed to closing deals during customer service and sales negotiations. Employees lacked a concrete way to objectively evaluate and improve their customer service skills, making it difficult to improve closing rates.

[1609] 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.

[1610] In this invention, the server includes a means for converting speech into text data, a means for analyzing the text data using a natural language processing engine, and a means for generating feedback based on the analysis results. This makes it easy to analyze speech data during customer service or business negotiations, and by providing specific feedback, it becomes possible to objectively and effectively improve the customer service skills of employees.

[1611] "Audio recording terminal means" refers to a device such as a smartphone or tablet that a user uses to record conversations such as business negotiations and customer service.

[1612] The "terminal means for uploading recorded voice data to a server" is a device that has the function of transmitting recorded voice data to a server via the Internet.

[1613] The "server means for converting uploaded voice data into text data" is a server that has the function of automatically converting voice data into text data.

[1614] The "server means for analyzing the converted text data using a natural language processing engine" refers to a server equipped with a natural language processing engine for analyzing the text data and evaluating points of change, phrasing, intonation of tone, etc.

[1615] The "server means for generating feedback based on the analysis results" is a server having a function for generating specific feedback using the results of analysis by the natural language processing engine.

[1616] The "server means for transmitting the generated feedback to the terminal" is a server having a function for transmitting the generated feedback to the terminal used by the user.

[1617] The "terminal means for displaying the feedback transmitted to the terminal" is a device having a function for displaying the feedback transmitted from the server on the screen.

[1618] The "terminal means for automatically recording audio during customer service" is a device that has the function of automatically recording conversations during customer service without user operation.

[1619] The "terminal means for providing feedback in real time" is a device that has the function of immediately providing the user with feedback generated by the server.

[1620] The "means for the user to input the results of a deal or non-deal to the server" is a function that includes an interface for the user to input the results of business negotiations or customer service to the server.

[1621] The "means for analyzing the tone and intonation of the audio data" is a natural language processing engine that has the function of analyzing and evaluating the tone and intonation of the recorded audio data.

[1622] This invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and speaking habits of the speaker when a deal is concluded or not. This system consists of two main components: a terminal means and a server means.

[1623] System configuration

[1624] Terminal means

[1625] The terminal means is a device used by a user, and may be a smartphone, tablet, or PC. The terminal means has the following functions:

[1626] 1. Audio recording function: Record audio during business negotiations and customer service.

[1627] 2. Audio data upload function: Upload recorded audio data to the server.

[1628] 3. Feedback display function: Displays the feedback information received from the server.

[1629] 4. Automatic recording function: Automatically records audio while serving customers.

[1630] 5. Real-time feedback function: Provide feedback in real time.

[1631] Server Means

[1632] The Server Means is a central system for processing and analyzing voice data and has the following functions:

[1633] 1. Speech recognition: Converts received voice data into text data. Google Cloud Speech-to-Text and IBM Watson Speech to Text are used as speech recognition engines.

[1634] 2. Natural language processing: The text data is analyzed using a natural language processing engine. Analysis items include points of change, phrasing, intonation, and speaker habits. The natural language processing engine uses OpenAI's GPT model.

[1635] 3. Feedback generation: Based on the analysis results, feedback is generated on points that need improvement and effective parts.

[1636] 4. Result input reception: Provides a means for users to input the results of a transaction, whether successful or unsuccessful, to the server.

[1637] These components provide specific feedback to users, helping them improve their customer service skills.

[1638] Program processing

[1639] The program of this system is composed of the terminal means and server means described above. The processing of the program will be explained below.

[1640] Audio recording and uploading

[1641] Users use their smartphones or tablets to record conversations during business meetings or customer service. Once recording is complete, the audio data is automatically uploaded to a server via the Internet. The device used is a voice recording library such as Expo Audio.

[1642] Speech recognition and text conversion

[1643] The server receives the uploaded voice data and converts it into text data using a speech recognition engine (Google Cloud Speech-to-Text or IBM Watson Speech to Text). This converted text data is then stored in a database such as MySQL or PostgreSQL.

[1644] Natural Language Processing and Analysis

[1645] The server analyzes the stored text data using a natural language processing engine (OpenAI's GPT model), which analyzes points of change, positive and negative phrases, tone of voice, and speaker habits.

[1646] Feedback generation and provision

[1647] The server generates feedback based on the analysis results that will be useful for the next customer service or business negotiation. This feedback is sent to the terminal means and displayed to the user.

[1648] As a concrete example, consider a scenario where a user is negotiating a product sales deal in a brick-and-mortar store. The audio of the negotiation is recorded on a terminal, and once the conversation is over, the recording is automatically uploaded to a server. After a few seconds, the server completes its analysis and generates feedback, which is displayed on the terminal, such as "Your tone was monotonous; you should have used more inflection" or "You should have emphasized the specific benefits."

[1649] Example prompt sentence:

[1650] Convert the audio data recorded by the user into text data and analyze the following items.

[1651] 1. Successful turning points and their phrases

[1652] 2. What phrases should users improve and why?

[1653] 3. Tone inflection and its effects

[1654] 4. Speaker habits and how to correct them

[1655] Text data:

[1656] "Dear customer, this product is very convenient and a great value."

[1657] Example output:

[1658] Successful pivot: "It's convenient and great value" -> This phrase leaves a good impression on customers

[1659] Wording to improve: "very" -> It would be more effective to use a concrete example (it would be better to say "This feature can be used to...")

[1660] Tone modulation: monotonous -> should be modulated

[1661] Speaker's habit: Often says "eh" -> This habit should be reduced

[1662] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1663] Step 1:

[1664] When a user starts serving a customer or negotiating a business, they start recording audio on their device (such as a smartphone or tablet). The device uses a voice recording library such as Expo Audio to collect audio data of the conversation. The recorded audio data is temporarily stored on the device.

[1665] Input: Voice of customer service or business negotiations

[1666] Output: Recorded audio data

[1667] Step 2:

[1668] When the user finishes recording, the terminal means automatically uploads the audio data to the server. The terminal means converts the audio data into a format (e.g., a WAV file) via the Internet and sends it to the server using an HTTP request.

[1669] Input: Recorded audio data

[1670] Output: Audio data file uploaded to the server

[1671] Step 3:

[1672] The server then passes the received voice data file through a speech recognition engine (such as Google Cloud Speech-to-Text or IBM Watson Speech to Text) to convert the voice data into text data. The speech recognition engine analyzes the voice file and generates the corresponding text.

[1673] Input: Audio data file uploaded to the server

[1674] Output: Text data

[1675] Step 4:

[1676] The server inputs the text data converted by the speech recognition engine into a natural language processing engine (OpenAI's GPT model) for analysis. Analysis items include turning points, phrasing, tone of voice, and speaker habits. The natural language processing engine evaluates these elements in the text and obtains analysis results.

[1677] Input: Text data

[1678] Output: Analysis results (changing points, phrasing, tone, speaker habits)

[1679] Step 5:

[1680] The server generates feedback based on the analysis results. Using a generative AI model, it creates feedback on specific areas for improvement and effective parts from the analysis results. The feedback includes specific advice that will be useful for the next customer service or business negotiation.

[1681] Input: Analysis results

[1682] Output: Generated feedback

[1683] Step 6:

[1684] The server sends the generated feedback to the terminal means, and uses an HTTP response to return the feedback data to the terminal, allowing the user to view it in real time.

[1685] Input: Generated feedback

[1686] Output: Feedback sent to the terminal

[1687] Step 7:

[1688] The terminal means displays the received feedback, and the user can check the feedback content on the terminal screen and use it for the next business negotiation or customer service.

[1689] Input: Feedback sent to the device

[1690] Output: Displayed feedback

[1691] The above is a detailed flow of the processing steps of this system.

[1692] 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.

[1693] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and user emotions when a deal is concluded or not. Specific embodiments for implementing the present invention will be described below.

[1694] System configuration

[1695] This system is mainly composed of "terminal means," "server means," and "emotion engine."

[1696] Terminal means:

[1697] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[1698] Server means:

[1699] This is the central system that processes and analyzes voice data. It converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal device.

[1700] Emotion Engine:

[1701] It is an engine for analyzing user emotions, analyzing the user's emotional state from voice and text data and reflecting the results in feedback.

[1702] Program processing

[1703] Recording and uploading audio

[1704] A user uses a terminal means to record the voice of a business negotiation or customer service. After the recording is completed, the voice data is temporarily saved and then uploaded to a server means.

[1705] Speech recognition and text conversion

[1706] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[1707] Text analysis and feedback generation

[1708] The server stores the converted text in a database and sends it to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrases, transitions, and tonal inflections.

[1709] Emotion analysis

[1710] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotions at that time based on the intonation, tempo, and choice of words of the user's voice.

[1711] Entering successful and unsuccessful deals and generating feedback

[1712] The user inputs into the server whether the outcome of the sales negotiation or customer service was a successful or unsuccessful transaction. The server generates feedback based on the successful / unsuccessful transaction data, as well as the results of text analysis and sentiment analysis. This feedback includes specific advice on areas for improvement and effective actions.

[1713] Providing feedback

[1714] The server transmits the generated feedback to the terminal means, and the terminal provides the feedback to the user, who can use it to improve their next business negotiation or customer service.

[1715] Specific examples

[1716] For example, a user is negotiating a sales transaction for a product and records the voice of the transaction on a terminal device. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to a server. The server uses a voice recognition engine to convert the voice data into text data. The converted text is something like, "Customer, this product is very convenient and a good deal."

[1717] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[1718] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "Your tone was monotonous and should have been more inflected," "You should have emphasized the specific benefits," or "Emotion analysis shows that the user is nervous about this part, so you should think of a way to help them relax."

[1719] The generated feedback is sent to the terminal device for the user to review, and the next time the salesperson makes a sales call, they can use this feedback to make improvements, such as adjusting the tone of voice or emphasizing specific benefits.

[1720] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[1721] The processing flow will be explained below.

[1722] Step 1:

[1723] The user uses the terminal means to record the voice of the customer service or business negotiation. When the user presses the recording start button, the terminal means starts capturing the voice using the microphone.

[1724] Step 2:

[1725] After the business meeting or customer service is over, the user presses the stop recording button. The recording stops and the device temporarily saves the audio data in local storage.

[1726] Step 3:

[1727] The device uploads the saved voice data to the server. Specifically, the device transfers the voice data to the server via the network. During the data transfer, the progress of the upload is monitored and a completion notification is received.

[1728] Step 4:

[1729] The server sends the received voice data to a speech recognition engine, which converts the voice into text. The speech recognition engine analyzes the voice file, extracts each spoken part, and converts it into text.

[1730] Step 5:

[1731] The server stores the converted text data in a database, where it is used for subsequent analysis.

[1732] Step 6:

[1733] The server sends the text data to a natural language processing engine for analysis, which involves parsing the text and identifying positive and negative phrasing, transitions, and tonal inflections.

[1734] Step 7:

[1735] The server sends the text and voice data to the emotion engine, which analyzes the user's emotions. The emotion engine determines the user's emotional state based on the intonation, tempo, and word choice of the voice.

[1736] Step 8:

[1737] The user inputs to the server whether the result of the business negotiation or customer service was a successful or unsuccessful transaction, which allows the server to collect user feedback information.

[1738] Step 9:

[1739] The server generates feedback based on data on successful and unsuccessful transactions, as well as the results of text and sentiment analysis, including specific advice on areas for improvement and what was effective.

[1740] Step 10:

[1741] The server sends the generated feedback to the terminal, and the feedback data is sent to the terminal so that the user can check it.

[1742] Step 11:

[1743] The device displays the received feedback to the user, who can then use it to identify and implement improvements for their next sales meeting or customer service.

[1744] Examples:

[1745] For example, a user is negotiating a sales transaction for a product and the voice of the transaction is recorded on the terminal means. When the transaction is over, the user presses a recording stop button and the voice data is uploaded to the server.

[1746] The server uses a speech recognition engine to convert the voice data into text, which reads, "Dear customer, this product is very convenient and a good value." The natural language processing engine then analyzes this text, examining how the change in phrases, tone, and intonation affected the speech.

[1747] At the same time, the server uses an emotion engine to analyze the user's emotions from the voice data and text data, which can detect, for example, that the user is nervous.

[1748] After that, when the user inputs into the server that the deal was not concluded, the server generates feedback based on the analysis results, such as "You should emphasize the specific benefits," "You should use a more modulated tone in this part," or "The user seemed nervous here, so you should think of a way to relax them."

[1749] The generated feedback is sent to the device for review by the user, who can then use it to make improvements during the next meeting, such as modulating the tone, emphasizing specific benefits, or trying relaxation techniques.

[1750] The present invention allows users to objectively evaluate their own presentation skills and identify specific areas for improvement that take into account their emotional state.

[1751] Example 2

[1752] 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."

[1753] In conventional business negotiations and customer service, it has been difficult to obtain specific and objective feedback using user voice data to improve the closing rate. In particular, there is a demand for a system that can accurately analyze the tone and intonation of voice data and the user's emotions, and provide feedback that reflects the results. However, there is a lack of technological means to solve this problem.

[1754] 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.

[1755] In this invention, the server includes a means for converting speech into text data, a means for analyzing the converted text data using a natural language processing engine, and a means for generating feedback based on the analysis results, thereby enabling the server to analyze the user's speech data and provide specific and objective feedback that takes into account tone, intonation, and emotion.

[1756] "Information processing device means for recording audio" refers to a device that has the function of allowing a user to record audio of business negotiations or customer service, and refers to a smartphone, tablet, PC, etc.

[1757] "Information processing device means for uploading recorded voice data from information processing device means to data processing device means" is a device that has the function of transmitting recorded voice data to a cloud or server via the Internet or the like.

[1758] The "data processing device means for converting uploaded voice data into text data" is a device having the function of analyzing received voice data using a voice recognition engine and converting it into text data.

[1759] The "data processing device means for analyzing the converted text data using a natural language processing engine" is a device that has the function of analyzing the text data using natural language processing technology and identifying phrasing, tone, and the like.

[1760] The "data processing device means for generating feedback based on the analysis results" is a device having a function for generating feedback including points for improvement for the user based on the results of natural language processing and sentiment analysis.

[1761] The "data processing device means for transmitting the generated feedback to the information processing device means" is a device having a function for transmitting the generated feedback to a device used by a user via the Internet or the like.

[1762] The "information processing device means for displaying feedback transmitted to the information processing device means" is a device having a function for displaying feedback transmitted from the server to the user.

[1763] The present invention is a system that analyzes voice data during detailed business negotiations and customer service, and provides feedback based on the analysis results to increase the user's success rate. A specific implementation example of this system is described below.

[1764] System configuration

[1765] The present invention mainly comprises the following elements:

[1766] 1. An information processing device for recording audio (smartphone, tablet, PC, etc.)

[1767] 2. Information processing device means for uploading the recorded voice data to a data processing device means (cloud server)

[1768] 3. Data processing means for converting uploaded voice data into text data (Google Cloud Speech-to-Text API)

[1769] 4. A data processing device means having a natural language processing engine for analyzing the converted text data (Google Cloud Natural Language API).

[1770] 5. Data processing device means for generating feedback based on the results of text analysis and sentiment analysis (IBM Watson Tone Analyzer)

[1771] 6. Data processing device means for transmitting the generated feedback to the information processing device means.

[1772] 7. Information processing device means for displaying feedback to the user (smartphone, tablet, PC, etc.)

[1773] Program processing

[1774] First, the user uses a terminal (smartphone, tablet, PC, etc.) to record the audio of a business meeting or customer service. Once the recording is complete, the audio data is saved in temporary storage on the terminal and then automatically uploaded to a server (cloud server).

[1775] The server receives the voice data uploaded from the device and sends it to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The speech recognition engine then processes the voice data, extracts each spoken part, and converts it into text data. The converted text data is stored in a database on the server.

[1776] The converted text data is then analyzed using a natural language processing engine (e.g., Google Cloud Natural Language API) to identify text syntax, positive and negative phrases, transitions, and tonal inflections.

[1777] The server then sends the text and voice data to an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. The emotion engine determines the user's emotions from the intonation, tempo, and word choice of the voice, and generates emotion data as the analysis result.

[1778] When a user enters the results of a sales meeting or customer service session into a dedicated application, whether the transaction was successful or unsuccessful, the results are immediately sent to the server. The server generates specific feedback based on the successful / unsuccessful transaction data and the text and sentiment analysis results obtained in the previous step. This feedback includes automatically generated advice and areas for improvement.

[1779] Finally, the server sends the generated feedback to a terminal device (smartphone, tablet, PC, etc.). The terminal device notifies the user in real time via an application that displays the feedback to the user. The user can check the received feedback and use it in their next business negotiation or customer service.

[1780] Specific examples

[1781] For example, suppose a user is negotiating a sales transaction for a product and has recorded the voice of the transaction on a terminal. When the transaction is over, the user presses the stop recording button, and the voice data is uploaded to the server. The server then uses a voice recognition engine to convert the voice data into text data.

[1782] The converted text should read something like this: "Dear customer, this product is very useful and a great value."

[1783] A natural language processing engine analyzes this text, examining how changes in phrases, tone, and inflection affect it, while an emotion engine analyzes the audio and text data to determine the user's emotional state.

[1784] Later, when the user inputs to the server that the deal was not closed, the server generates the following feedback based on the analysis results:

[1785] "The tone was monotonous and should have been a bit more inflected."

[1786] "We need to emphasize the specific benefits"

[1787] "Emotion analysis shows that the user is nervous about this part, so we should think about ways to relax them."

[1788] The generated feedback is sent to the terminal device and reviewed by the user. Based on this feedback, the user can make improvements during the next business meeting, such as adding inflection to the tone or emphasizing specific benefits.

[1789] Example of input prompt for generative AI model

[1790] "Generate effective feedback to close deals."

[1791] "Please create feedback based on the emotion analysis results of this audio data."

[1792] "Please provide advice on how to improve my phrasing and tone during business meetings."

[1793] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1794] Step 1: Record and upload your audio

[1795] A user uses a terminal means (smartphone, tablet, PC, etc.) to record the audio of business negotiations or customer service. The user starts the recording application and taps the record button to start recording. When recording is complete, the user taps the stop recording button to temporarily save the audio data. Next, the terminal automatically uploads the audio data to a data processing device means (server). The uploaded audio data is saved on the server.

[1796] Input: User recorded voice data

[1797] Output: Audio data stored on the server

[1798] Step 2: Speech recognition and text conversion

[1799] The server sends the received voice data to a speech recognition engine (for example, Google Cloud Speech-to-Text API). The server converts the voice data into text data using the speech recognition engine, and extracts the text for each utterance. This text data is stored in the server's database.

[1800] Input: Audio data

[1801] Output: Text data

[1802] Step 3: Text analysis

[1803] The server sends the stored text data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes the text syntactically, identifies positive and negative phrases, transitions, and tonal inflections, and generates analysis results, which are then stored in a database.

[1804] Input: Text data

[1805] Output: Text analysis results

[1806] Step 4: Sentiment Analysis

[1807] The server sends the text and voice data to an emotion analysis engine (for example, IBM Watson Tone Analyzer). The emotion analysis engine analyzes the user's emotions based on the intonation, tempo, and word choice of the voice, and generates emotion data. The emotion data is stored in the server's database.

[1808] Input: Text data, audio data

[1809] Output: Emotion analysis results

[1810] Step 5: Entering closed and open deals

[1811] The user inputs whether the outcome of the business negotiation or customer service is a successful or unsuccessful contract into the data processing device (server) via a dedicated application. The input result is sent to the server and stored in a database.

[1812] Input: Results of closed or open deals

[1813] Output: Save the results of successful and unsuccessful transactions to the database

[1814] Step 6: Feedback generation

[1815] The server generates feedback based on the data on successful and unsuccessful transactions, text analysis results, and sentiment analysis results. This feedback includes areas for improvement and effective advice. The generated feedback is stored in a database.

[1816] Input: Successful and unsuccessful transactions data, text analysis results, sentiment analysis results

[1817] Output: Feedback

[1818] Step 7: Provide feedback

[1819] The server sends the generated feedback to the terminal means. The terminal notifies the user in real time via an application that displays the received feedback to the user. The user can check the feedback and use it in their next business negotiation or customer service.

[1820] Input: Feedback

[1821] Output: Feedback displayed to the user

[1822] Through the above steps, the system can perform detailed analysis of speech data from business negotiations and customer service, and provide useful feedback to the user.

[1823] (Application example 2)

[1824] 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."

[1825] While conventional customer service systems can record and evaluate conversations with customers to improve sales associates' customer service skills, it is difficult to provide detailed feedback that takes into account specific phrases, tones, and even the emotional state of the customer. Furthermore, it is not possible to quickly provide specific and effective advice based on whether the customer service result is a successful or unsuccessful transaction. As a result, improvement in sales associate skills is delayed, and there are limitations to improving customer satisfaction.

[1826] 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.

[1827] In this invention, the server includes means for converting recorded voice data into text data, means for analyzing the converted text data using a natural language processing engine, means for generating feedback based on the analysis results, means for analyzing the emotional state of the user, means for analyzing the emotional state of the customer, and means for providing specific advice for improvement. This enables analysis of the phrases and tone used by store clerks when serving customers, as well as advanced feedback based on an understanding of the emotional state of the user or customer, thereby realizing the provision of specific measures for improving skills.

[1828] "Terminal means" refers to a device that allows a user to record audio and upload the recording to a server. Examples include a smartphone, tablet, or PC.

[1829] "Server means" refers to a central management system for processing and analyzing voice data, generating and sending feedback to the terminals.

[1830] "Audio data" refers to audio information recorded by a terminal means and stored in digital format.

[1831] "Text data" refers to character information generated from voice data by a voice recognition engine.

[1832] A "natural language processing engine" refers to software that analyzes text data and extracts and evaluates information such as specific phrasing, tone, and sentiment.

[1833] "Emotion analysis means" refers to software or an engine for determining the emotional state of users and customers from voice and text data.

[1834] "Feedback" refers to specific advice and information, including areas for improvement and success, generated based on the analysis results.

[1835] "Contract / Uncontract" refers to information indicating whether or not a customer service session or business negotiation resulted in a contract being concluded.

[1836] The present invention is a system that provides feedback to increase the success rate by recording voices during customer service and business negotiations and analyzing the points of response, phrases, tone of voice, intonation, and emotions of the user and customer when a deal is concluded or not. A specific embodiment of this system is described below.

[1837] System configuration

[1838] This system is mainly composed of "terminal means," "server means," and "emotion analysis means."

[1839] Terminal means:

[1840] This is a device used by the user. It can be a smartphone, tablet, PC, etc., and has the function of recording audio and uploading the recorded data to the server. It also displays the feedback received from the server.

[1841] Server means:

[1842] It is a central management system that processes and analyzes voice data. Specifically, it converts voice into text data, analyzes it using a natural language processing engine, and generates feedback that is sent to the terminal. Specific software used includes the Google Cloud Speech-to-Text API and the Google Cloud Natural Language API.

[1843] Emotion analysis means:

[1844] It is software or an engine for analyzing the emotions of users and customers. It analyzes the emotional state from voice and text data and reflects the results in feedback. Specific software used includes the Microsoft Azure Emotion API.

[1845] Program processing

[1846] The program of this system operates as follows.

[1847] 1. Record and upload your audio

[1848] The user uses a terminal to record the audio of business negotiations or customer service. After recording is complete, the audio data is temporarily saved and then uploaded to a cloud server. The specific hardware used includes a smartphone and a microphone.

[1849] 2. Speech Recognition and Text Conversion

[1850] The server then sends the received audio data to the Google Cloud Speech-to-Text API, which converts the audio into text data. This process converts the audio file into text information, which is then sent to the next analysis step.

[1851] 3. Text analysis and feedback generation

[1852] The server then sends the converted text data to the Google Cloud Natural Language API for analysis, which identifies the text's syntax, positive and negative phrasing, transitions, and tonal inflections.

[1853] 4. Emotion analysis

[1854] The server sends text and voice data to the Microsoft Azure Emotion API, which analyzes the emotions of users and customers. The emotion engine determines the user's current emotions based on the intonation, tempo, and choice of words of the user's voice.

[1855] 5. Entering successful and unsuccessful transactions and generating feedback

[1856] The user inputs into the server whether the result of the sales negotiation or customer service was a successful or unsuccessful deal. The server generates feedback based on the data on successful and unsuccessful deals and the analysis results. This feedback includes specific advice on areas for improvement and effective actions.

[1857] 6. Providing Feedback

[1858] The server sends the generated feedback to the terminal means, where the user can check it and use it for the next business negotiation or customer service.

[1859] Specific examples

[1860] For example, suppose a user is selling a new washing machine and the following conversation takes place:

[1861] Salesperson: "This washing machine is very energy efficient and a great value."

[1862] Customer: "Please tell me specifically how it saves energy."

[1863] Salesperson: "It uses 30% less electricity than previous models."

[1864] As an example of feedback generation, the following advice is provided:

[1865] It's more effective to be confident when you say "energy saving."

[1866] When customers ask for specific information, it is a good idea to provide detailed data (for example, a comparison of monthly electricity bills).

[1867] Optionally, add examples based on the customer's expected usage scenarios to generate customer interest.

[1868] Prompt Sentence Examples

[1869] I have some audio data from a customer service session. I'd like to extract the following information from this data:

[1870] 1. Customer emotional state (positive, negative, neutral)

[1871] 2. The emotional state of the store clerk

[1872] 3. Important phrases and tone changes

[1873] 4. Customer reaction (interested, indifferent, nervous)

[1874] Then, use this information to provide feedback on:

[1875] 1. Areas for improvement in the tone and phrasing of store staff

[1876] 2. Specific advice that is likely to lead to a sale

[1877] 3. Proposing solutions tailored to customer emotions

[1878] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1879] Step 1:

[1880] The user uses the terminal means to record the voice.

[1881] Input: Voice of customer service or business negotiations

[1882] How it works: A user launches the application on their smartphone and taps the record button to begin recording audio.

[1883] Output: Recorded audio data

[1884] Step 2:

[1885] After recording is complete, the user uploads the audio data to a cloud server.

[1886] Input: Audio data temporarily saved on the device

[1887] How it works: When the user taps the stop recording button, the audio data is automatically uploaded to the cloud server.

[1888] Output: Audio data stored on a cloud server

[1889] Step 3:

[1890] The server sends the received voice data to the Google Cloud Speech-to-Text API, which converts the voice data into text data.

[1891] Input: Audio data stored on a cloud server

[1892] How it works: The server sends the audio file to the Google Cloud Speech-to-Text API, which performs speech recognition processing.

[1893] Output: Recording converted to text data

[1894] Step 4:

[1895] The server sends the converted text data to the Google Cloud Natural Language API for analysis.

[1896] Input: Text data generated by speech recognition

[1897] How it works: The server sends text data to the Google Cloud Natural Language API, which performs natural language processing (NLP) on the data. Analysis includes identifying positive and negative phrases, transitions, and tonal inflections.

[1898] Output: Analysis results (specific phrases, tone inflections, emotional state, etc.)

[1899] Step 5:

[1900] The server sends text and voice data to the Microsoft Azure Emotion API to analyze user and customer emotions.

[1901] Input: Audio and text data

[1902] How it works: The server sends the data to the Microsoft Azure Emotion API, which performs the emotion analysis process.

[1903] Output: User and customer sentiment analysis results

[1904] Step 6:

[1905] The user inputs the results of the business negotiation or customer service (whether the deal was concluded or not) into the server.

[1906] Input: Results of negotiations and customer service (contracts / non-contracts)

[1907] How it works: The user uses an application on their smartphone to enter the results into the server.

[1908] Output: Results of successful and unsuccessful transactions saved on the server

[1909] Step 7:

[1910] The server generates feedback based on data on successful and unsuccessful transactions and analysis results.

[1911] Input: Analysis results and data on successful and unsuccessful transactions

[1912] How it works: The server compares your data with past data and generates specific advice, including areas for improvement and success.

[1913] Output: Feedback (areas for improvement, specific advice)

[1914] Step 8:

[1915] The server transmits the generated feedback to the terminal means.

[1916] Input: Generated feedback

[1917] How it works: The server sends the feedback to the user's smartphone.

[1918] Output: Feedback displayed on the user's smartphone

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] FIG. 9 is a diagram illustrating 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 actions 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.

[1924] 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.

[1925] 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).

[1926] 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.

[1927] 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."

[1928] 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.

[1929] 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).

[1930] 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.

[1931] 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.

[1932] 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.

[1933] 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.

[1934] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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 example of a 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.

[1935] 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.

[1936] 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.

[1937] 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.

[1938] 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.

[1939] 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.

[1940] The following is further disclosed regarding the above embodiment.

[1941] (Claim 1)

[1942] a terminal means for recording audio;

[1943] a terminal means for uploading the recorded voice data to a server;

[1944] A server means for converting the uploaded voice data into text data;

[1945] a server means for analyzing the converted text data using a natural language processing engine;

[1946] a server means for generating feedback based on the analysis results;

[1947] server means for transmitting the generated feedback to the terminal;

[1948] terminal means for displaying the feedback sent to the terminal;

[1949] A system including:

[1950] (Claim 2)

[1951] 2. The system according to claim 1, further comprising means for a user to input the result of a transaction, whether or not the transaction has been concluded, to the server.

[1952] (Claim 3)

[1953] 10. The system of claim 1, further comprising means for analyzing the tone and inflection of the audio data.

[1954] "Example 1"

[1955] (Claim 1)

[1956] a terminal means for recording audio;

[1957] a terminal means for uploading the recorded voice data to a server;

[1958] A server means for converting the uploaded voice data into text data;

[1959] a server means for analyzing the converted text data using a natural language processing engine;

[1960] A server means for generating feedback using a generative AI model based on the analysis result and the result of whether the transaction has been concluded or not;

[1961] server means for transmitting the generated feedback to the terminal;

[1962] terminal means for displaying the feedback sent to the terminal;

[1963] A system including:

[1964] (Claim 2)

[1965] 2. The system according to claim 1, further comprising means for a user to input the result of a transaction, whether or not the transaction has been concluded, to the server.

[1966] (Claim 3)

[1967] 10. The system of claim 1, further comprising means for analyzing the tone and inflection of the audio data.

[1968] "Application Example 1"

[1969] (Claim 1)

[1970] a terminal means for recording audio;

[1971] a terminal means for uploading the recorded voice data to a server;

[1972] A server means for converting the uploaded voice data into text data;

[1973] a server means for analyzing the converted text data using a natural language processing engine;

[1974] a server means for generating feedback based on the analysis results;

[1975] server means for transmitting the generated feedback to the terminal;

[1976] terminal means for displaying the feedback sent to the terminal;

[1977] A terminal means for automatically recording voice during customer service;

[1978] a terminal means for providing feedback in real time;

[1979] A system including:

[1980] (Claim 2)

[1981] 2. The system according to claim 1, further comprising means for a user to input the result of a transaction, whether or not the transaction has been concluded, to the server.

[1982] (Claim 3)

[1983] 10. The system of claim 1, further comprising means for analyzing the tone and inflection of the audio data.

[1984] "Example 2: Combining Emotion Engines"

[1985] (Claim 1)

[1986] an information processing device for recording audio;

[1987] an information processing device means for uploading recorded voice data from the information processing device means to the data processing device means;

[1988] a data processing device for converting the uploaded voice data into text data;

[1989] a data processing device that analyzes the converted text data using a natural language processing engine;

[1990] a data processing means for generating feedback based on the analysis results;

[1991] data processing means for transmitting the generated feedback to the information processing means;

[1992] information processing means for displaying the feedback transmitted to the information processing means;

[1993] A system including:

[1994] (Claim 2)

[1995] 2. The system according to claim 1, further comprising means for a user to input the result of a deal being concluded or not being concluded into the data processing means.

[1996] (Claim 3)

[1997] 10. The system of claim 1, further comprising means for analyzing the tone and inflection of the audio data.

[1998] "Application example 2 when combining emotion engines"

[1999] (Claim 1)

[2000] a terminal means for recording audio;

[2001] a terminal means for uploading the recorded voice data to a server;

[2002] A server means for converting the uploaded voice data into text data;

[2003] a server means for analyzing the converted text data using a natural language processing engine;

[2004] a server means for generating feedback based on the analysis results;

[2005] server means for transmitting the generated feedback to the terminal;

[2006] terminal means for displaying the feedback sent to the terminal;

[2007] emotion analysis means for analyzing the emotional state of a user;

[2008] emotion analysis means for analyzing the emotional state of a customer;

[2009] a means of providing specific advice for improvement;

[2010] A system including:

[2011] (Claim 2)

[2012] 2. The system according to claim 1, further comprising means for a user to input the result of a transaction, whether or not the transaction has been concluded, to the server.

[2013] (Claim 3)

[2014] 10. The system of claim 1, further comprising means for analyzing the tone and inflection of the audio data. [Explanation of symbols]

[2015] 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. a terminal means for recording audio; a terminal means for uploading the recorded voice data to a server; A server means for converting the uploaded voice data into text data; a server means for analyzing the converted text data using a natural language processing engine; a server means for generating feedback based on the analysis results; server means for transmitting the generated feedback to the terminal; terminal means for displaying the feedback sent to the terminal; A system including:

2. 2. The system according to claim 1, further comprising means for a user to input the result of a deal, whether or not the deal has been concluded, to the server.

3. 10. The system of claim 1, further comprising means for analyzing the tone and inflection of the audio data.

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