System

Abstract

A system is provided.SOLUTION: The system includes an input means for a user to perform voice input, a server means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for storing generated summary data, and a providing means for providing the data stored in the storage means to the user.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] Although reviews have a significant impact on store ratings and customer visits, it takes time and effort for users to post reviews, resulting in a low number of posts. Furthermore, handwritten reviews are subjective and prone to contain negative comments. Furthermore, providing feedback in real time is difficult. [Means for solving the problem]

[0005] The present invention provides a system that includes an input means for users to input voice data, a server means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, and a provision means for providing the saved data to users. This allows users to easily input reviews by voice, and the server automatically converts and summarizes the results into text, removing negative expressions and unwanted words, and emphasizing positive opinions. As a result, high-quality reviews are generated in real time, and other users can use them as reference.

[0006] "User" refers to an individual or consumer who uses the system to post reviews.

[0007] "Voice input" refers to the act of a user providing information to a system by voice.

[0008] "Input means" refers to a device or interface through which a user can provide voice input.

[0009] "Server means" refers to a computer server for receiving and processing voice data.

[0010] "Generation means" refers to software or algorithms for converting audio data into text data.

[0011] "Text data" refers to data in which information input by voice is expressed in text form.

[0012] "Processing means" refers to software or algorithms for summarizing and analyzing text data.

[0013] "Summary data" refers to abbreviated data that contains important information extracted from the original text data.

[0014] "Storage means" refers to a device or system for storing the generated summary data.

[0015] "Providing means" refers to the interface or system for providing the stored data to the user or other users.

[0016] "Negative language" refers to words or phrases in reviews that have negative connotations.

[0017] "NG words" refers to a list of words and phrases that are considered undesirable if included in a review.

[0018] "Positive feedback" refers to words or phrases in reviews that have a positive connotation. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[0041] System Overview

[0042] 1. The user scans the QR code

[0043] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The QR code contains a URL for a web page that offers voice input functionality.

[0044] 2. Provides a voice input interface

[0045] The terminal displays a voice input interface, which includes a voice input button, a start recording button, a stop recording button, and a send button.

[0046] 3. Record your thoughts

[0047] The user presses the "Start Recording" button and begins speaking their thoughts aloud, which the device records and stores locally.

[0048] 4. Send audio data

[0049] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[0050] 5. Convert audio data to text

[0051] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The server then retrieves the converted text data.

[0052] 6. Summarizing and Analyzing Text Data

[0053] The server analyzes the text data, extracts important information, filters out negative expressions and NG words, and restructures the text to emphasize positive opinions.

[0054] 7. Save to database

[0055] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[0056] 8. Final Review and Submission

[0057] The server sends the refined review data to the device, and the user confirms the final review content. By pressing the "Confirmation Complete" button, the review is officially posted and becomes available for other users to view.

[0058] Specific examples

[0059] 1. User scans the QR code:

[0060] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[0061] 2. The voice input page will appear:

[0062] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[0063] 3. User records their thoughts:

[0064] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[0065] 4. Stop recording and send:

[0066] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[0067] 5. The server converts the audio data into text:

[0068] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[0069] 6. Summarize and analyze text data:

[0070] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[0071] 7. Save to database:

[0072] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[0073] 8. User finalizes post:

[0074] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[0075] This system allows users to easily post high-quality reviews by voice input, enabling real-time feedback and providing useful information to other users.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The smartphone interprets the URL written on the QR code and displays a web page for voice input in the browser.

[0079] Step 2:

[0080] The terminal provides the user with a voice input interface, which includes a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the recorded data.

[0081] Step 3:

[0082] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[0083] Step 4:

[0084] The user presses the "Stop" button. The device stops recording and saves the recording locally as a temporary file.

[0085] Step 5:

[0086] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request.

[0087] Step 6:

[0088] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[0089] Step 7:

[0090] The server calls the generative AI model and converts the stored voice data into text data. The AI ​​model converts the voice data into text and returns the results to the server.

[0091] Step 8:

[0092] The server analyzes the generated text data, extracts important information from it, filters out negative expressions and NG words, and restructures it to emphasize positive opinions.

[0093] Step 9:

[0094] The server saves the summarized and refined review data in a database, adding meta information such as date and time, store ID, and user ID.

[0095] Step 10:

[0096] The server sends the refined review data back to the device, which displays a final confirmation interface for the user to review.

[0097] Step 11:

[0098] The user confirms the final review content and presses the "Confirm" button. The server updates the status of the submission completion, and the review data is made available to other users and store managers in real time.

[0099] Example 1

[0100] 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."

[0101] Current consumer review and feedback systems often require users to manually input text, which is time-consuming and laborious. Furthermore, the quality of the reviews entered varies, and they often contain negative or inappropriate content. There is a need for a system that can easily and efficiently generate high-quality reviews by addressing these issues.

[0102] 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.

[0103] In this invention, the server includes an access means that allows a user to access using a QR code, a display means that displays a voice input interface provided by the access means, and a filtering means that filters out negative expressions and NG words and emphasizes positive opinions when summarizing and analyzing the voice data. This allows users to easily access using the QR code and quickly provide reviews through voice input, and the generated reviews are of high quality and emphasize positive expressions, making it possible to provide useful information to other users.

[0104] A "user" is an individual or consumer who utilizes the system to provide voice input.

[0105] "Input means" refers to a device or interface that allows a user to input voice information. Examples include smartphones and tablets.

[0106] "Server Means" refers to a central system for receiving and processing voice data, including functions such as data storage, conversion, and analysis.

[0107] "Generation means" refers to a program or algorithm used to convert voice data into text data. For example, a generative AI model is used.

[0108] "Processing means" refers to programs and algorithms for summarizing and analyzing text data.

[0109] "Storage means" refers to a storage device or database for storing the generated summary data.

[0110] "Providing means" refers to the interface or function for providing stored data to users or other systems.

[0111] "Device" means a device used by a user to provide voice input. Examples include smartphones and tablets.

[0112] A "database" refers to a system that stores information in an organized manner and makes it searchable.

[0113] "Access Method" refers to the function or method by which a user can access the system using a QR code.

[0114] "Display means" refers to a function or device for visually presenting the voice input interface to the user.

[0115] "Filtering measures" refer to processes that eliminate negative expressions and inappropriate words and emphasize positive opinions.

[0116] The present invention relates to a system that allows users to easily generate and share high-quality word-of-mouth reviews through voice input. This system allows users to access the system using a QR code, input voice data, convert the voice data into text data, summarize and analyze the data, and finally provide organized word-of-mouth data. A detailed description of specific embodiments of this system is provided below.

[0117] First, we will explain the hardware and software configuration of the system. This system includes a device (e.g., a smartphone, tablet, etc.) for users to input voice data, a server that receives and processes the voice data, a generative AI model that converts the voice data into text data, an algorithm for summarizing and analyzing the text data, a database that stores the generated summary data, and an interface that provides the stored data to users.

[0118] The following process is an example of a specific operation.

[0119] First, the user scans a QR code installed in a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that provides voice input functionality. The smartphone's browser automatically opens the web page, and the voice input screen is displayed. At this stage, the user presses the "Start Recording" button and voice-records their impressions of the cafe or restaurant. The device records the audio using the built-in microphone and saves the recording locally.

[0120] Next, when the user presses the "Send" button, the device uploads the recorded voice data to the server. The server receives the voice data and stores it in the appropriate directory. The server then passes the voice data to the generative AI model, which converts the voice into text data. The generative AI model uses a prompt sentence, such as "Please convert this voice data into text data."

[0121] The converted text data is then retrieved by a server, which then analyzes it to extract important information, filtering out negative expressions and inappropriate words, and restructuring the text to emphasize positive opinions.

[0122] Once the text data has been summarized and filtered, the server stores the summarized review data in a database. The stored data also includes meta information such as the collection date, store ID, and user ID. The server then sends the refined review data back to the device, where the user can confirm the final review content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[0123] This system allows users to easily access the system using a QR code and quickly provide reviews through voice input. The generated reviews are of high quality and emphasize positive expressions, providing useful information for other users.

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

[0125] Step 1:

[0126] The user scans the QR code

[0127] A user scans a QR code displayed at a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that offers voice input functionality. The scanned URL is entered, and the smartphone's browser automatically opens the web page.

[0128] Input: QR code read by the camera

[0129] Output: Web page with voice input function

[0130] Step 2:

[0131] Show voice input interface

[0132] The terminal displays a voice input interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button, and the user uses this interface to input voice.

[0133] Input: The browser that opened the web page

[0134] Output: Audio input interface

[0135] Step 3:

[0136] Record your thoughts

[0137] The user presses the "Start Recording" button, and the device records audio using the built-in microphone. The user then enters their impressions of the restaurant or cafe by voice. Recording can be stopped by pressing the "Stop Recording" button.

[0138] Input: Press the start recording button, input voice

[0139] Output: Recorded audio data

[0140] Step 4:

[0141] Send audio data

[0142] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[0143] Input: Pressing the send button, recorded voice data

[0144] Output: Audio data stored on the server

[0145] Step 5:

[0146] Converting voice data to text

[0147] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The generative AI model uses a prompt saying, "Please convert this voice data into text data." The text data is returned from the generative AI model and retrieved by the server.

[0148] Input: Recorded voice data, prompt "Convert this voice data into text data"

[0149] Output: Converted text data

[0150] Step 6:

[0151] Summarize and analyze text data

[0152] The server analyzes the acquired text data, extracts important information, filters out negative expressions and inappropriate words, and emphasizes positive opinions.

[0153] Input: Converted text data

[0154] Output: Summarized and analyzed text data

[0155] Step 7:

[0156] Store in database

[0157] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[0158] Input: Summarized and refined text data

[0159] Output: Review data stored in the database

[0160] Step 8:

[0161] Final check and submission

[0162] The server sends the refined review data back to the device, where the user can confirm the content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[0163] Input: Summarized and refined text data, user confirmation

[0164] Output: Officially posted review data

[0165] (Application example 1)

[0166] 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."

[0167] Existing brick-and-mortar stores lack the means to efficiently collect and analyze customer feedback in a high-quality manner. Traditional surveys and feedback systems are time-consuming and tend to limit the amount of data collected. Furthermore, due to a lack of an easy-to-use interface for customers, the quality and quantity of feedback is often insufficient.

[0168] 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.

[0169] In this invention, the server includes an input means for users to input voice data, a means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server. This allows users to easily provide feedback by voice in physical stores, and makes it possible to efficiently collect, analyze, store, and provide high-quality word-of-mouth data.

[0170] "User" refers to an individual or organization that uses the system to provide voice input and feedback.

[0171] "Input means" refers to a device or interface that allows a user to input voice information.

[0172] "Server" refers to a computer system for receiving and processing audio data.

[0173] "Generation means" refers to software or algorithms for converting audio data into text data.

[0174] "Processing means" refers to software and algorithms for summarizing and analyzing text data.

[0175] "Storage means" refers to a database or storage system for storing the generated summary data.

[0176] "Presentation means" refers to the interface or means for presenting stored data to users or other systems.

[0177] "QR Code" refers to a visual code that encodes information for accessing a voice input interface.

[0178] "Communication means" refers to a network interface for exchanging voice data and processing results with the server.

[0179] "Generative AI model" refers to an artificial intelligence model used to convert voice data into text data.

[0180] "Summary data" refers to information that summarizes text data generated from audio data.

[0181] "Final confirmation means" refers to an interface that allows the user to confirm the summary data and confirm the posting.

[0182] A system embodying this invention includes an input means for a user to input voice data, a server means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for providing the stored data to the user, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server.

[0183] Basic system configuration

[0184] 1. Input means (terminal):

[0185] Users use devices such as smartphones and tablets to read QR codes installed in restaurants and cafes, which contain the URL of a web page that offers voice input functionality.

[0186] The terminal displays a voice input interface to prompt the user to input voice, and the interface includes a voice input button, a recording start button, a recording stop button, and a send button.

[0187] 2. Server means:

[0188] Receives audio data sent from the terminal and stores it in the appropriate directory.

[0189] The server passes the voice data to a generative AI model (e.g., OpenAI's Whisper or Deepgram), which converts the voice data into text data.

[0190] 3. Generation means:

[0191] Using a generative AI model running on the server, voice data is converted into text data with high accuracy.

[0192] Specific generative AI models include Whisper and Deepgram.

[0193] 4. Processing Method:

[0194] The server analyzes the text data and extracts important information. It then filters out negative expressions and unwanted words and restructures the text to emphasize positive opinions.

[0195] The server stores the analysis results as summary data.

[0196] 5. Preservation means:

[0197] The server stores the summarized and refined review data in a database, including meta-information such as the collection date, store ID, and user ID.

[0198] 6. Means of provision:

[0199] The server sends the refined review data back to the device for final confirmation and posting. Once the user confirms and presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[0200] Specific use cases

[0201] scenario

[0202] A user enters a cafe, enjoys their latte, and then scans the QR code on the table to post feedback.

[0203] 1. User scans the QR code:

[0204] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[0205] 2. The voice input page will appear:

[0206] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[0207] 3. User records their thoughts:

[0208] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[0209] 4. Stop recording and send:

[0210] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[0211] 5. The server converts the audio data into text:

[0212] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[0213] 6. Summarize and analyze text data:

[0214] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[0215] 7. Save to database:

[0216] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[0217] 8. User finalizes post:

[0218] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[0219] Examples of prompt statements

[0220] Example prompts to be input to the generative AI model:

[0221] Voice input: "The latte at this cafe was very good, but it took a while for it to be served."

[0222] Prompt for generative AI model: "Turn the following audio recording into text, highlighting the positive points and summarizing it. Please tone down the negative aspects.

[0223] Audio recording: {audio data}

[0224] """

[0225] Audio data: "The latte at this cafe was very tasty, but it took a while for it to be served after I ordered it."

[0226] """

[0227] Using this prompt, the audio data is converted into text and a summary is generated that highlights positive words.

[0228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0229] Step 1:

[0230] The user scans the QR code.

[0231] In this step, a user uses their smartphone camera to scan a QR code displayed in a physical store. The QR code contains the URL of a web page that provides voice input functionality, and scanning the code retrieves the URL. The input is the user scanning the QR code, and the output is the URL of the voice input page.

[0232] Step 2:

[0233] The device displays a voice input interface.

[0234] The device opens a browser using the URL obtained in step 1 and displays a voice input interface. This interface includes a voice input button, a start recording button, a stop recording button, and a send button. The input is the URL read from the QR code, and the output is the display of the voice input interface.

[0235] Step 3:

[0236] The user records their impressions.

[0237] The user presses the "Start Recording" button on the voice input interface and starts speaking. The device records the voice and stores it locally. The input is the user's voice, and the output is the recorded voice data.

[0238] Step 4:

[0239] The user transmits voice data.

[0240] When the user presses the "Stop" button and then the "Send" button, the device uploads the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server.

[0241] Step 5:

[0242] The server converts the audio data into text.

[0243] The server passes the received voice data to a generative AI model, which converts the voice data into text data. Examples of generative AI models used include OpenAI's Whisper and Deepgram. The input is voice data, and the output is generated text data.

[0244] Step 6:

[0245] The server summarizes and analyzes the text data.

[0246] The server analyzes the text data and extracts important information. The server filters out negative expressions and unwanted words and restructures the data to emphasize positive opinions. The input is the generated text data, and the output is the summarized and restructured text data.

[0247] Step 7:

[0248] The server stores the summarized data in a database.

[0249] The server stores the summarized and refined review data in a database. When saving, meta information such as date and time, store ID, and user ID is also added. The input is the summarized and reconstructed text data, and the output is the review data stored in the database.

[0250] Step 8:

[0251] The server sends the summary data to the terminal, and the user makes a final confirmation.

[0252] The server sends the refined review data back to the user's device, where the user performs a final confirmation. When the user presses the "Confirmation Complete" button, the review is officially posted. The input is the summarized and reconstructed review data and the user's confirmation operation, and the output is the officially posted review.

[0253] 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.

[0254] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion engine for recognizing the user's emotions, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[0255] System Overview

[0256] 1. Scan the QR code

[0257] The user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone's browser opens the URL and displays a web page designed specifically for voice input.

[0258] 2. Present voice input interface

[0259] The device provides the user with a recording interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button.

[0260] 3. Recording your impressions

[0261] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's thoughts and temporarily stores them locally.

[0262] 4. Sending audio data

[0263] When the user presses the "Send" button, the device sends the recorded audio data to the server via an HTTP POST request. The server receives the audio data.

[0264] 5. Convert audio data into text data

[0265] The server sends the received voice data to the generative AI model, which converts it into text data. The generative AI model extracts text information from the voice and generates text data.

[0266] 6. Sentiment Analysis of Text Data

[0267] The server passes the text data to the emotion engine to recognize the user's emotions. The emotion engine extracts positive and negative emotions from the text data and returns the emotion information to the server.

[0268] 7. Summarizing and Analyzing Text Data

[0269] The server extracts important information based on the text data and emotional information, filters out negative expressions and unwanted words, and reconstructs the data to emphasize positive opinions, creating summary data.

[0270] 8. Saving Summary Data

[0271] The server saves the generated summary data in a database, along with metadata such as the user ID, store ID, and date and time.

[0272] 9. Review and Final Submission

[0273] The server then sends the saved summary data back to the terminal and displays a final confirmation interface. The user confirms the content and presses the "Confirm" button to officially post the review. The saved data is made available for viewing by other users and store managers.

[0274] Specific examples

[0275] 1. User scans the QR code

[0276] Users scan a QR code placed on a cafe table with their smartphone, and a web page for voice input is displayed.

[0277] 2. The voice input page will appear

[0278] The device provides a voice input interface that displays "Start Recording," "Stop Recording," and "Send" buttons.

[0279] 3. User Records Their Thoughts

[0280] The user presses the "Start Recording" button and says, "The latte at this cafe was very tasty, but the order was a little late." The device records this comment and stores it locally.

[0281] 4. Stop recording and send

[0282] After the user presses the "Stop" button, he or she presses the "Send" button, and the terminal sends the recorded voice data to the server.

[0283] 5. The server converts the voice data into text

[0284] The server sends the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but the order was a little late."

[0285] 6. Emotional Recognition

[0286] The server passes the text data to an emotion engine, which recognizes the positive emotion "The latte was very tasty" and the negative emotion "The order was a little late."

[0287] 7. Summarizing and Analyzing Text Data

[0288] The server emphasizes the positive comments, summarizing them as "the latte is very tasty," and filters out the negative ones.

[0289] 8. Save to database

[0290] The server saves the summary data, "The latte is very tasty," in a database along with metadata such as the collection date, store ID, and user ID.

[0291] 9. User finalizes post

[0292] The server sends the summary data back to the terminal, and the user finally confirms the content. When the user presses the "Confirm" button, the review is officially posted and made public to other users and store managers.

[0293] This system allows users to easily post high-quality reviews, while at the same time highlighting positive feedback, providing extremely useful data for other users and stores.

[0294] The processing flow will be explained below.

[0295] Step 1:

[0296] A user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone interprets the URL written in the QR code and displays a web page for voice input in the browser.

[0297] Step 2:

[0298] The terminal provides the user with a voice input interface, which displays a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the voice data.

[0299] Step 3:

[0300] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[0301] Step 4:

[0302] The user presses the "Stop" button. The device stops recording and saves the recording locally. The audio data is saved as a temporary file on the device.

[0303] Step 5:

[0304] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request. The request includes metadata (e.g., store ID, user ID) along with the audio data.

[0305] Step 6:

[0306] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[0307] Step 7:

[0308] The server calls the generative AI model and converts the saved voice data into text data. The AI ​​model converts the voice data into text, and the server receives the converted text data.

[0309] Step 8:

[0310] The server passes the text data to the emotion engine to analyze the user's emotions. The emotion engine extracts positive and negative emotions from the text and returns the results to the server.

[0311] Step 9:

[0312] The server processes the text data based on the results from the emotion engine, filtering out negative expressions and unwanted words and restructuring the data to emphasize positive opinions.

[0313] Step 10:

[0314] The server saves the summarized and refined review data in a database, along with metadata such as date and time, store ID, and user ID.

[0315] Step 11:

[0316] The server sends the refined review data to the terminal, which displays a final confirmation interface so that the user can check the content.

[0317] Step 12:

[0318] The user confirms the final review content and presses the "Confirm" button. The server updates the submission status, and the new review data is made available to other users and store managers in real time.

[0319] This detailed processing flow allows users to easily post high-quality reviews and allows stores to receive useful feedback in real time.

[0320] Example 2

[0321] 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."

[0322] Conventional review posting systems require users to manually input text, which is time-consuming and has the problem of not fully reflecting emotional expressions. There is also a risk that negative or inappropriate language may be posted. This leads to issues such as a decrease in user convenience and the quality of posted data.

[0323] 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.

[0324] In this invention, the server includes a generating means for converting voice data into text data, an emotion recognizing means for performing emotion analysis on the text data, and a processing means for summarizing and analyzing the text data. This allows users to easily post reviews by voice, and also enables the generation of high-quality word-of-mouth data by emphasizing positive opinions while filtering out negative expressions and inappropriate words.

[0325] "Input means" refers to a device or interface that a user uses to make voice input.

[0326] "Server Means" refers to a central management system for receiving and processing voice and other data.

[0327] "Generation means" refers to a function or device for converting voice data into text data.

[0328] "Emotion recognition means" refers to a function or device for analyzing and detecting a user's emotions from text data.

[0329] "Processing means" refers to functions or devices for summarizing and analyzing text data.

[0330] "Storage means" refers to a function or device for storing the generated summary data and other related data.

[0331] "Providing means" refers to the functions and devices for providing stored data to users and other parties.

[0332] "Terminal" refers to a personal electronic device that a user uses to provide voice input.

[0333] "Database" refers to a system for centrally storing and managing summarized review data and other related information.

[0334] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion recognition means for recognizing a user's emotion, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described in detail below.

[0335] First, the user scans a QR code displayed at a restaurant or cafe with a smartphone or other device. When the scanned URL is accessed, a web page for voice input is displayed. This web page has buttons for "Start Recording," "Stop Recording," and "Send."

[0336] When the user presses the "Start Recording" button and begins speaking, the device records the user's voice and temporarily stores it locally. When the user finishes recording, they press the "Stop Recording" button, and then the "Send" button, and the device sends the voice data to the server.

[0337] The server sends the received voice data to a generative AI model (such as OpenAI's Whisper), which converts it into text data. The generated text data is then passed to an emotion recognition means (such as IBM Watson), where sentiment analysis is performed to identify positive and negative emotions. After receiving the results of the sentiment analysis, the server uses a processing means to summarize and analyze the text data, extracting important information and filtering out negative expressions and NG words. A reconstructed summary is generated that emphasizes positive opinions.

[0338] The generated summary data is stored in a database by the server. This data is also saved along with metadata such as the user ID, store ID, and collection date. The server then sends the summary data back to the terminal and displays a confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted. The saved data is made available for viewing by other users and store managers.

[0339] Specific examples

[0340] When a user visits the cafe and scans a QR code on the table with their smartphone, a web page for voice input is automatically displayed. When the user presses the "Start Recording" button and says something like, "The latte at this cafe was very tasty, but the order was a little late," the device begins recording the voice. When the user finishes recording and presses the "Send" button, the device sends the recorded data to the server.

[0341] The server converts the received voice data into text data using a generative AI model such as OpenAI's Whisper, and then analyzes the generated text data using an emotion engine such as IBM Watson, detecting positive emotions such as "The latte was very delicious" and negative emotions such as "The order was a little late." Based on this, the server generates a summary such as "The latte was very delicious" and stores it in a database.

[0342] The server sends the summary data to the terminal for the user to make a final confirmation, and when the user presses the "Confirmation Complete" button, the review is officially posted.

[0343] Prompt Sentence Examples

[0344] Example prompt sentence:

[0345] "Transcribe the following line and identify the sentiment: 'The latte at this cafe was very tasty, but the order was a little slow.'"

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1:

[0348] The user scans a QR code installed in a cafe or restaurant with their smartphone.

[0349] Input: QR code, smartphone

[0350] Output: URL of the web page for voice input

[0351] How it works: The user reads the QR code using a QR code reader app or the smartphone's built-in camera. The smartphone's browser automatically accesses the URL that was read and displays a web page for voice input.

[0352] Step 2:

[0353] The device displays a voice input interface.

[0354] Input: URL of the web page for voice input

[0355] Output: Audio input interface ("Start Recording", "Stop Recording", "Send" buttons)

[0356] What it does: The browser accesses the specified URL and displays the recording interface to the user, with "Start Recording," "Stop Recording," and "Send" buttons.

[0357] Step 3:

[0358] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's voice and temporarily stores it locally.

[0359] Input: User's voice, start recording operation

[0360] Output: Recorded audio data

[0361] Specific operation: When the user presses the "Start Recording" button, the device starts recording and captures the user's voice. The audio data is temporarily stored in the device's local memory until the recording is finished.

[0362] Step 4:

[0363] The user presses the "Stop Recording" button and then the "Send" button. The device sends the recorded audio data to the server via an HTTP POST request.

[0364] Input: Recorded voice data, sending operation

[0365] Output: Audio data sent to the server

[0366] Specific operation: When the user presses the "Stop Recording" button and then the "Send" button, the device creates an HTTP POST request and sends the recorded audio data to the server. The server receives this request.

[0367] Step 5:

[0368] The server sends the received voice data to a generative AI model, which converts it into text data.

[0369] Input: Audio data

[0370] Output: Text data

[0371] Specific operation: The server passes the voice data to the generative AI model, which then performs speech recognition and generates text data. For example, it uses OpenAI's Whisper to extract text information from the voice.

[0372] Step 6:

[0373] The server passes the text data to an emotion recognition means to recognize the user's emotion.

[0374] Input: Text data

[0375] Output: Emotional information (positive, negative)

[0376] Specific operation: The server sends the text data to an emotion recognition unit (e.g., IBM Watson) for emotion analysis. The emotion recognition unit extracts positive and negative emotions from the text data and returns the emotion information to the server.

[0377] Step 7:

[0378] The server extracts important information based on text data and emotional information, and filters out negative expressions and prohibited words.

[0379] Input: Text data, emotion information

[0380] Output: Summary data

[0381] Specific operation: The server considers emotional information, restructures the text to emphasize positive opinions, filters out negative expressions and NG words, and generates summary data.

[0382] Step 8:

[0383] The server stores the generated summary data in a database.

[0384] Input: Summary data, metadata (user ID, store ID, date and time, etc.)

[0385] Output: Saved summary data

[0386] Specific operation: When the server saves the generated summary data in the database, it also saves metadata such as user ID, store ID, and collection date, which makes the data unique and enables various searches.

[0387] Step 9:

[0388] The server sends the summary data back to the terminal and displays a final confirmation interface. The user checks the content and presses the "Confirm" button to officially post the review.

[0389] Input: Summary data

[0390] output: Final confirmation interface, officially posted review data

[0391] Specific operation: The server sends the data back to the device and displays a final confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted and made available in a saved location for other users and store managers to view.

[0392] (Application example 2)

[0393] 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."

[0394] With conventional voice input systems, when users provide feedback via voice, the content is not properly converted into text data, and sentiment analysis and summarization are not performed, resulting in problems with the content of the feedback not being accurately reflected. Another problem is that feedback containing negative expressions or prohibited words is made public as is. Furthermore, since there is no interface for users to perform a final review, the quality of the feedback cannot be guaranteed, and there are also issues with it not being able to be used as useful data for other users and store managers.

[0395] 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.

[0396] In this invention, the server includes a terminal means for a user to input voice data, a means for providing an interface for the user to input voice data, a server means for receiving the voice data, a generation means for converting the voice data into text data, an emotion identification means for identifying emotions based on the text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a provision means for providing the data stored in the storage means to the user, and a means for the provision means to provide an interface for the user to finalize the summary data. This allows user feedback to be accurately and quickly converted into text data and subjected to emotion analysis and summarization, thereby filtering out negative expressions and unwanted words and emphasizing positive opinions. Furthermore, providing an interface for the user to finalize the feedback ensures the quality of the feedback and allows it to be used as useful data for other users and store managers.

[0397] The "terminal for user voice input" refers to a device used by a user to input their own voice, and refers to a general mobile communication terminal such as a smartphone or tablet.

[0398] "Means for providing an interface" refers to a mechanism that provides the user with an operation screen and functions for voice input, and refers to a screen that includes buttons for starting and stopping recording, sending, etc.

[0399] The "server means for receiving voice data" is a server device for receiving voice data sent from a user via a network.

[0400] "Generative means for converting voice data into text data" refers to a mechanism for analyzing voice data and converting it into corresponding text data, including voice recognition software and generative AI models.

[0401] The "emotion identification means for identifying emotions based on text data" is a mechanism for identifying a user's emotions from the content of text data, and refers to an emotion analysis engine that uses natural language processing.

[0402] The "processing means for summarizing and analyzing text data" is a mechanism for extracting important information from text data and summarizing the content, and uses a natural language processing algorithm.

[0403] The "storage means for saving the summary data" is a mechanism for continuously storing the generated summary data, and includes databases and cloud storage.

[0404] The "means for providing the user with data stored in the storage means" refers to a mechanism that allows the user to view or use the stored data, and refers to a device that provides a viewing interface and notification functions.

[0405] "Means for providing an interface for users to make final confirmation of summary data" refers to a mechanism that provides an operation screen or function that allows users to make final confirmation of the generated summary data and approve or correct it.

[0406] This system allows users to easily input voice data, accurately converts the data into text, analyzes and summarizes emotions, and stores and provides them. This system includes a mobile communication device such as a smartphone or tablet, a server, a database, a generative AI model, and a sentiment analysis engine.

[0407] The user first uses the device to input voice data. Smartphones and tablets are suitable for this purpose. For example, the user can scan a QR code placed on a table in a store to view an interface dedicated to voice input. This interface provides a screen with buttons to start recording, stop recording, and send.

[0408] When a user records their voice feedback, the voice data is temporarily stored on the device. After recording is complete, the user presses the send button and the voice data is sent to the server via an HTTP POST request. The server receives the voice data and converts it into text using a generative AI model. This generative AI model can be, for example, the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[0409] The generated text data is passed to an emotion identification unit, which performs emotion analysis using natural language processing engines such as IBM Watson or Microsoft Azure Text Analytics, to identify positive and negative emotions.

[0410] The text data is then summarized and analyzed. Natural language processing algorithms are used to highlight positive opinions and filter out negative expressions and unwanted words. Important information is extracted and summary data is generated. This summary data is then stored in a database, which can be a storage medium. Databases such as Amazon RDS or MySQL can be used.

[0411] The saved summary data is provided to the user via the providing means. The user uses a smartphone or tablet to display an interface for final confirmation of the summary data. The interface is designed so that the user can confirm the content of the summary data again and press the "Confirmation Complete" button to officially post the review.

[0412] As a concrete example, a user scans a QR code on a table in a cafe with their smartphone and provides verbal feedback such as, "The food was very good, but the service was a little slow." This feedback is converted into text data by inputting the following prompt sentence into a generative AI model:

[0413] Audio data: 'The food was very good, but the service was a little slow.'

[0414] Convert it into text data.

[0415] This allows for accurate conversion of user feedback, rapid sentiment analysis, and summarization, and the system can be used as useful data for other users and store managers.

[0416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0417] Step 1:

[0418] The user uses a smartphone or tablet to scan a QR code displayed on a table in the store, which opens a web page for voice input.

[0419] Input: QR code

[0420] Output: A web page dedicated to voice input is displayed.

[0421] What it does: When you use your smartphone camera to read a QR code, your browser opens the URL embedded in the QR code, and the web page pointed to by that URL is displayed on the screen.

[0422] Step 2:

[0423] The terminal provides the user with an interface for voice input, which includes a recording start button, a recording stop button, and a send button.

[0424] Input: Load web page

[0425] Output: Audio input interface

[0426] Specific behavior: A web page displays a voice input UI composed of HTML / CSS / JavaScript on the screen.

[0427] Step 3:

[0428] The user presses the start recording button to record the voice feedback, and when the recording is complete, presses the stop recording button to temporarily save the voice data.

[0429] Input: User voice input

[0430] Output: Temporarily saved audio data

[0431] Specific operation: When voice input begins, the smartphone's microphone captures the voice data and temporarily stores it in local storage.

[0432] Step 4:

[0433] When the user presses the send button, the terminal sends the audio data to the server using an HTTP POST request.

[0434] Input: Stored audio data

[0435] Output: Sending audio data to the server

[0436] Specific operation: Uses the HTTP library to send audio data to the specified URL on the server.

[0437] Step 5:

[0438] The server sends the received voice data to a generative AI model and converts it into text data.

[0439] Input: Audio data

[0440] Output: Text data

[0441] How it works: The server sends a request to the generative AI model via a REST API to generate text data from the audio data, using the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[0442] Step 6:

[0443] The server passes the generated text data to an emotion identification means for emotion analysis.

[0444] Input: Text data

[0445] Output: Emotional information

[0446] Specific operation: Text data is passed to a natural language processing engine (such as IBM Watson or Microsoft Azure Text Analytics) to extract sentiment information such as positive or negative.

[0447] Step 7:

[0448] The server summarizes and analyzes the text data and emotional information, extracts important information, filters out negative expressions and unwanted words, and creates summary data that emphasizes positive opinions.

[0449] Input: Text data, emotion information

[0450] Output: Summary data

[0451] What it does: It uses natural language processing algorithms to extract important information from text data and highlight and reframe positive opinions.

[0452] Step 8:

[0453] The generated summary data is saved in a database, along with metadata such as the user ID, store ID, and date and time.

[0454] Input: Abstract data, metadata

[0455] Output: Summary data stored in a database

[0456] What it does: It saves summary data and metadata to databases such as MySQL or Amazon RDS using INSERT queries.

[0457] Step 9:

[0458] The server transmits the saved summary data to the terminal and provides an interface for the user to make a final confirmation.

[0459] Input: Summary data retrieved from the database

[0460] Output: Interface for final confirmation

[0461] Specific operation: The saved summary data is sent to the terminal as an HTTP response and displayed in the summary display area of ​​the web page.

[0462] Step 10:

[0463] If the user makes a final check and there are no problems with the content, the review will be officially posted by pressing the "Confirm" button.

[0464] Input: User confirmation operation

[0465] Output: Review officially posted

[0466] Specific operation: Upon user action, the final confirmation completion information is sent to the server and finally made public to other users and store managers.

[0467] This process allows user feedback to be processed quickly and accurately, and summary data that reflects appropriate emotions is generated.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] [Second embodiment]

[0472] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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).

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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."

[0484] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[0485] System Overview

[0486] 1. The user scans the QR code

[0487] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The QR code contains a URL for a web page that offers voice input functionality.

[0488] 2. Provides a voice input interface

[0489] The terminal displays a voice input interface, which includes a voice input button, a start recording button, a stop recording button, and a send button.

[0490] 3. Record your thoughts

[0491] The user presses the "Start Recording" button and begins speaking their thoughts aloud, which the device records and stores locally.

[0492] 4. Send audio data

[0493] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[0494] 5. Convert audio data to text

[0495] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The server then retrieves the converted text data.

[0496] 6. Summarizing and Analyzing Text Data

[0497] The server analyzes the text data, extracts important information, filters out negative expressions and NG words, and restructures the text to emphasize positive opinions.

[0498] 7. Save to database

[0499] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[0500] 8. Final Review and Submission

[0501] The server sends the refined review data to the device, and the user confirms the final review content. By pressing the "Confirmation Complete" button, the review is officially posted and becomes available for other users to view.

[0502] Specific examples

[0503] 1. User scans the QR code:

[0504] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[0505] 2. The voice input page will appear:

[0506] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[0507] 3. User records their thoughts:

[0508] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[0509] 4. Stop recording and send:

[0510] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[0511] 5. The server converts the audio data into text:

[0512] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[0513] 6. Summarize and analyze text data:

[0514] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[0515] 7. Save to database:

[0516] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[0517] 8. User finalizes post:

[0518] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[0519] This system allows users to easily post high-quality reviews by voice input, enabling real-time feedback and providing useful information to other users.

[0520] The processing flow will be explained below.

[0521] Step 1:

[0522] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The smartphone interprets the URL written on the QR code and displays a web page for voice input in the browser.

[0523] Step 2:

[0524] The terminal provides the user with a voice input interface, which includes a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the recorded data.

[0525] Step 3:

[0526] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[0527] Step 4:

[0528] The user presses the "Stop" button. The device stops recording and saves the recording locally as a temporary file.

[0529] Step 5:

[0530] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request.

[0531] Step 6:

[0532] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[0533] Step 7:

[0534] The server calls the generative AI model and converts the stored voice data into text data. The AI ​​model converts the voice data into text and returns the results to the server.

[0535] Step 8:

[0536] The server analyzes the generated text data, extracts important information from it, filters out negative expressions and NG words, and restructures it to emphasize positive opinions.

[0537] Step 9:

[0538] The server saves the summarized and refined review data in a database, adding meta information such as date and time, store ID, and user ID.

[0539] Step 10:

[0540] The server sends the refined review data back to the device, which displays a final confirmation interface for the user to review.

[0541] Step 11:

[0542] The user confirms the final review content and presses the "Confirm" button. The server updates the status of the submission completion, and the review data is made available to other users and store managers in real time.

[0543] Example 1

[0544] 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."

[0545] Current consumer review and feedback systems often require users to manually input text, which is time-consuming and laborious. Furthermore, the quality of the reviews entered varies, and they often contain negative or inappropriate content. There is a need for a system that can easily and efficiently generate high-quality reviews by addressing these issues.

[0546] 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.

[0547] In this invention, the server includes an access means that allows a user to access using a QR code, a display means that displays a voice input interface provided by the access means, and a filtering means that filters out negative expressions and NG words and emphasizes positive opinions when summarizing and analyzing the voice data. This allows users to easily access using the QR code and quickly provide reviews through voice input, and the generated reviews are of high quality and emphasize positive expressions, making it possible to provide useful information to other users.

[0548] A "user" is an individual or consumer who utilizes the system to provide voice input.

[0549] "Input means" refers to a device or interface that allows a user to input voice information. Examples include smartphones and tablets.

[0550] "Server Means" refers to a central system for receiving and processing voice data, including functions such as data storage, conversion, and analysis.

[0551] "Generation means" refers to a program or algorithm used to convert voice data into text data. For example, a generative AI model is used.

[0552] "Processing means" refers to programs and algorithms for summarizing and analyzing text data.

[0553] "Storage means" refers to a storage device or database for storing the generated summary data.

[0554] "Providing means" refers to the interface or function for providing stored data to users or other systems.

[0555] "Device" means a device used by a user to provide voice input. Examples include smartphones and tablets.

[0556] A "database" refers to a system that stores information in an organized manner and makes it searchable.

[0557] "Access Method" refers to the function or method by which a user can access the system using a QR code.

[0558] "Display means" refers to a function or device for visually presenting the voice input interface to the user.

[0559] "Filtering measures" refer to processes that eliminate negative expressions and inappropriate words and emphasize positive opinions.

[0560] The present invention relates to a system that allows users to easily generate and share high-quality word-of-mouth reviews through voice input. This system allows users to access the system using a QR code, input voice data, convert the voice data into text data, summarize and analyze the data, and finally provide organized word-of-mouth data. A detailed description of specific embodiments of this system is provided below.

[0561] First, we will explain the hardware and software configuration of the system. This system includes a device (e.g., a smartphone, tablet, etc.) for users to input voice data, a server that receives and processes the voice data, a generative AI model that converts the voice data into text data, an algorithm for summarizing and analyzing the text data, a database that stores the generated summary data, and an interface that provides the stored data to users.

[0562] The following process is an example of a specific operation.

[0563] First, the user scans a QR code installed in a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that provides voice input functionality. The smartphone's browser automatically opens the web page, and the voice input screen is displayed. At this stage, the user presses the "Start Recording" button and voice-records their impressions of the cafe or restaurant. The device records the audio using the built-in microphone and saves the recording locally.

[0564] Next, when the user presses the "Send" button, the device uploads the recorded voice data to the server. The server receives the voice data and stores it in the appropriate directory. The server then passes the voice data to the generative AI model, which converts the voice into text data. The generative AI model uses a prompt sentence, such as "Please convert this voice data into text data."

[0565] The converted text data is then retrieved by a server, which then analyzes it to extract important information, filtering out negative expressions and inappropriate words, and restructuring the text to emphasize positive opinions.

[0566] Once the text data has been summarized and filtered, the server stores the summarized review data in a database. The stored data also includes meta information such as the collection date, store ID, and user ID. The server then sends the refined review data back to the device, where the user can confirm the final review content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[0567] This system allows users to easily access the system using a QR code and quickly provide reviews through voice input. The generated reviews are of high quality and emphasize positive expressions, providing useful information for other users.

[0568] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0569] Step 1:

[0570] The user scans the QR code

[0571] A user scans a QR code displayed at a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that offers voice input functionality. The scanned URL is entered, and the smartphone's browser automatically opens the web page.

[0572] Input: QR code read by the camera

[0573] Output: Web page with voice input function

[0574] Step 2:

[0575] Show voice input interface

[0576] The terminal displays a voice input interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button, and the user uses this interface to input voice.

[0577] Input: The browser that opened the web page

[0578] Output: Audio input interface

[0579] Step 3:

[0580] Record your thoughts

[0581] The user presses the "Start Recording" button, and the device records audio using the built-in microphone. The user then enters their impressions of the restaurant or cafe by voice. Recording can be stopped by pressing the "Stop Recording" button.

[0582] Input: Press the start recording button, input voice

[0583] Output: Recorded audio data

[0584] Step 4:

[0585] Send audio data

[0586] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[0587] Input: Pressing the send button, recorded voice data

[0588] Output: Audio data stored on the server

[0589] Step 5:

[0590] Converting voice data to text

[0591] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The generative AI model uses a prompt saying, "Please convert this voice data into text data." The text data is returned from the generative AI model and retrieved by the server.

[0592] Input: Recorded voice data, prompt "Convert this voice data into text data"

[0593] Output: Converted text data

[0594] Step 6:

[0595] Summarize and analyze text data

[0596] The server analyzes the acquired text data, extracts important information, filters out negative expressions and inappropriate words, and emphasizes positive opinions.

[0597] Input: Converted text data

[0598] Output: Summarized and analyzed text data

[0599] Step 7:

[0600] Store in database

[0601] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[0602] Input: Summarized and refined text data

[0603] Output: Review data stored in the database

[0604] Step 8:

[0605] Final check and submission

[0606] The server sends the refined review data back to the device, where the user can confirm the content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[0607] Input: Summarized and refined text data, user confirmation

[0608] Output: Officially posted review data

[0609] (Application example 1)

[0610] 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."

[0611] Existing brick-and-mortar stores lack the means to efficiently collect and analyze customer feedback in a high-quality manner. Traditional surveys and feedback systems are time-consuming and tend to limit the amount of data collected. Furthermore, due to a lack of an easy-to-use interface for customers, the quality and quantity of feedback is often insufficient.

[0612] 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.

[0613] In this invention, the server includes an input means for users to input voice data, a means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server. This allows users to easily provide feedback by voice in physical stores, and makes it possible to efficiently collect, analyze, store, and provide high-quality word-of-mouth data.

[0614] "User" refers to an individual or organization that uses the system to provide voice input and feedback.

[0615] "Input means" refers to a device or interface that allows a user to input voice information.

[0616] "Server" refers to a computer system for receiving and processing audio data.

[0617] "Generation means" refers to software or algorithms for converting audio data into text data.

[0618] "Processing means" refers to software and algorithms for summarizing and analyzing text data.

[0619] "Storage means" refers to a database or storage system for storing the generated summary data.

[0620] "Presentation means" refers to the interface or means for presenting stored data to users or other systems.

[0621] "QR Code" refers to a visual code that encodes information for accessing a voice input interface.

[0622] "Communication means" refers to a network interface for exchanging voice data and processing results with the server.

[0623] "Generative AI model" refers to an artificial intelligence model used to convert voice data into text data.

[0624] "Summary data" refers to information that summarizes text data generated from audio data.

[0625] "Final confirmation means" refers to an interface that allows the user to confirm the summary data and confirm the posting.

[0626] A system embodying this invention includes an input means for a user to input voice data, a server means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for providing the stored data to the user, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server.

[0627] Basic system configuration

[0628] 1. Input means (terminal):

[0629] Users use devices such as smartphones and tablets to read QR codes installed in restaurants and cafes, which contain the URL of a web page that offers voice input functionality.

[0630] The terminal displays a voice input interface to prompt the user to input voice, and the interface includes a voice input button, a recording start button, a recording stop button, and a send button.

[0631] 2. Server means:

[0632] Receives audio data sent from the terminal and stores it in the appropriate directory.

[0633] The server passes the voice data to a generative AI model (e.g., OpenAI's Whisper or Deepgram), which converts the voice data into text data.

[0634] 3. Generation means:

[0635] Using a generative AI model running on the server, voice data is converted into text data with high accuracy.

[0636] Specific generative AI models include Whisper and Deepgram.

[0637] 4. Processing Method:

[0638] The server analyzes the text data and extracts important information. It then filters out negative expressions and unwanted words and restructures the text to emphasize positive opinions.

[0639] The server stores the analysis results as summary data.

[0640] 5. Preservation means:

[0641] The server stores the summarized and refined review data in a database, including meta-information such as the collection date, store ID, and user ID.

[0642] 6. Means of provision:

[0643] The server sends the refined review data back to the device for final confirmation and posting. Once the user confirms and presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[0644] Specific use cases

[0645] scenario

[0646] A user enters a cafe, enjoys their latte, and then scans the QR code on the table to post feedback.

[0647] 1. User scans the QR code:

[0648] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[0649] 2. The voice input page will appear:

[0650] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[0651] 3. User records their thoughts:

[0652] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[0653] 4. Stop recording and send:

[0654] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[0655] 5. The server converts the audio data into text:

[0656] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[0657] 6. Summarize and analyze text data:

[0658] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[0659] 7. Save to database:

[0660] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[0661] 8. User finalizes post:

[0662] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[0663] Examples of prompt statements

[0664] Example prompts to be input to the generative AI model:

[0665] Voice input: "The latte at this cafe was very good, but it took a while for it to be served."

[0666] Prompt for generative AI model: "Turn the following audio recording into text, highlighting the positive points and summarizing it. Please tone down the negative aspects.

[0667] Audio recording: {audio data}

[0668] """

[0669] Audio data: "The latte at this cafe was very tasty, but it took a while for it to be served after I ordered it."

[0670] """

[0671] Using this prompt, the audio data is converted into text and a summary is generated that highlights positive words.

[0672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0673] Step 1:

[0674] The user scans the QR code.

[0675] In this step, a user uses their smartphone camera to scan a QR code displayed in a physical store. The QR code contains the URL of a web page that provides voice input functionality, and scanning the code retrieves the URL. The input is the user scanning the QR code, and the output is the URL of the voice input page.

[0676] Step 2:

[0677] The device displays a voice input interface.

[0678] The device opens a browser using the URL obtained in step 1 and displays a voice input interface. This interface includes a voice input button, a start recording button, a stop recording button, and a send button. The input is the URL read from the QR code, and the output is the display of the voice input interface.

[0679] Step 3:

[0680] The user records their impressions.

[0681] The user presses the "Start Recording" button on the voice input interface and starts speaking. The device records the voice and stores it locally. The input is the user's voice, and the output is the recorded voice data.

[0682] Step 4:

[0683] The user transmits voice data.

[0684] When the user presses the "Stop" button and then the "Send" button, the device uploads the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server.

[0685] Step 5:

[0686] The server converts the audio data into text.

[0687] The server passes the received voice data to a generative AI model, which converts the voice data into text data. Examples of generative AI models used include OpenAI's Whisper and Deepgram. The input is voice data, and the output is generated text data.

[0688] Step 6:

[0689] The server summarizes and analyzes the text data.

[0690] The server analyzes the text data and extracts important information. The server filters out negative expressions and unwanted words and restructures the data to emphasize positive opinions. The input is the generated text data, and the output is the summarized and restructured text data.

[0691] Step 7:

[0692] The server stores the summarized data in a database.

[0693] The server stores the summarized and refined review data in a database. When saving, meta information such as date and time, store ID, and user ID is also added. The input is the summarized and reconstructed text data, and the output is the review data stored in the database.

[0694] Step 8:

[0695] The server sends the summary data to the terminal, and the user makes a final confirmation.

[0696] The server sends the refined review data back to the user's device, where the user performs a final confirmation. When the user presses the "Confirmation Complete" button, the review is officially posted. The input is the summarized and reconstructed review data and the user's confirmation operation, and the output is the officially posted review.

[0697] 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.

[0698] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion engine for recognizing the user's emotions, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[0699] System Overview

[0700] 1. Scan the QR code

[0701] The user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone's browser opens the URL and displays a web page designed specifically for voice input.

[0702] 2. Present voice input interface

[0703] The device provides the user with a recording interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button.

[0704] 3. Recording your impressions

[0705] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's thoughts and temporarily stores them locally.

[0706] 4. Sending audio data

[0707] When the user presses the "Send" button, the device sends the recorded audio data to the server via an HTTP POST request. The server receives the audio data.

[0708] 5. Convert audio data into text data

[0709] The server sends the received voice data to the generative AI model, which converts it into text data. The generative AI model extracts text information from the voice and generates text data.

[0710] 6. Sentiment Analysis of Text Data

[0711] The server passes the text data to the emotion engine to recognize the user's emotions. The emotion engine extracts positive and negative emotions from the text data and returns the emotion information to the server.

[0712] 7. Summarizing and Analyzing Text Data

[0713] The server extracts important information based on the text data and emotional information, filters out negative expressions and unwanted words, and reconstructs the data to emphasize positive opinions, creating summary data.

[0714] 8. Saving Summary Data

[0715] The server saves the generated summary data in a database, along with metadata such as the user ID, store ID, and date and time.

[0716] 9. Review and Final Submission

[0717] The server then sends the saved summary data back to the terminal and displays a final confirmation interface. The user confirms the content and presses the "Confirm" button to officially post the review. The saved data is made available for viewing by other users and store managers.

[0718] Specific examples

[0719] 1. User scans the QR code

[0720] Users scan a QR code placed on a cafe table with their smartphone, and a web page for voice input is displayed.

[0721] 2. The voice input page will appear

[0722] The device provides a voice input interface that displays "Start Recording," "Stop Recording," and "Send" buttons.

[0723] 3. User Records Their Thoughts

[0724] The user presses the "Start Recording" button and says, "The latte at this cafe was very tasty, but the order was a little late." The device records this comment and stores it locally.

[0725] 4. Stop recording and send

[0726] After the user presses the "Stop" button, he or she presses the "Send" button, and the terminal sends the recorded voice data to the server.

[0727] 5. The server converts the voice data into text

[0728] The server sends the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but the order was a little late."

[0729] 6. Emotional Recognition

[0730] The server passes the text data to an emotion engine, which recognizes the positive emotion "The latte was very tasty" and the negative emotion "The order was a little late."

[0731] 7. Summarizing and Analyzing Text Data

[0732] The server emphasizes the positive comments, summarizing them as "the latte is very tasty," and filters out the negative ones.

[0733] 8. Save to database

[0734] The server saves the summary data, "The latte is very tasty," in a database along with metadata such as the collection date, store ID, and user ID.

[0735] 9. User finalizes post

[0736] The server sends the summary data back to the terminal, and the user finally confirms the content. When the user presses the "Confirm" button, the review is officially posted and made public to other users and store managers.

[0737] This system allows users to easily post high-quality reviews, while at the same time highlighting positive feedback, providing extremely useful data for other users and stores.

[0738] The processing flow will be explained below.

[0739] Step 1:

[0740] A user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone interprets the URL written in the QR code and displays a web page for voice input in the browser.

[0741] Step 2:

[0742] The terminal provides the user with a voice input interface, which displays a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the voice data.

[0743] Step 3:

[0744] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[0745] Step 4:

[0746] The user presses the "Stop" button. The device stops recording and saves the recording locally. The audio data is saved as a temporary file on the device.

[0747] Step 5:

[0748] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request. The request includes metadata (e.g., store ID, user ID) along with the audio data.

[0749] Step 6:

[0750] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[0751] Step 7:

[0752] The server calls the generative AI model and converts the saved voice data into text data. The AI ​​model converts the voice data into text, and the server receives the converted text data.

[0753] Step 8:

[0754] The server passes the text data to the emotion engine to analyze the user's emotions. The emotion engine extracts positive and negative emotions from the text and returns the results to the server.

[0755] Step 9:

[0756] The server processes the text data based on the results from the emotion engine, filtering out negative expressions and unwanted words and restructuring the data to emphasize positive opinions.

[0757] Step 10:

[0758] The server saves the summarized and refined review data in a database, along with metadata such as date and time, store ID, and user ID.

[0759] Step 11:

[0760] The server sends the refined review data to the terminal, which displays a final confirmation interface so that the user can check the content.

[0761] Step 12:

[0762] The user confirms the final review content and presses the "Confirm" button. The server updates the submission status, and the new review data is made available to other users and store managers in real time.

[0763] This detailed processing flow allows users to easily post high-quality reviews and allows stores to receive useful feedback in real time.

[0764] Example 2

[0765] 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."

[0766] Conventional review posting systems require users to manually input text, which is time-consuming and has the problem of not fully reflecting emotional expressions. There is also a risk that negative or inappropriate language may be posted. This leads to issues such as a decrease in user convenience and the quality of posted data.

[0767] 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.

[0768] In this invention, the server includes a generating means for converting voice data into text data, an emotion recognizing means for performing emotion analysis on the text data, and a processing means for summarizing and analyzing the text data. This allows users to easily post reviews by voice, and also enables the generation of high-quality word-of-mouth data by emphasizing positive opinions while filtering out negative expressions and inappropriate words.

[0769] "Input means" refers to a device or interface that a user uses to make voice input.

[0770] "Server Means" refers to a central management system for receiving and processing voice and other data.

[0771] "Generation means" refers to a function or device for converting voice data into text data.

[0772] "Emotion recognition means" refers to a function or device for analyzing and detecting a user's emotions from text data.

[0773] "Processing means" refers to functions or devices for summarizing and analyzing text data.

[0774] "Storage means" refers to a function or device for storing the generated summary data and other related data.

[0775] "Providing means" refers to the functions and devices for providing stored data to users and other parties.

[0776] "Terminal" refers to a personal electronic device that a user uses to provide voice input.

[0777] "Database" refers to a system for centrally storing and managing summarized review data and other related information.

[0778] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion recognition means for recognizing a user's emotion, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described in detail below.

[0779] First, the user scans a QR code displayed at a restaurant or cafe with a smartphone or other device. When the scanned URL is accessed, a web page for voice input is displayed. This web page has buttons for "Start Recording," "Stop Recording," and "Send."

[0780] When the user presses the "Start Recording" button and begins speaking, the device records the user's voice and temporarily stores it locally. When the user finishes recording, they press the "Stop Recording" button, and then the "Send" button, and the device sends the voice data to the server.

[0781] The server sends the received voice data to a generative AI model (such as OpenAI's Whisper), which converts it into text data. The generated text data is then passed to an emotion recognition means (such as IBM Watson), where sentiment analysis is performed to identify positive and negative emotions. After receiving the results of the sentiment analysis, the server uses a processing means to summarize and analyze the text data, extracting important information and filtering out negative expressions and NG words. A reconstructed summary is generated that emphasizes positive opinions.

[0782] The generated summary data is stored in a database by the server. This data is also saved along with metadata such as the user ID, store ID, and collection date. The server then sends the summary data back to the terminal and displays a confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted. The saved data is made available for viewing by other users and store managers.

[0783] Specific examples

[0784] When a user visits the cafe and scans a QR code on the table with their smartphone, a web page for voice input is automatically displayed. When the user presses the "Start Recording" button and says something like, "The latte at this cafe was very tasty, but the order was a little late," the device begins recording the voice. When the user finishes recording and presses the "Send" button, the device sends the recorded data to the server.

[0785] The server converts the received voice data into text data using a generative AI model such as OpenAI's Whisper, and then analyzes the generated text data using an emotion engine such as IBM Watson, detecting positive emotions such as "The latte was very delicious" and negative emotions such as "The order was a little late." Based on this, the server generates a summary such as "The latte was very delicious" and stores it in a database.

[0786] The server sends the summary data to the terminal for the user to make a final confirmation, and when the user presses the "Confirmation Complete" button, the review is officially posted.

[0787] Prompt Sentence Examples

[0788] Example prompt sentence:

[0789] "Transcribe the following line and identify the sentiment: 'The latte at this cafe was very tasty, but the order was a little slow.'"

[0790] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0791] Step 1:

[0792] The user scans a QR code installed in a cafe or restaurant with their smartphone.

[0793] Input: QR code, smartphone

[0794] Output: URL of the web page for voice input

[0795] How it works: The user reads the QR code using a QR code reader app or the smartphone's built-in camera. The smartphone's browser automatically accesses the URL that was read and displays a web page for voice input.

[0796] Step 2:

[0797] The device displays a voice input interface.

[0798] Input: URL of the web page for voice input

[0799] Output: Audio input interface ("Start Recording", "Stop Recording", "Send" buttons)

[0800] What it does: The browser accesses the specified URL and displays the recording interface to the user, with "Start Recording," "Stop Recording," and "Send" buttons.

[0801] Step 3:

[0802] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's voice and temporarily stores it locally.

[0803] Input: User's voice, start recording operation

[0804] Output: Recorded audio data

[0805] Specific operation: When the user presses the "Start Recording" button, the device starts recording and captures the user's voice. The audio data is temporarily stored in the device's local memory until the recording is finished.

[0806] Step 4:

[0807] The user presses the "Stop Recording" button and then the "Send" button. The device sends the recorded audio data to the server via an HTTP POST request.

[0808] Input: Recorded voice data, sending operation

[0809] Output: Audio data sent to the server

[0810] Specific operation: When the user presses the "Stop Recording" button and then the "Send" button, the device creates an HTTP POST request and sends the recorded audio data to the server. The server receives this request.

[0811] Step 5:

[0812] The server sends the received voice data to a generative AI model, which converts it into text data.

[0813] Input: Audio data

[0814] Output: Text data

[0815] Specific operation: The server passes the voice data to the generative AI model, which then performs speech recognition and generates text data. For example, it uses OpenAI's Whisper to extract text information from the voice.

[0816] Step 6:

[0817] The server passes the text data to an emotion recognition means to recognize the user's emotion.

[0818] Input: Text data

[0819] Output: Emotional information (positive, negative)

[0820] Specific operation: The server sends the text data to an emotion recognition unit (e.g., IBM Watson) for emotion analysis. The emotion recognition unit extracts positive and negative emotions from the text data and returns the emotion information to the server.

[0821] Step 7:

[0822] The server extracts important information based on text data and emotional information, and filters out negative expressions and prohibited words.

[0823] Input: Text data, emotion information

[0824] Output: Summary data

[0825] Specific operation: The server considers emotional information, restructures the text to emphasize positive opinions, filters out negative expressions and NG words, and generates summary data.

[0826] Step 8:

[0827] The server stores the generated summary data in a database.

[0828] Input: Summary data, metadata (user ID, store ID, date and time, etc.)

[0829] Output: Saved summary data

[0830] Specific operation: When the server saves the generated summary data in the database, it also saves metadata such as user ID, store ID, and collection date, which makes the data unique and enables various searches.

[0831] Step 9:

[0832] The server sends the summary data back to the terminal and displays a final confirmation interface. The user checks the content and presses the "Confirm" button to officially post the review.

[0833] Input: Summary data

[0834] output: Final confirmation interface, officially posted review data

[0835] Specific operation: The server sends the data back to the device and displays a final confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted and made available in a saved location for other users and store managers to view.

[0836] (Application example 2)

[0837] 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."

[0838] With conventional voice input systems, when users provide feedback via voice, the content is not properly converted into text data, and sentiment analysis and summarization are not performed, resulting in problems with the content of the feedback not being accurately reflected. Another problem is that feedback containing negative expressions or prohibited words is made public as is. Furthermore, since there is no interface for users to perform a final review, the quality of the feedback cannot be guaranteed, and there are also issues with it not being able to be used as useful data for other users and store managers.

[0839] 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.

[0840] In this invention, the server includes a terminal means for a user to input voice data, a means for providing an interface for the user to input voice data, a server means for receiving the voice data, a generation means for converting the voice data into text data, an emotion identification means for identifying emotions based on the text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a provision means for providing the data stored in the storage means to the user, and a means for the provision means to provide an interface for the user to finalize the summary data. This allows user feedback to be accurately and quickly converted into text data and subjected to emotion analysis and summarization, thereby filtering out negative expressions and unwanted words and emphasizing positive opinions. Furthermore, providing an interface for the user to finalize the feedback ensures the quality of the feedback and allows it to be used as useful data for other users and store managers.

[0841] The "terminal for user voice input" refers to a device used by a user to input their own voice, and refers to a general mobile communication terminal such as a smartphone or tablet.

[0842] "Means for providing an interface" refers to a mechanism that provides the user with an operation screen and functions for voice input, and refers to a screen that includes buttons for starting and stopping recording, sending, etc.

[0843] The "server means for receiving voice data" is a server device for receiving voice data sent from a user via a network.

[0844] "Generative means for converting voice data into text data" refers to a mechanism for analyzing voice data and converting it into corresponding text data, including voice recognition software and generative AI models.

[0845] The "emotion identification means for identifying emotions based on text data" is a mechanism for identifying a user's emotions from the content of text data, and refers to an emotion analysis engine that uses natural language processing.

[0846] The "processing means for summarizing and analyzing text data" is a mechanism for extracting important information from text data and summarizing the content, and uses a natural language processing algorithm.

[0847] The "storage means for saving the summary data" is a mechanism for continuously storing the generated summary data, and includes databases and cloud storage.

[0848] The "means for providing the user with data stored in the storage means" refers to a mechanism that allows the user to view or use the stored data, and refers to a device that provides a viewing interface and notification functions.

[0849] "Means for providing an interface for users to make final confirmation of summary data" refers to a mechanism that provides an operation screen or function that allows users to make final confirmation of the generated summary data and approve or correct it.

[0850] This system allows users to easily input voice data, accurately converts the data into text, analyzes and summarizes emotions, and stores and provides them. This system includes a mobile communication device such as a smartphone or tablet, a server, a database, a generative AI model, and a sentiment analysis engine.

[0851] The user first uses the device to input voice data. Smartphones and tablets are suitable for this purpose. For example, the user can scan a QR code placed on a table in a store to view an interface dedicated to voice input. This interface provides a screen with buttons to start recording, stop recording, and send.

[0852] When a user records their voice feedback, the voice data is temporarily stored on the device. After recording is complete, the user presses the send button and the voice data is sent to the server via an HTTP POST request. The server receives the voice data and converts it into text using a generative AI model. This generative AI model can be, for example, the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[0853] The generated text data is passed to an emotion identification unit, which performs emotion analysis using natural language processing engines such as IBM Watson or Microsoft Azure Text Analytics, to identify positive and negative emotions.

[0854] The text data is then summarized and analyzed. Natural language processing algorithms are used to highlight positive opinions and filter out negative expressions and unwanted words. Important information is extracted and summary data is generated. This summary data is then stored in a database, which can be a storage medium. Databases such as Amazon RDS or MySQL can be used.

[0855] The saved summary data is provided to the user via the providing means. The user uses a smartphone or tablet to display an interface for final confirmation of the summary data. The interface is designed so that the user can confirm the content of the summary data again and press the "Confirmation Complete" button to officially post the review.

[0856] As a concrete example, a user scans a QR code on a table in a cafe with their smartphone and provides verbal feedback such as, "The food was very good, but the service was a little slow." This feedback is converted into text data by inputting the following prompt sentence into a generative AI model:

[0857] Audio data: 'The food was very good, but the service was a little slow.'

[0858] Convert it into text data.

[0859] This allows for accurate conversion of user feedback, rapid sentiment analysis, and summarization, and the system can be used as useful data for other users and store managers.

[0860] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0861] Step 1:

[0862] The user uses a smartphone or tablet to scan a QR code displayed on a table in the store, which opens a web page for voice input.

[0863] Input: QR code

[0864] Output: A web page dedicated to voice input is displayed.

[0865] What it does: When you use your smartphone camera to read a QR code, your browser opens the URL embedded in the QR code, and the web page pointed to by that URL is displayed on the screen.

[0866] Step 2:

[0867] The terminal provides the user with an interface for voice input, which includes a recording start button, a recording stop button, and a send button.

[0868] Input: Load web page

[0869] Output: Audio input interface

[0870] Specific behavior: A web page displays a voice input UI composed of HTML / CSS / JavaScript on the screen.

[0871] Step 3:

[0872] The user presses the start recording button to record the voice feedback, and when the recording is complete, presses the stop recording button to temporarily save the voice data.

[0873] Input: User voice input

[0874] Output: Temporarily saved audio data

[0875] Specific operation: When voice input begins, the smartphone's microphone captures the voice data and temporarily stores it in local storage.

[0876] Step 4:

[0877] When the user presses the send button, the terminal sends the audio data to the server using an HTTP POST request.

[0878] Input: Stored audio data

[0879] Output: Sending audio data to the server

[0880] Specific operation: Uses the HTTP library to send audio data to the specified URL on the server.

[0881] Step 5:

[0882] The server sends the received voice data to a generative AI model and converts it into text data.

[0883] Input: Audio data

[0884] Output: Text data

[0885] How it works: The server sends a request to the generative AI model via a REST API to generate text data from the audio data, using the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[0886] Step 6:

[0887] The server passes the generated text data to an emotion identification means for emotion analysis.

[0888] Input: Text data

[0889] Output: Emotional information

[0890] Specific operation: Text data is passed to a natural language processing engine (such as IBM Watson or Microsoft Azure Text Analytics) to extract sentiment information such as positive or negative.

[0891] Step 7:

[0892] The server summarizes and analyzes the text data and emotional information, extracts important information, filters out negative expressions and unwanted words, and creates summary data that emphasizes positive opinions.

[0893] Input: Text data, emotion information

[0894] Output: Summary data

[0895] What it does: It uses natural language processing algorithms to extract important information from text data and highlight and reframe positive opinions.

[0896] Step 8:

[0897] The generated summary data is saved in a database, along with metadata such as the user ID, store ID, and date and time.

[0898] Input: Abstract data, metadata

[0899] Output: Summary data stored in a database

[0900] What it does: It saves summary data and metadata to databases such as MySQL or Amazon RDS using INSERT queries.

[0901] Step 9:

[0902] The server transmits the saved summary data to the terminal and provides an interface for the user to make a final confirmation.

[0903] Input: Summary data retrieved from the database

[0904] Output: Interface for final confirmation

[0905] Specific operation: The saved summary data is sent to the terminal as an HTTP response and displayed in the summary display area of ​​the web page.

[0906] Step 10:

[0907] If the user makes a final check and there are no problems with the content, the review will be officially posted by pressing the "Confirm" button.

[0908] Input: User confirmation operation

[0909] Output: Review officially posted

[0910] Specific operation: Upon user action, the final confirmation completion information is sent to the server and finally made public to other users and store managers.

[0911] This process allows user feedback to be processed quickly and accurately, and summary data that reflects appropriate emotions is generated.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] [Third embodiment]

[0916] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0917] 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.

[0918] 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).

[0919] 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.

[0920] 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.

[0921] 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).

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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."

[0928] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[0929] System Overview

[0930] 1. The user scans the QR code

[0931] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The QR code contains a URL for a web page that offers voice input functionality.

[0932] 2. Provides a voice input interface

[0933] The terminal displays a voice input interface, which includes a voice input button, a start recording button, a stop recording button, and a send button.

[0934] 3. Record your thoughts

[0935] The user presses the "Start Recording" button and begins speaking their thoughts aloud, which the device records and stores locally.

[0936] 4. Send audio data

[0937] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[0938] 5. Convert audio data to text

[0939] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The server then retrieves the converted text data.

[0940] 6. Summarizing and Analyzing Text Data

[0941] The server analyzes the text data, extracts important information, filters out negative expressions and NG words, and restructures the text to emphasize positive opinions.

[0942] 7. Save to database

[0943] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[0944] 8. Final Review and Submission

[0945] The server sends the refined review data to the device, and the user confirms the final review content. By pressing the "Confirmation Complete" button, the review is officially posted and becomes available for other users to view.

[0946] Specific examples

[0947] 1. User scans the QR code:

[0948] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[0949] 2. The voice input page will appear:

[0950] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[0951] 3. User records their thoughts:

[0952] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[0953] 4. Stop recording and send:

[0954] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[0955] 5. The server converts the audio data into text:

[0956] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[0957] 6. Summarize and analyze text data:

[0958] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[0959] 7. Save to database:

[0960] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[0961] 8. User finalizes post:

[0962] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[0963] This system allows users to easily post high-quality reviews by voice input, enabling real-time feedback and providing useful information to other users.

[0964] The processing flow will be explained below.

[0965] Step 1:

[0966] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The smartphone interprets the URL written on the QR code and displays a web page for voice input in the browser.

[0967] Step 2:

[0968] The terminal provides the user with a voice input interface, which includes a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the recorded data.

[0969] Step 3:

[0970] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[0971] Step 4:

[0972] The user presses the "Stop" button. The device stops recording and saves the recording locally as a temporary file.

[0973] Step 5:

[0974] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request.

[0975] Step 6:

[0976] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[0977] Step 7:

[0978] The server calls the generative AI model and converts the stored voice data into text data. The AI ​​model converts the voice data into text and returns the results to the server.

[0979] Step 8:

[0980] The server analyzes the generated text data, extracts important information from it, filters out negative expressions and NG words, and restructures it to emphasize positive opinions.

[0981] Step 9:

[0982] The server saves the summarized and refined review data in a database, adding meta information such as date and time, store ID, and user ID.

[0983] Step 10:

[0984] The server sends the refined review data back to the device, which displays a final confirmation interface for the user to review.

[0985] Step 11:

[0986] The user confirms the final review content and presses the "Confirm" button. The server updates the status of the submission completion, and the review data is made available to other users and store managers in real time.

[0987] Example 1

[0988] 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."

[0989] Current consumer review and feedback systems often require users to manually input text, which is time-consuming and laborious. Furthermore, the quality of the reviews entered varies, and they often contain negative or inappropriate content. There is a need for a system that can easily and efficiently generate high-quality reviews by addressing these issues.

[0990] 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.

[0991] In this invention, the server includes an access means that allows a user to access using a QR code, a display means that displays a voice input interface provided by the access means, and a filtering means that filters out negative expressions and NG words and emphasizes positive opinions when summarizing and analyzing the voice data. This allows users to easily access using the QR code and quickly provide reviews through voice input, and the generated reviews are of high quality and emphasize positive expressions, making it possible to provide useful information to other users.

[0992] A "user" is an individual or consumer who utilizes the system to provide voice input.

[0993] "Input means" refers to a device or interface that allows a user to input voice information. Examples include smartphones and tablets.

[0994] "Server Means" refers to a central system for receiving and processing voice data, including functions such as data storage, conversion, and analysis.

[0995] "Generation means" refers to a program or algorithm used to convert voice data into text data. For example, a generative AI model is used.

[0996] "Processing means" refers to programs and algorithms for summarizing and analyzing text data.

[0997] "Storage means" refers to a storage device or database for storing the generated summary data.

[0998] "Providing means" refers to the interface or function for providing stored data to users or other systems.

[0999] "Device" means a device used by a user to provide voice input. Examples include smartphones and tablets.

[1000] A "database" refers to a system that stores information in an organized manner and makes it searchable.

[1001] "Access Method" refers to the function or method by which a user can access the system using a QR code.

[1002] "Display means" refers to a function or device for visually presenting the voice input interface to the user.

[1003] "Filtering measures" refer to processes that eliminate negative expressions and inappropriate words and emphasize positive opinions.

[1004] The present invention relates to a system that allows users to easily generate and share high-quality word-of-mouth reviews through voice input. This system allows users to access the system using a QR code, input voice data, convert the voice data into text data, summarize and analyze the data, and finally provide organized word-of-mouth data. A detailed description of specific embodiments of this system is provided below.

[1005] First, we will explain the hardware and software configuration of the system. This system includes a device (e.g., a smartphone, tablet, etc.) for users to input voice data, a server that receives and processes the voice data, a generative AI model that converts the voice data into text data, an algorithm for summarizing and analyzing the text data, a database that stores the generated summary data, and an interface that provides the stored data to users.

[1006] The following process is an example of a specific operation.

[1007] First, the user scans a QR code installed in a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that provides voice input functionality. The smartphone's browser automatically opens the web page, and the voice input screen is displayed. At this stage, the user presses the "Start Recording" button and voice-records their impressions of the cafe or restaurant. The device records the audio using the built-in microphone and saves the recording locally.

[1008] Next, when the user presses the "Send" button, the device uploads the recorded voice data to the server. The server receives the voice data and stores it in the appropriate directory. The server then passes the voice data to the generative AI model, which converts the voice into text data. The generative AI model uses a prompt sentence, such as "Please convert this voice data into text data."

[1009] The converted text data is then retrieved by a server, which then analyzes it to extract important information, filtering out negative expressions and inappropriate words, and restructuring the text to emphasize positive opinions.

[1010] Once the text data has been summarized and filtered, the server stores the summarized review data in a database. The stored data also includes meta information such as the collection date, store ID, and user ID. The server then sends the refined review data back to the device, where the user can confirm the final review content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[1011] This system allows users to easily access the system using a QR code and quickly provide reviews through voice input. The generated reviews are of high quality and emphasize positive expressions, providing useful information for other users.

[1012] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1013] Step 1:

[1014] The user scans the QR code

[1015] A user scans a QR code displayed at a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that offers voice input functionality. The scanned URL is entered, and the smartphone's browser automatically opens the web page.

[1016] Input: QR code read by the camera

[1017] Output: Web page with voice input function

[1018] Step 2:

[1019] Show voice input interface

[1020] The terminal displays a voice input interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button, and the user uses this interface to input voice.

[1021] Input: The browser that opened the web page

[1022] Output: Audio input interface

[1023] Step 3:

[1024] Record your thoughts

[1025] The user presses the "Start Recording" button, and the device records audio using the built-in microphone. The user then enters their impressions of the restaurant or cafe by voice. Recording can be stopped by pressing the "Stop Recording" button.

[1026] Input: Press the start recording button, input voice

[1027] Output: Recorded audio data

[1028] Step 4:

[1029] Send audio data

[1030] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[1031] Input: Pressing the send button, recorded voice data

[1032] Output: Audio data stored on the server

[1033] Step 5:

[1034] Converting voice data to text

[1035] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The generative AI model uses a prompt saying, "Please convert this voice data into text data." The text data is returned from the generative AI model and retrieved by the server.

[1036] Input: Recorded voice data, prompt "Convert this voice data into text data"

[1037] Output: Converted text data

[1038] Step 6:

[1039] Summarize and analyze text data

[1040] The server analyzes the acquired text data, extracts important information, filters out negative expressions and inappropriate words, and emphasizes positive opinions.

[1041] Input: Converted text data

[1042] Output: Summarized and analyzed text data

[1043] Step 7:

[1044] Store in database

[1045] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[1046] Input: Summarized and refined text data

[1047] Output: Review data stored in the database

[1048] Step 8:

[1049] Final check and submission

[1050] The server sends the refined review data back to the device, where the user can confirm the content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[1051] Input: Summarized and refined text data, user confirmation

[1052] Output: Officially posted review data

[1053] (Application example 1)

[1054] 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."

[1055] Existing brick-and-mortar stores lack the means to efficiently collect and analyze customer feedback in a high-quality manner. Traditional surveys and feedback systems are time-consuming and tend to limit the amount of data collected. Furthermore, due to a lack of an easy-to-use interface for customers, the quality and quantity of feedback is often insufficient.

[1056] 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.

[1057] In this invention, the server includes an input means for users to input voice data, a means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server. This allows users to easily provide feedback by voice in physical stores, and makes it possible to efficiently collect, analyze, store, and provide high-quality word-of-mouth data.

[1058] "User" refers to an individual or organization that uses the system to provide voice input and feedback.

[1059] "Input means" refers to a device or interface that allows a user to input voice information.

[1060] "Server" refers to a computer system for receiving and processing audio data.

[1061] "Generation means" refers to software or algorithms for converting audio data into text data.

[1062] "Processing means" refers to software and algorithms for summarizing and analyzing text data.

[1063] "Storage means" refers to a database or storage system for storing the generated summary data.

[1064] "Presentation means" refers to the interface or means for presenting stored data to users or other systems.

[1065] "QR Code" refers to a visual code that encodes information for accessing a voice input interface.

[1066] "Communication means" refers to a network interface for exchanging voice data and processing results with the server.

[1067] "Generative AI model" refers to an artificial intelligence model used to convert voice data into text data.

[1068] "Summary data" refers to information that summarizes text data generated from audio data.

[1069] "Final confirmation means" refers to an interface that allows the user to confirm the summary data and confirm the posting.

[1070] A system embodying this invention includes an input means for a user to input voice data, a server means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for providing the stored data to the user, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server.

[1071] Basic system configuration

[1072] 1. Input means (terminal):

[1073] Users use devices such as smartphones and tablets to read QR codes installed in restaurants and cafes, which contain the URL of a web page that offers voice input functionality.

[1074] The terminal displays a voice input interface to prompt the user to input voice, and the interface includes a voice input button, a recording start button, a recording stop button, and a send button.

[1075] 2. Server means:

[1076] Receives audio data sent from the terminal and stores it in the appropriate directory.

[1077] The server passes the voice data to a generative AI model (e.g., OpenAI's Whisper or Deepgram), which converts the voice data into text data.

[1078] 3. Generation means:

[1079] Using a generative AI model running on the server, voice data is converted into text data with high accuracy.

[1080] Specific generative AI models include Whisper and Deepgram.

[1081] 4. Processing Method:

[1082] The server analyzes the text data and extracts important information. It then filters out negative expressions and unwanted words and restructures the text to emphasize positive opinions.

[1083] The server stores the analysis results as summary data.

[1084] 5. Preservation means:

[1085] The server stores the summarized and refined review data in a database, including meta-information such as the collection date, store ID, and user ID.

[1086] 6. Means of provision:

[1087] The server sends the refined review data back to the device for final confirmation and posting. Once the user confirms and presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[1088] Specific use cases

[1089] scenario

[1090] A user enters a cafe, enjoys their latte, and then scans the QR code on the table to post feedback.

[1091] 1. User scans the QR code:

[1092] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[1093] 2. The voice input page will appear:

[1094] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[1095] 3. User records their thoughts:

[1096] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[1097] 4. Stop recording and send:

[1098] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[1099] 5. The server converts the audio data into text:

[1100] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[1101] 6. Summarize and analyze text data:

[1102] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[1103] 7. Save to database:

[1104] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[1105] 8. User finalizes post:

[1106] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[1107] Examples of prompt statements

[1108] Example prompts to be input to the generative AI model:

[1109] Voice input: "The latte at this cafe was very good, but it took a while for it to be served."

[1110] Prompt for generative AI model: "Turn the following audio recording into text, highlighting the positive points and summarizing it. Please tone down the negative aspects.

[1111] Audio recording: {audio data}

[1112] """

[1113] Audio data: "The latte at this cafe was very tasty, but it took a while for it to be served after I ordered it."

[1114] """

[1115] Using this prompt, the audio data is converted into text and a summary is generated that highlights positive words.

[1116] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1117] Step 1:

[1118] The user scans the QR code.

[1119] In this step, a user uses their smartphone camera to scan a QR code displayed in a physical store. The QR code contains the URL of a web page that provides voice input functionality, and scanning the code retrieves the URL. The input is the user scanning the QR code, and the output is the URL of the voice input page.

[1120] Step 2:

[1121] The device displays a voice input interface.

[1122] The device opens a browser using the URL obtained in step 1 and displays a voice input interface. This interface includes a voice input button, a start recording button, a stop recording button, and a send button. The input is the URL read from the QR code, and the output is the display of the voice input interface.

[1123] Step 3:

[1124] The user records their impressions.

[1125] The user presses the "Start Recording" button on the voice input interface and starts speaking. The device records the voice and stores it locally. The input is the user's voice, and the output is the recorded voice data.

[1126] Step 4:

[1127] The user transmits voice data.

[1128] When the user presses the "Stop" button and then the "Send" button, the device uploads the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server.

[1129] Step 5:

[1130] The server converts the audio data into text.

[1131] The server passes the received voice data to a generative AI model, which converts the voice data into text data. Examples of generative AI models used include OpenAI's Whisper and Deepgram. The input is voice data, and the output is generated text data.

[1132] Step 6:

[1133] The server summarizes and analyzes the text data.

[1134] The server analyzes the text data and extracts important information. The server filters out negative expressions and unwanted words and restructures the data to emphasize positive opinions. The input is the generated text data, and the output is the summarized and restructured text data.

[1135] Step 7:

[1136] The server stores the summarized data in a database.

[1137] The server stores the summarized and refined review data in a database. When saving, meta information such as date and time, store ID, and user ID is also added. The input is the summarized and reconstructed text data, and the output is the review data stored in the database.

[1138] Step 8:

[1139] The server sends the summary data to the terminal, and the user makes a final confirmation.

[1140] The server sends the refined review data back to the user's device, where the user performs a final confirmation. When the user presses the "Confirmation Complete" button, the review is officially posted. The input is the summarized and reconstructed review data and the user's confirmation operation, and the output is the officially posted review.

[1141] 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.

[1142] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion engine for recognizing the user's emotions, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[1143] System Overview

[1144] 1. Scan the QR code

[1145] The user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone's browser opens the URL and displays a web page designed specifically for voice input.

[1146] 2. Present voice input interface

[1147] The device provides the user with a recording interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button.

[1148] 3. Recording your impressions

[1149] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's thoughts and temporarily stores them locally.

[1150] 4. Sending audio data

[1151] When the user presses the "Send" button, the device sends the recorded audio data to the server via an HTTP POST request. The server receives the audio data.

[1152] 5. Convert audio data into text data

[1153] The server sends the received voice data to the generative AI model, which converts it into text data. The generative AI model extracts text information from the voice and generates text data.

[1154] 6. Sentiment Analysis of Text Data

[1155] The server passes the text data to the emotion engine to recognize the user's emotions. The emotion engine extracts positive and negative emotions from the text data and returns the emotion information to the server.

[1156] 7. Summarizing and Analyzing Text Data

[1157] The server extracts important information based on the text data and emotional information, filters out negative expressions and unwanted words, and reconstructs the data to emphasize positive opinions, creating summary data.

[1158] 8. Saving Summary Data

[1159] The server saves the generated summary data in a database, along with metadata such as the user ID, store ID, and date and time.

[1160] 9. Review and Final Submission

[1161] The server then sends the saved summary data back to the terminal and displays a final confirmation interface. The user confirms the content and presses the "Confirm" button to officially post the review. The saved data is made available for viewing by other users and store managers.

[1162] Specific examples

[1163] 1. User scans the QR code

[1164] Users scan a QR code placed on a cafe table with their smartphone, and a web page for voice input is displayed.

[1165] 2. The voice input page will appear

[1166] The device provides a voice input interface that displays "Start Recording," "Stop Recording," and "Send" buttons.

[1167] 3. User Records Their Thoughts

[1168] The user presses the "Start Recording" button and says, "The latte at this cafe was very tasty, but the order was a little late." The device records this comment and stores it locally.

[1169] 4. Stop recording and send

[1170] After the user presses the "Stop" button, he or she presses the "Send" button, and the terminal sends the recorded voice data to the server.

[1171] 5. The server converts the voice data into text

[1172] The server sends the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but the order was a little late."

[1173] 6. Emotional Recognition

[1174] The server passes the text data to an emotion engine, which recognizes the positive emotion "The latte was very tasty" and the negative emotion "The order was a little late."

[1175] 7. Summarizing and Analyzing Text Data

[1176] The server emphasizes the positive comments, summarizing them as "the latte is very tasty," and filters out the negative ones.

[1177] 8. Save to database

[1178] The server saves the summary data, "The latte is very tasty," in a database along with metadata such as the collection date, store ID, and user ID.

[1179] 9. User finalizes post

[1180] The server sends the summary data back to the terminal, and the user finally confirms the content. When the user presses the "Confirm" button, the review is officially posted and made public to other users and store managers.

[1181] This system allows users to easily post high-quality reviews, while at the same time highlighting positive feedback, providing extremely useful data for other users and stores.

[1182] The processing flow will be explained below.

[1183] Step 1:

[1184] A user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone interprets the URL written in the QR code and displays a web page for voice input in the browser.

[1185] Step 2:

[1186] The terminal provides the user with a voice input interface, which displays a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the voice data.

[1187] Step 3:

[1188] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[1189] Step 4:

[1190] The user presses the "Stop" button. The device stops recording and saves the recording locally. The audio data is saved as a temporary file on the device.

[1191] Step 5:

[1192] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request. The request includes metadata (e.g., store ID, user ID) along with the audio data.

[1193] Step 6:

[1194] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[1195] Step 7:

[1196] The server calls the generative AI model and converts the saved voice data into text data. The AI ​​model converts the voice data into text, and the server receives the converted text data.

[1197] Step 8:

[1198] The server passes the text data to the emotion engine to analyze the user's emotions. The emotion engine extracts positive and negative emotions from the text and returns the results to the server.

[1199] Step 9:

[1200] The server processes the text data based on the results from the emotion engine, filtering out negative expressions and unwanted words and restructuring the data to emphasize positive opinions.

[1201] Step 10:

[1202] The server saves the summarized and refined review data in a database, along with metadata such as date and time, store ID, and user ID.

[1203] Step 11:

[1204] The server sends the refined review data to the terminal, which displays a final confirmation interface so that the user can check the content.

[1205] Step 12:

[1206] The user confirms the final review content and presses the "Confirm" button. The server updates the submission status, and the new review data is made available to other users and store managers in real time.

[1207] This detailed processing flow allows users to easily post high-quality reviews and allows stores to receive useful feedback in real time.

[1208] Example 2

[1209] 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."

[1210] Conventional review posting systems require users to manually input text, which is time-consuming and has the problem of not fully reflecting emotional expressions. There is also a risk that negative or inappropriate language may be posted. This leads to issues such as a decrease in user convenience and the quality of posted data.

[1211] 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.

[1212] In this invention, the server includes a generating means for converting voice data into text data, an emotion recognizing means for performing emotion analysis on the text data, and a processing means for summarizing and analyzing the text data. This allows users to easily post reviews by voice, and also enables the generation of high-quality word-of-mouth data by emphasizing positive opinions while filtering out negative expressions and inappropriate words.

[1213] "Input means" refers to a device or interface that a user uses to make voice input.

[1214] "Server Means" refers to a central management system for receiving and processing voice and other data.

[1215] "Generation means" refers to a function or device for converting voice data into text data.

[1216] "Emotion recognition means" refers to a function or device for analyzing and detecting a user's emotions from text data.

[1217] "Processing means" refers to functions or devices for summarizing and analyzing text data.

[1218] "Storage means" refers to a function or device for storing the generated summary data and other related data.

[1219] "Providing means" refers to the functions and devices for providing stored data to users and other parties.

[1220] "Terminal" refers to a personal electronic device that a user uses to provide voice input.

[1221] "Database" refers to a system for centrally storing and managing summarized review data and other related information.

[1222] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion recognition means for recognizing a user's emotion, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described in detail below.

[1223] First, the user scans a QR code displayed at a restaurant or cafe with a smartphone or other device. When the scanned URL is accessed, a web page for voice input is displayed. This web page has buttons for "Start Recording," "Stop Recording," and "Send."

[1224] When the user presses the "Start Recording" button and begins speaking, the device records the user's voice and temporarily stores it locally. When the user finishes recording, they press the "Stop Recording" button, and then the "Send" button, and the device sends the voice data to the server.

[1225] The server sends the received voice data to a generative AI model (such as OpenAI's Whisper), which converts it into text data. The generated text data is then passed to an emotion recognition means (such as IBM Watson), where sentiment analysis is performed to identify positive and negative emotions. After receiving the results of the sentiment analysis, the server uses a processing means to summarize and analyze the text data, extracting important information and filtering out negative expressions and NG words. A reconstructed summary is generated that emphasizes positive opinions.

[1226] The generated summary data is stored in a database by the server. This data is also saved along with metadata such as the user ID, store ID, and collection date. The server then sends the summary data back to the terminal and displays a confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted. The saved data is made available for viewing by other users and store managers.

[1227] Specific examples

[1228] When a user visits the cafe and scans a QR code on the table with their smartphone, a web page for voice input is automatically displayed. When the user presses the "Start Recording" button and says something like, "The latte at this cafe was very tasty, but the order was a little late," the device begins recording the voice. When the user finishes recording and presses the "Send" button, the device sends the recorded data to the server.

[1229] The server converts the received voice data into text data using a generative AI model such as OpenAI's Whisper, and then analyzes the generated text data using an emotion engine such as IBM Watson, detecting positive emotions such as "The latte was very delicious" and negative emotions such as "The order was a little late." Based on this, the server generates a summary such as "The latte was very delicious" and stores it in a database.

[1230] The server sends the summary data to the terminal for the user to make a final confirmation, and when the user presses the "Confirmation Complete" button, the review is officially posted.

[1231] Prompt Sentence Examples

[1232] Example prompt sentence:

[1233] "Transcribe the following line and identify the sentiment: 'The latte at this cafe was very tasty, but the order was a little slow.'"

[1234] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1235] Step 1:

[1236] The user scans a QR code installed in a cafe or restaurant with their smartphone.

[1237] Input: QR code, smartphone

[1238] Output: URL of the web page for voice input

[1239] How it works: The user reads the QR code using a QR code reader app or the smartphone's built-in camera. The smartphone's browser automatically accesses the URL that was read and displays a web page for voice input.

[1240] Step 2:

[1241] The device displays a voice input interface.

[1242] Input: URL of the web page for voice input

[1243] Output: Audio input interface ("Start Recording", "Stop Recording", "Send" buttons)

[1244] What it does: The browser accesses the specified URL and displays the recording interface to the user, with "Start Recording," "Stop Recording," and "Send" buttons.

[1245] Step 3:

[1246] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's voice and temporarily stores it locally.

[1247] Input: User's voice, start recording operation

[1248] Output: Recorded audio data

[1249] Specific operation: When the user presses the "Start Recording" button, the device starts recording and captures the user's voice. The audio data is temporarily stored in the device's local memory until the recording is finished.

[1250] Step 4:

[1251] The user presses the "Stop Recording" button and then the "Send" button. The device sends the recorded audio data to the server via an HTTP POST request.

[1252] Input: Recorded voice data, sending operation

[1253] Output: Audio data sent to the server

[1254] Specific operation: When the user presses the "Stop Recording" button and then the "Send" button, the device creates an HTTP POST request and sends the recorded audio data to the server. The server receives this request.

[1255] Step 5:

[1256] The server sends the received voice data to a generative AI model, which converts it into text data.

[1257] Input: Audio data

[1258] Output: Text data

[1259] Specific operation: The server passes the voice data to the generative AI model, which then performs speech recognition and generates text data. For example, it uses OpenAI's Whisper to extract text information from the voice.

[1260] Step 6:

[1261] The server passes the text data to an emotion recognition means to recognize the user's emotion.

[1262] Input: Text data

[1263] Output: Emotional information (positive, negative)

[1264] Specific operation: The server sends the text data to an emotion recognition unit (e.g., IBM Watson) for emotion analysis. The emotion recognition unit extracts positive and negative emotions from the text data and returns the emotion information to the server.

[1265] Step 7:

[1266] The server extracts important information based on text data and emotional information, and filters out negative expressions and prohibited words.

[1267] Input: Text data, emotion information

[1268] Output: Summary data

[1269] Specific operation: The server considers emotional information, restructures the text to emphasize positive opinions, filters out negative expressions and NG words, and generates summary data.

[1270] Step 8:

[1271] The server stores the generated summary data in a database.

[1272] Input: Summary data, metadata (user ID, store ID, date and time, etc.)

[1273] Output: Saved summary data

[1274] Specific operation: When the server saves the generated summary data in the database, it also saves metadata such as user ID, store ID, and collection date, which makes the data unique and enables various searches.

[1275] Step 9:

[1276] The server sends the summary data back to the terminal and displays a final confirmation interface. The user checks the content and presses the "Confirm" button to officially post the review.

[1277] Input: Summary data

[1278] output: Final confirmation interface, officially posted review data

[1279] Specific operation: The server sends the data back to the device and displays a final confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted and made available in a saved location for other users and store managers to view.

[1280] (Application example 2)

[1281] 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."

[1282] With conventional voice input systems, when users provide feedback via voice, the content is not properly converted into text data, and sentiment analysis and summarization are not performed, resulting in problems with the content of the feedback not being accurately reflected. Another problem is that feedback containing negative expressions or prohibited words is made public as is. Furthermore, since there is no interface for users to perform a final review, the quality of the feedback cannot be guaranteed, and there are also issues with it not being able to be used as useful data for other users and store managers.

[1283] 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.

[1284] In this invention, the server includes a terminal means for a user to input voice data, a means for providing an interface for the user to input voice data, a server means for receiving the voice data, a generation means for converting the voice data into text data, an emotion identification means for identifying emotions based on the text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a provision means for providing the data stored in the storage means to the user, and a means for the provision means to provide an interface for the user to finalize the summary data. This allows user feedback to be accurately and quickly converted into text data and subjected to emotion analysis and summarization, thereby filtering out negative expressions and unwanted words and emphasizing positive opinions. Furthermore, providing an interface for the user to finalize the feedback ensures the quality of the feedback and allows it to be used as useful data for other users and store managers.

[1285] The "terminal for user voice input" refers to a device used by a user to input their own voice, and refers to a general mobile communication terminal such as a smartphone or tablet.

[1286] "Means for providing an interface" refers to a mechanism that provides the user with an operation screen and functions for voice input, and refers to a screen that includes buttons for starting and stopping recording, sending, etc.

[1287] The "server means for receiving voice data" is a server device for receiving voice data sent from a user via a network.

[1288] "Generative means for converting voice data into text data" refers to a mechanism for analyzing voice data and converting it into corresponding text data, including voice recognition software and generative AI models.

[1289] The "emotion identification means for identifying emotions based on text data" is a mechanism for identifying a user's emotions from the content of text data, and refers to an emotion analysis engine that uses natural language processing.

[1290] The "processing means for summarizing and analyzing text data" is a mechanism for extracting important information from text data and summarizing the content, and uses a natural language processing algorithm.

[1291] The "storage means for saving the summary data" is a mechanism for continuously storing the generated summary data, and includes databases and cloud storage.

[1292] The "means for providing the user with data stored in the storage means" refers to a mechanism that allows the user to view or use the stored data, and refers to a device that provides a viewing interface and notification functions.

[1293] "Means for providing an interface for users to make final confirmation of summary data" refers to a mechanism that provides an operation screen or function that allows users to make final confirmation of the generated summary data and approve or correct it.

[1294] This system allows users to easily input voice data, accurately converts the data into text, analyzes and summarizes emotions, and stores and provides them. This system includes a mobile communication device such as a smartphone or tablet, a server, a database, a generative AI model, and a sentiment analysis engine.

[1295] The user first uses the device to input voice data. Smartphones and tablets are suitable for this purpose. For example, the user can scan a QR code placed on a table in a store to view an interface dedicated to voice input. This interface provides a screen with buttons to start recording, stop recording, and send.

[1296] When a user records their voice feedback, the voice data is temporarily stored on the device. After recording is complete, the user presses the send button and the voice data is sent to the server via an HTTP POST request. The server receives the voice data and converts it into text using a generative AI model. This generative AI model can be, for example, the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[1297] The generated text data is passed to an emotion identification unit, which performs emotion analysis using natural language processing engines such as IBM Watson or Microsoft Azure Text Analytics, to identify positive and negative emotions.

[1298] The text data is then summarized and analyzed. Natural language processing algorithms are used to highlight positive opinions and filter out negative expressions and unwanted words. Important information is extracted and summary data is generated. This summary data is then stored in a database, which can be a storage medium. Databases such as Amazon RDS or MySQL can be used.

[1299] The saved summary data is provided to the user via the providing means. The user uses a smartphone or tablet to display an interface for final confirmation of the summary data. The interface is designed so that the user can confirm the content of the summary data again and press the "Confirmation Complete" button to officially post the review.

[1300] As a concrete example, a user scans a QR code on a table in a cafe with their smartphone and provides verbal feedback such as, "The food was very good, but the service was a little slow." This feedback is converted into text data by inputting the following prompt sentence into a generative AI model:

[1301] Audio data: 'The food was very good, but the service was a little slow.'

[1302] Convert it into text data.

[1303] This allows for accurate conversion of user feedback, rapid sentiment analysis, and summarization, and the system can be used as useful data for other users and store managers.

[1304] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1305] Step 1:

[1306] The user uses a smartphone or tablet to scan a QR code displayed on a table in the store, which opens a web page for voice input.

[1307] Input: QR code

[1308] Output: A web page dedicated to voice input is displayed.

[1309] What it does: When you use your smartphone camera to read a QR code, your browser opens the URL embedded in the QR code, and the web page pointed to by that URL is displayed on the screen.

[1310] Step 2:

[1311] The terminal provides the user with an interface for voice input, which includes a recording start button, a recording stop button, and a send button.

[1312] Input: Load web page

[1313] Output: Audio input interface

[1314] Specific behavior: A web page displays a voice input UI composed of HTML / CSS / JavaScript on the screen.

[1315] Step 3:

[1316] The user presses the start recording button to record the voice feedback, and when the recording is complete, presses the stop recording button to temporarily save the voice data.

[1317] Input: User voice input

[1318] Output: Temporarily saved audio data

[1319] Specific operation: When voice input begins, the smartphone's microphone captures the voice data and temporarily stores it in local storage.

[1320] Step 4:

[1321] When the user presses the send button, the terminal sends the audio data to the server using an HTTP POST request.

[1322] Input: Stored audio data

[1323] Output: Sending audio data to the server

[1324] Specific operation: Uses the HTTP library to send audio data to the specified URL on the server.

[1325] Step 5:

[1326] The server sends the received voice data to a generative AI model and converts it into text data.

[1327] Input: Audio data

[1328] Output: Text data

[1329] How it works: The server sends a request to the generative AI model via a REST API to generate text data from the audio data, using the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[1330] Step 6:

[1331] The server passes the generated text data to an emotion identification means for emotion analysis.

[1332] Input: Text data

[1333] Output: Emotional information

[1334] Specific operation: Text data is passed to a natural language processing engine (such as IBM Watson or Microsoft Azure Text Analytics) to extract sentiment information such as positive or negative.

[1335] Step 7:

[1336] The server summarizes and analyzes the text data and emotional information, extracts important information, filters out negative expressions and unwanted words, and creates summary data that emphasizes positive opinions.

[1337] Input: Text data, emotion information

[1338] Output: Summary data

[1339] What it does: It uses natural language processing algorithms to extract important information from text data and highlight and reframe positive opinions.

[1340] Step 8:

[1341] The generated summary data is saved in a database, along with metadata such as the user ID, store ID, and date and time.

[1342] Input: Abstract data, metadata

[1343] Output: Summary data stored in a database

[1344] What it does: It saves summary data and metadata to databases such as MySQL or Amazon RDS using INSERT queries.

[1345] Step 9:

[1346] The server transmits the saved summary data to the terminal and provides an interface for the user to make a final confirmation.

[1347] Input: Summary data retrieved from the database

[1348] Output: Interface for final confirmation

[1349] Specific operation: The saved summary data is sent to the terminal as an HTTP response and displayed in the summary display area of ​​the web page.

[1350] Step 10:

[1351] If the user makes a final check and there are no problems with the content, the review will be officially posted by pressing the "Confirm" button.

[1352] Input: User confirmation operation

[1353] Output: Review officially posted

[1354] Specific operation: Upon user action, the final confirmation completion information is sent to the server and finally made public to other users and store managers.

[1355] This process allows user feedback to be processed quickly and accurately, and summary data that reflects appropriate emotions is generated.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] [Fourth embodiment]

[1360] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1361] 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.

[1362] 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).

[1363] 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.

[1364] 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.

[1365] 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).

[1366] 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.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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."

[1373] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[1374] System Overview

[1375] 1. The user scans the QR code

[1376] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The QR code contains a URL for a web page that offers voice input functionality.

[1377] 2. Provides a voice input interface

[1378] The terminal displays a voice input interface, which includes a voice input button, a start recording button, a stop recording button, and a send button.

[1379] 3. Record your thoughts

[1380] The user presses the "Start Recording" button and begins speaking their thoughts aloud, which the device records and stores locally.

[1381] 4. Send audio data

[1382] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[1383] 5. Convert audio data to text

[1384] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The server then retrieves the converted text data.

[1385] 6. Summarizing and Analyzing Text Data

[1386] The server analyzes the text data, extracts important information, filters out negative expressions and NG words, and restructures the text to emphasize positive opinions.

[1387] 7. Save to database

[1388] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[1389] 8. Final Review and Submission

[1390] The server sends the refined review data to the device, and the user confirms the final review content. By pressing the "Confirmation Complete" button, the review is officially posted and becomes available for other users to view.

[1391] Specific examples

[1392] 1. User scans the QR code:

[1393] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[1394] 2. The voice input page will appear:

[1395] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[1396] 3. User records their thoughts:

[1397] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[1398] 4. Stop recording and send:

[1399] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[1400] 5. The server converts the audio data into text:

[1401] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[1402] 6. Summarize and analyze text data:

[1403] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[1404] 7. Save to database:

[1405] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[1406] 8. User finalizes post:

[1407] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[1408] This system allows users to easily post high-quality reviews by voice input, enabling real-time feedback and providing useful information to other users.

[1409] The processing flow will be explained below.

[1410] Step 1:

[1411] A user scans a QR code displayed at a restaurant or cafe with their smartphone. The smartphone interprets the URL written on the QR code and displays a web page for voice input in the browser.

[1412] Step 2:

[1413] The terminal provides the user with a voice input interface, which includes a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the recorded data.

[1414] Step 3:

[1415] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[1416] Step 4:

[1417] The user presses the "Stop" button. The device stops recording and saves the recording locally as a temporary file.

[1418] Step 5:

[1419] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request.

[1420] Step 6:

[1421] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[1422] Step 7:

[1423] The server calls the generative AI model and converts the stored voice data into text data. The AI ​​model converts the voice data into text and returns the results to the server.

[1424] Step 8:

[1425] The server analyzes the generated text data, extracts important information from it, filters out negative expressions and NG words, and restructures it to emphasize positive opinions.

[1426] Step 9:

[1427] The server saves the summarized and refined review data in a database, adding meta information such as date and time, store ID, and user ID.

[1428] Step 10:

[1429] The server sends the refined review data back to the device, which displays a final confirmation interface for the user to review.

[1430] Step 11:

[1431] The user confirms the final review content and presses the "Confirm" button. The server updates the status of the submission completion, and the review data is made available to other users and store managers in real time.

[1432] Example 1

[1433] 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."

[1434] Current consumer review and feedback systems often require users to manually input text, which is time-consuming and laborious. Furthermore, the quality of the reviews entered varies, and they often contain negative or inappropriate content. There is a need for a system that can easily and efficiently generate high-quality reviews by addressing these issues.

[1435] 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.

[1436] In this invention, the server includes an access means that allows a user to access using a QR code, a display means that displays a voice input interface provided by the access means, and a filtering means that filters out negative expressions and NG words and emphasizes positive opinions when summarizing and analyzing the voice data. This allows users to easily access using the QR code and quickly provide reviews through voice input, and the generated reviews are of high quality and emphasize positive expressions, making it possible to provide useful information to other users.

[1437] A "user" is an individual or consumer who utilizes the system to provide voice input.

[1438] "Input means" refers to a device or interface that allows a user to input voice information. Examples include smartphones and tablets.

[1439] "Server Means" refers to a central system for receiving and processing voice data, including functions such as data storage, conversion, and analysis.

[1440] "Generation means" refers to a program or algorithm used to convert voice data into text data. For example, a generative AI model is used.

[1441] "Processing means" refers to programs and algorithms for summarizing and analyzing text data.

[1442] "Storage means" refers to a storage device or database for storing the generated summary data.

[1443] "Providing means" refers to the interface or function for providing stored data to users or other systems.

[1444] "Device" means a device used by a user to provide voice input. Examples include smartphones and tablets.

[1445] A "database" refers to a system that stores information in an organized manner and makes it searchable.

[1446] "Access Method" refers to the function or method by which a user can access the system using a QR code.

[1447] "Display means" refers to a function or device for visually presenting the voice input interface to the user.

[1448] "Filtering measures" refer to processes that eliminate negative expressions and inappropriate words and emphasize positive opinions.

[1449] The present invention relates to a system that allows users to easily generate and share high-quality word-of-mouth reviews through voice input. This system allows users to access the system using a QR code, input voice data, convert the voice data into text data, summarize and analyze the data, and finally provide organized word-of-mouth data. A detailed description of specific embodiments of this system is provided below.

[1450] First, we will explain the hardware and software configuration of the system. This system includes a device (e.g., a smartphone, tablet, etc.) for users to input voice data, a server that receives and processes the voice data, a generative AI model that converts the voice data into text data, an algorithm for summarizing and analyzing the text data, a database that stores the generated summary data, and an interface that provides the stored data to users.

[1451] The following process is an example of a specific operation.

[1452] First, the user scans a QR code installed in a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that provides voice input functionality. The smartphone's browser automatically opens the web page, and the voice input screen is displayed. At this stage, the user presses the "Start Recording" button and voice-records their impressions of the cafe or restaurant. The device records the audio using the built-in microphone and saves the recording locally.

[1453] Next, when the user presses the "Send" button, the device uploads the recorded voice data to the server. The server receives the voice data and stores it in the appropriate directory. The server then passes the voice data to the generative AI model, which converts the voice into text data. The generative AI model uses a prompt sentence, such as "Please convert this voice data into text data."

[1454] The converted text data is then retrieved by a server, which then analyzes it to extract important information, filtering out negative expressions and inappropriate words, and restructuring the text to emphasize positive opinions.

[1455] Once the text data has been summarized and filtered, the server stores the summarized review data in a database. The stored data also includes meta information such as the collection date, store ID, and user ID. The server then sends the refined review data back to the device, where the user can confirm the final review content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[1456] This system allows users to easily access the system using a QR code and quickly provide reviews through voice input. The generated reviews are of high quality and emphasize positive expressions, providing useful information for other users.

[1457] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1458] Step 1:

[1459] The user scans the QR code

[1460] A user scans a QR code displayed at a cafe or restaurant with their smartphone camera. The QR code contains the URL of a web page that offers voice input functionality. The scanned URL is entered, and the smartphone's browser automatically opens the web page.

[1461] Input: QR code read by the camera

[1462] Output: Web page with voice input function

[1463] Step 2:

[1464] Show voice input interface

[1465] The terminal displays a voice input interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button, and the user uses this interface to input voice.

[1466] Input: The browser that opened the web page

[1467] Output: Audio input interface

[1468] Step 3:

[1469] Record your thoughts

[1470] The user presses the "Start Recording" button, and the device records audio using the built-in microphone. The user then enters their impressions of the restaurant or cafe by voice. Recording can be stopped by pressing the "Stop Recording" button.

[1471] Input: Press the start recording button, input voice

[1472] Output: Recorded audio data

[1473] Step 4:

[1474] Send audio data

[1475] When the user presses the "Send" button, the device uploads the recorded audio data to the server, which receives the audio data and stores it in the appropriate directory.

[1476] Input: Pressing the send button, recorded voice data

[1477] Output: Audio data stored on the server

[1478] Step 5:

[1479] Converting voice data to text

[1480] The server passes the received voice data to the generative AI model, which converts the voice data into text data. The generative AI model uses a prompt saying, "Please convert this voice data into text data." The text data is returned from the generative AI model and retrieved by the server.

[1481] Input: Recorded voice data, prompt "Convert this voice data into text data"

[1482] Output: Converted text data

[1483] Step 6:

[1484] Summarize and analyze text data

[1485] The server analyzes the acquired text data, extracts important information, filters out negative expressions and inappropriate words, and emphasizes positive opinions.

[1486] Input: Converted text data

[1487] Output: Summarized and analyzed text data

[1488] Step 7:

[1489] Store in database

[1490] The server stores the summarized and refined review data in a database, including meta information such as the collection date, store ID, and user ID.

[1491] Input: Summarized and refined text data

[1492] Output: Review data stored in the database

[1493] Step 8:

[1494] Final check and submission

[1495] The server sends the refined review data back to the device, where the user can confirm the content. When the user presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[1496] Input: Summarized and refined text data, user confirmation

[1497] Output: Officially posted review data

[1498] (Application example 1)

[1499] 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."

[1500] Existing brick-and-mortar stores lack the means to efficiently collect and analyze customer feedback in a high-quality manner. Traditional surveys and feedback systems are time-consuming and tend to limit the amount of data collected. Furthermore, due to a lack of an easy-to-use interface for customers, the quality and quantity of feedback is often insufficient.

[1501] 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.

[1502] In this invention, the server includes an input means for users to input voice data, a means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server. This allows users to easily provide feedback by voice in physical stores, and makes it possible to efficiently collect, analyze, store, and provide high-quality word-of-mouth data.

[1503] "User" refers to an individual or organization that uses the system to provide voice input and feedback.

[1504] "Input means" refers to a device or interface that allows a user to input voice information.

[1505] "Server" refers to a computer system for receiving and processing audio data.

[1506] "Generation means" refers to software or algorithms for converting audio data into text data.

[1507] "Processing means" refers to software and algorithms for summarizing and analyzing text data.

[1508] "Storage means" refers to a database or storage system for storing the generated summary data.

[1509] "Presentation means" refers to the interface or means for presenting stored data to users or other systems.

[1510] "QR Code" refers to a visual code that encodes information for accessing a voice input interface.

[1511] "Communication means" refers to a network interface for exchanging voice data and processing results with the server.

[1512] "Generative AI model" refers to an artificial intelligence model used to convert voice data into text data.

[1513] "Summary data" refers to information that summarizes text data generated from audio data.

[1514] "Final confirmation means" refers to an interface that allows the user to confirm the summary data and confirm the posting.

[1515] A system embodying this invention includes an input means for a user to input voice data, a server means for receiving voice data, a generation means for converting the voice data into text data, a processing means for summarizing and analyzing the text data, a storage means for providing the stored data to the user, a means for reading a QR code and displaying a voice input interface, a communication means for transmitting the voice data to the server, and a means for final confirmation and posting of the summary data obtained from the server.

[1516] Basic system configuration

[1517] 1. Input means (terminal):

[1518] Users use devices such as smartphones and tablets to read QR codes installed in restaurants and cafes, which contain the URL of a web page that offers voice input functionality.

[1519] The terminal displays a voice input interface to prompt the user to input voice, and the interface includes a voice input button, a recording start button, a recording stop button, and a send button.

[1520] 2. Server means:

[1521] Receives audio data sent from the terminal and stores it in the appropriate directory.

[1522] The server passes the voice data to a generative AI model (e.g., OpenAI's Whisper or Deepgram), which converts the voice data into text data.

[1523] 3. Generation means:

[1524] Using a generative AI model running on the server, voice data is converted into text data with high accuracy.

[1525] Specific generative AI models include Whisper and Deepgram.

[1526] 4. Processing Method:

[1527] The server analyzes the text data and extracts important information. It then filters out negative expressions and unwanted words and restructures the text to emphasize positive opinions.

[1528] The server stores the analysis results as summary data.

[1529] 5. Preservation means:

[1530] The server stores the summarized and refined review data in a database, including meta-information such as the collection date, store ID, and user ID.

[1531] 6. Means of provision:

[1532] The server sends the refined review data back to the device for final confirmation and posting. Once the user confirms and presses the "Confirm" button, the review is officially posted and becomes available for other users to view.

[1533] Specific use cases

[1534] scenario

[1535] A user enters a cafe, enjoys their latte, and then scans the QR code on the table to post feedback.

[1536] 1. User scans the QR code:

[1537] A user scans a QR code on a cafe table with their smartphone, and the smartphone's browser opens, displaying a voice input page.

[1538] 2. The voice input page will appear:

[1539] The device will display the voice input interface, with "Start Recording," "Stop," and "Send" buttons on the screen.

[1540] 3. User records their thoughts:

[1541] The user presses the "Start Recording" button and voice-records, "The latte at this cafe was very tasty, but it took a while from the time I ordered until it was served."

[1542] 4. Stop recording and send:

[1543] The user presses the "Stop" button, then the "Send" button. The device sends the audio data to the server.

[1544] 5. The server converts the audio data into text:

[1545] The server passes the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but it took a while for it to be served after I ordered it."

[1546] 6. Summarize and analyze text data:

[1547] The server analyzes the text and highlights the positive aspects, such as "the latte is delicious," while filtering out the negative aspects.

[1548] 7. Save to database:

[1549] The server saves the summarized review data, such as "The latte is very delicious," in a database. When saving, meta information such as the date and time and the store ID is also added.

[1550] 8. User finalizes post:

[1551] The server sends the summary data back to the terminal, where the user can confirm it. Once the "Confirmation Complete" button is pressed, the review is officially posted.

[1552] Examples of prompt statements

[1553] Example prompts to be input to the generative AI model:

[1554] Voice input: "The latte at this cafe was very good, but it took a while for it to be served."

[1555] Prompt for generative AI model: "Turn the following audio recording into text, highlighting the positive points and summarizing it. Please tone down the negative aspects.

[1556] Audio recording: {audio data}

[1557] """

[1558] Audio data: "The latte at this cafe was very tasty, but it took a while for it to be served after I ordered it."

[1559] """

[1560] Using this prompt, the audio data is converted into text and a summary is generated that highlights positive words.

[1561] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1562] Step 1:

[1563] The user scans the QR code.

[1564] In this step, a user uses their smartphone camera to scan a QR code displayed in a physical store. The QR code contains the URL of a web page that provides voice input functionality, and scanning the code retrieves the URL. The input is the user scanning the QR code, and the output is the URL of the voice input page.

[1565] Step 2:

[1566] The device displays a voice input interface.

[1567] The device opens a browser using the URL obtained in step 1 and displays a voice input interface. This interface includes a voice input button, a start recording button, a stop recording button, and a send button. The input is the URL read from the QR code, and the output is the display of the voice input interface.

[1568] Step 3:

[1569] The user records their impressions.

[1570] The user presses the "Start Recording" button on the voice input interface and starts speaking. The device records the voice and stores it locally. The input is the user's voice, and the output is the recorded voice data.

[1571] Step 4:

[1572] The user transmits voice data.

[1573] When the user presses the "Stop" button and then the "Send" button, the device uploads the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server.

[1574] Step 5:

[1575] The server converts the audio data into text.

[1576] The server passes the received voice data to a generative AI model, which converts the voice data into text data. Examples of generative AI models used include OpenAI's Whisper and Deepgram. The input is voice data, and the output is generated text data.

[1577] Step 6:

[1578] The server summarizes and analyzes the text data.

[1579] The server analyzes the text data and extracts important information. The server filters out negative expressions and unwanted words and restructures the data to emphasize positive opinions. The input is the generated text data, and the output is the summarized and restructured text data.

[1580] Step 7:

[1581] The server stores the summarized data in a database.

[1582] The server stores the summarized and refined review data in a database. When saving, meta information such as date and time, store ID, and user ID is also added. The input is the summarized and reconstructed text data, and the output is the review data stored in the database.

[1583] Step 8:

[1584] The server sends the summary data to the terminal, and the user makes a final confirmation.

[1585] The server sends the refined review data back to the user's device, where the user performs a final confirmation. When the user presses the "Confirmation Complete" button, the review is officially posted. The input is the summarized and reconstructed review data and the user's confirmation operation, and the output is the officially posted review.

[1586] 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.

[1587] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion engine for recognizing the user's emotions, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described below.

[1588] System Overview

[1589] 1. Scan the QR code

[1590] The user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone's browser opens the URL and displays a web page designed specifically for voice input.

[1591] 2. Present voice input interface

[1592] The device provides the user with a recording interface, which includes a "Start Recording" button, a "Stop Recording" button, and a "Send" button.

[1593] 3. Recording your impressions

[1594] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's thoughts and temporarily stores them locally.

[1595] 4. Sending audio data

[1596] When the user presses the "Send" button, the device sends the recorded audio data to the server via an HTTP POST request. The server receives the audio data.

[1597] 5. Convert audio data into text data

[1598] The server sends the received voice data to the generative AI model, which converts it into text data. The generative AI model extracts text information from the voice and generates text data.

[1599] 6. Sentiment Analysis of Text Data

[1600] The server passes the text data to the emotion engine to recognize the user's emotions. The emotion engine extracts positive and negative emotions from the text data and returns the emotion information to the server.

[1601] 7. Summarizing and Analyzing Text Data

[1602] The server extracts important information based on the text data and emotional information, filters out negative expressions and unwanted words, and reconstructs the data to emphasize positive opinions, creating summary data.

[1603] 8. Saving Summary Data

[1604] The server saves the generated summary data in a database, along with metadata such as the user ID, store ID, and date and time.

[1605] 9. Review and Final Submission

[1606] The server then sends the saved summary data back to the terminal and displays a final confirmation interface. The user confirms the content and presses the "Confirm" button to officially post the review. The saved data is made available for viewing by other users and store managers.

[1607] Specific examples

[1608] 1. User scans the QR code

[1609] Users scan a QR code placed on a cafe table with their smartphone, and a web page for voice input is displayed.

[1610] 2. The voice input page will appear

[1611] The device provides a voice input interface that displays "Start Recording," "Stop Recording," and "Send" buttons.

[1612] 3. User Records Their Thoughts

[1613] The user presses the "Start Recording" button and says, "The latte at this cafe was very tasty, but the order was a little late." The device records this comment and stores it locally.

[1614] 4. Stop recording and send

[1615] After the user presses the "Stop" button, he or she presses the "Send" button, and the terminal sends the recorded voice data to the server.

[1616] 5. The server converts the voice data into text

[1617] The server sends the voice data to a generative AI model, which converts it into text data such as, "The latte at this cafe was very delicious, but the order was a little late."

[1618] 6. Emotional Recognition

[1619] The server passes the text data to an emotion engine, which recognizes the positive emotion "The latte was very tasty" and the negative emotion "The order was a little late."

[1620] 7. Summarizing and Analyzing Text Data

[1621] The server emphasizes the positive comments, summarizing them as "the latte is very tasty," and filters out the negative ones.

[1622] 8. Save to database

[1623] The server saves the summary data, "The latte is very tasty," in a database along with metadata such as the collection date, store ID, and user ID.

[1624] 9. User finalizes post

[1625] The server sends the summary data back to the terminal, and the user finally confirms the content. When the user presses the "Confirm" button, the review is officially posted and made public to other users and store managers.

[1626] This system allows users to easily post high-quality reviews, while at the same time highlighting positive feedback, providing extremely useful data for other users and stores.

[1627] The processing flow will be explained below.

[1628] Step 1:

[1629] A user scans a QR code placed on a table at a restaurant or cafe with their smartphone. The smartphone interprets the URL written in the QR code and displays a web page for voice input in the browser.

[1630] Step 2:

[1631] The terminal provides the user with a voice input interface, which displays a "Start Recording" button to start recording, a "Stop" button to stop recording, and a "Send" button to send the voice data.

[1632] Step 3:

[1633] The user presses the "Start Recording" button. The device activates the microphone and begins recording the user's voice input. The user then provides their voice about their experience and impressions at the store.

[1634] Step 4:

[1635] The user presses the "Stop" button. The device stops recording and saves the recording locally. The audio data is saved as a temporary file on the device.

[1636] Step 5:

[1637] The user presses the "Send" button. The device sends the recorded audio data to the server using an HTTP POST request. The request includes metadata (e.g., store ID, user ID) along with the audio data.

[1638] Step 6:

[1639] The server receives the audio data and saves it in the appropriate directory. The server confirms receipt of the audio data and begins further processing.

[1640] Step 7:

[1641] The server calls the generative AI model and converts the saved voice data into text data. The AI ​​model converts the voice data into text, and the server receives the converted text data.

[1642] Step 8:

[1643] The server passes the text data to the emotion engine to analyze the user's emotions. The emotion engine extracts positive and negative emotions from the text and returns the results to the server.

[1644] Step 9:

[1645] The server processes the text data based on the results from the emotion engine, filtering out negative expressions and unwanted words and restructuring the data to emphasize positive opinions.

[1646] Step 10:

[1647] The server saves the summarized and refined review data in a database, along with metadata such as date and time, store ID, and user ID.

[1648] Step 11:

[1649] The server sends the refined review data to the terminal, which displays a final confirmation interface so that the user can check the content.

[1650] Step 12:

[1651] The user confirms the final review content and presses the "Confirm" button. The server updates the submission status, and the new review data is made available to other users and store managers in real time.

[1652] This detailed processing flow allows users to easily post high-quality reviews and allows stores to receive useful feedback in real time.

[1653] Example 2

[1654] 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."

[1655] Conventional review posting systems require users to manually input text, which is time-consuming and has the problem of not fully reflecting emotional expressions. There is also a risk that negative or inappropriate language may be posted. This leads to issues such as a decrease in user convenience and the quality of posted data.

[1656] 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.

[1657] In this invention, the server includes a generating means for converting voice data into text data, an emotion recognizing means for performing emotion analysis on the text data, and a processing means for summarizing and analyzing the text data. This allows users to easily post reviews by voice, and also enables the generation of high-quality word-of-mouth data by emphasizing positive opinions while filtering out negative expressions and inappropriate words.

[1658] "Input means" refers to a device or interface that a user uses to make voice input.

[1659] "Server Means" refers to a central management system for receiving and processing voice and other data.

[1660] "Generation means" refers to a function or device for converting voice data into text data.

[1661] "Emotion recognition means" refers to a function or device for analyzing and detecting a user's emotions from text data.

[1662] "Processing means" refers to functions or devices for summarizing and analyzing text data.

[1663] "Storage means" refers to a function or device for storing the generated summary data and other related data.

[1664] "Providing means" refers to the functions and devices for providing stored data to users and other parties.

[1665] "Terminal" refers to a personal electronic device that a user uses to provide voice input.

[1666] "Database" refers to a system for centrally storing and managing summarized review data and other related information.

[1667] The present invention is a system including an input means (e.g., a terminal such as a smartphone or tablet) for a user to input voice, a server means for receiving and processing voice data, a generation means for converting voice data into text data, an emotion recognition means for recognizing a user's emotion, a processing means for summarizing and analyzing the text data, a storage means for storing the generated summary data, and a provision means for providing the stored data to a user. Specific embodiments of the system are described in detail below.

[1668] First, the user scans a QR code displayed at a restaurant or cafe with a smartphone or other device. When the scanned URL is accessed, a web page for voice input is displayed. This web page has buttons for "Start Recording," "Stop Recording," and "Send."

[1669] When the user presses the "Start Recording" button and begins speaking, the device records the user's voice and temporarily stores it locally. When the user finishes recording, they press the "Stop Recording" button, and then the "Send" button, and the device sends the voice data to the server.

[1670] The server sends the received voice data to a generative AI model (such as OpenAI's Whisper), which converts it into text data. The generated text data is then passed to an emotion recognition means (such as IBM Watson), where sentiment analysis is performed to identify positive and negative emotions. After receiving the results of the sentiment analysis, the server uses a processing means to summarize and analyze the text data, extracting important information and filtering out negative expressions and NG words. A reconstructed summary is generated that emphasizes positive opinions.

[1671] The generated summary data is stored in a database by the server. This data is also saved along with metadata such as the user ID, store ID, and collection date. The server then sends the summary data back to the terminal and displays a confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted. The saved data is made available for viewing by other users and store managers.

[1672] Specific examples

[1673] When a user visits the cafe and scans a QR code on the table with their smartphone, a web page for voice input is automatically displayed. When the user presses the "Start Recording" button and says something like, "The latte at this cafe was very tasty, but the order was a little late," the device begins recording the voice. When the user finishes recording and presses the "Send" button, the device sends the recorded data to the server.

[1674] The server converts the received voice data into text data using a generative AI model such as OpenAI's Whisper, and then analyzes the generated text data using an emotion engine such as IBM Watson, detecting positive emotions such as "The latte was very delicious" and negative emotions such as "The order was a little late." Based on this, the server generates a summary such as "The latte was very delicious" and stores it in a database.

[1675] The server sends the summary data to the terminal for the user to make a final confirmation, and when the user presses the "Confirmation Complete" button, the review is officially posted.

[1676] Prompt Sentence Examples

[1677] Example prompt sentence:

[1678] "Transcribe the following line and identify the sentiment: 'The latte at this cafe was very tasty, but the order was a little slow.'"

[1679] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1680] Step 1:

[1681] The user scans a QR code installed in a cafe or restaurant with their smartphone.

[1682] Input: QR code, smartphone

[1683] Output: URL of the web page for voice input

[1684] How it works: The user reads the QR code using a QR code reader app or the smartphone's built-in camera. The smartphone's browser automatically accesses the URL that was read and displays a web page for voice input.

[1685] Step 2:

[1686] The device displays a voice input interface.

[1687] Input: URL of the web page for voice input

[1688] Output: Audio input interface ("Start Recording", "Stop Recording", "Send" buttons)

[1689] What it does: The browser accesses the specified URL and displays the recording interface to the user, with "Start Recording," "Stop Recording," and "Send" buttons.

[1690] Step 3:

[1691] The user presses the "Start Recording" button and begins speaking their thoughts. The device records the user's voice and temporarily stores it locally.

[1692] Input: User's voice, start recording operation

[1693] Output: Recorded audio data

[1694] Specific operation: When the user presses the "Start Recording" button, the device starts recording and captures the user's voice. The audio data is temporarily stored in the device's local memory until the recording is finished.

[1695] Step 4:

[1696] The user presses the "Stop Recording" button and then the "Send" button. The device sends the recorded audio data to the server via an HTTP POST request.

[1697] Input: Recorded voice data, sending operation

[1698] Output: Audio data sent to the server

[1699] Specific operation: When the user presses the "Stop Recording" button and then the "Send" button, the device creates an HTTP POST request and sends the recorded audio data to the server. The server receives this request.

[1700] Step 5:

[1701] The server sends the received voice data to a generative AI model, which converts it into text data.

[1702] Input: Audio data

[1703] Output: Text data

[1704] Specific operation: The server passes the voice data to the generative AI model, which then performs speech recognition and generates text data. For example, it uses OpenAI's Whisper to extract text information from the voice.

[1705] Step 6:

[1706] The server passes the text data to an emotion recognition means to recognize the user's emotion.

[1707] Input: Text data

[1708] Output: Emotional information (positive, negative)

[1709] Specific operation: The server sends the text data to an emotion recognition unit (e.g., IBM Watson) for emotion analysis. The emotion recognition unit extracts positive and negative emotions from the text data and returns the emotion information to the server.

[1710] Step 7:

[1711] The server extracts important information based on text data and emotional information, and filters out negative expressions and prohibited words.

[1712] Input: Text data, emotion information

[1713] Output: Summary data

[1714] Specific operation: The server considers emotional information, restructures the text to emphasize positive opinions, filters out negative expressions and NG words, and generates summary data.

[1715] Step 8:

[1716] The server stores the generated summary data in a database.

[1717] Input: Summary data, metadata (user ID, store ID, date and time, etc.)

[1718] Output: Saved summary data

[1719] Specific operation: When the server saves the generated summary data in the database, it also saves metadata such as user ID, store ID, and collection date, which makes the data unique and enables various searches.

[1720] Step 9:

[1721] The server sends the summary data back to the terminal and displays a final confirmation interface. The user checks the content and presses the "Confirm" button to officially post the review.

[1722] Input: Summary data

[1723] output: Final confirmation interface, officially posted review data

[1724] Specific operation: The server sends the data back to the device and displays a final confirmation interface for the user. Once the user confirms the content and presses the "Confirm" button, the review is officially posted and made available in a saved location for other users and store managers to view.

[1725] (Application example 2)

[1726] 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."

[1727] With conventional voice input systems, when users provide feedback via voice, the content is not properly converted into text data, and sentiment analysis and summarization are not performed, resulting in problems with the content of the feedback not being accurately reflected. Another problem is that feedback containing negative expressions or prohibited words is made public as is. Furthermore, since there is no interface for users to perform a final review, the quality of the feedback cannot be guaranteed, and there are also issues with it not being able to be used as useful data for other users and store managers.

[1728] 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.

[1729] In this invention, the server includes a terminal means for a user to input voice data, a means for providing an interface for the user to input voice data, a server means for receiving the voice data, a generation means for converting the voice data into text data, an emotion identification means for identifying emotions based on the text data, a processing means for summarizing and analyzing the text data, a storage means for saving the generated summary data, a provision means for providing the data stored in the storage means to the user, and a means for the provision means to provide an interface for the user to finalize the summary data. This allows user feedback to be accurately and quickly converted into text data and subjected to emotion analysis and summarization, thereby filtering out negative expressions and unwanted words and emphasizing positive opinions. Furthermore, providing an interface for the user to finalize the feedback ensures the quality of the feedback and allows it to be used as useful data for other users and store managers.

[1730] The "terminal for user voice input" refers to a device used by a user to input their own voice, and refers to a general mobile communication terminal such as a smartphone or tablet.

[1731] "Means for providing an interface" refers to a mechanism that provides the user with an operation screen and functions for voice input, and refers to a screen that includes buttons for starting and stopping recording, sending, etc.

[1732] The "server means for receiving voice data" is a server device for receiving voice data sent from a user via a network.

[1733] "Generative means for converting voice data into text data" refers to a mechanism for analyzing voice data and converting it into corresponding text data, including voice recognition software and generative AI models.

[1734] The "emotion identification means for identifying emotions based on text data" is a mechanism for identifying a user's emotions from the content of text data, and refers to an emotion analysis engine that uses natural language processing.

[1735] The "processing means for summarizing and analyzing text data" is a mechanism for extracting important information from text data and summarizing the content, and uses a natural language processing algorithm.

[1736] The "storage means for saving the summary data" is a mechanism for continuously storing the generated summary data, and includes databases and cloud storage.

[1737] The "means for providing the user with data stored in the storage means" refers to a mechanism that allows the user to view or use the stored data, and refers to a device that provides a viewing interface and notification functions.

[1738] "Means for providing an interface for users to make final confirmation of summary data" refers to a mechanism that provides an operation screen or function that allows users to make final confirmation of the generated summary data and approve or correct it.

[1739] This system allows users to easily input voice data, accurately converts the data into text, analyzes and summarizes emotions, and stores and provides them. This system includes a mobile communication device such as a smartphone or tablet, a server, a database, a generative AI model, and a sentiment analysis engine.

[1740] The user first uses the device to input voice data. Smartphones and tablets are suitable for this purpose. For example, the user can scan a QR code placed on a table in a store to view an interface dedicated to voice input. This interface provides a screen with buttons to start recording, stop recording, and send.

[1741] When a user records their voice feedback, the voice data is temporarily stored on the device. After recording is complete, the user presses the send button and the voice data is sent to the server via an HTTP POST request. The server receives the voice data and converts it into text using a generative AI model. This generative AI model can be, for example, the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[1742] The generated text data is passed to an emotion identification unit, which performs emotion analysis using natural language processing engines such as IBM Watson or Microsoft Azure Text Analytics, to identify positive and negative emotions.

[1743] The text data is then summarized and analyzed. Natural language processing algorithms are used to highlight positive opinions and filter out negative expressions and unwanted words. Important information is extracted and summary data is generated. This summary data is then stored in a database, which can be a storage medium. Databases such as Amazon RDS or MySQL can be used.

[1744] The saved summary data is provided to the user via the providing means. The user uses a smartphone or tablet to display an interface for final confirmation of the summary data. The interface is designed so that the user can confirm the content of the summary data again and press the "Confirmation Complete" button to officially post the review.

[1745] As a concrete example, a user scans a QR code on a table in a cafe with their smartphone and provides verbal feedback such as, "The food was very good, but the service was a little slow." This feedback is converted into text data by inputting the following prompt sentence into a generative AI model:

[1746] Audio data: 'The food was very good, but the service was a little slow.'

[1747] Convert it into text data.

[1748] This allows for accurate conversion of user feedback, rapid sentiment analysis, and summarization, and the system can be used as useful data for other users and store managers.

[1749] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1750] Step 1:

[1751] The user uses a smartphone or tablet to scan a QR code displayed on a table in the store, which opens a web page for voice input.

[1752] Input: QR code

[1753] Output: A web page dedicated to voice input is displayed.

[1754] What it does: When you use your smartphone camera to read a QR code, your browser opens the URL embedded in the QR code, and the web page pointed to by that URL is displayed on the screen.

[1755] Step 2:

[1756] The terminal provides the user with an interface for voice input, which includes a recording start button, a recording stop button, and a send button.

[1757] Input: Load web page

[1758] Output: Audio input interface

[1759] Specific behavior: A web page displays a voice input UI composed of HTML / CSS / JavaScript on the screen.

[1760] Step 3:

[1761] The user presses the start recording button to record the voice feedback, and when the recording is complete, presses the stop recording button to temporarily save the voice data.

[1762] Input: User voice input

[1763] Output: Temporarily saved audio data

[1764] Specific operation: When voice input begins, the smartphone's microphone captures the voice data and temporarily stores it in local storage.

[1765] Step 4:

[1766] When the user presses the send button, the terminal sends the audio data to the server using an HTTP POST request.

[1767] Input: Stored audio data

[1768] Output: Sending audio data to the server

[1769] Specific operation: Uses the HTTP library to send audio data to the specified URL on the server.

[1770] Step 5:

[1771] The server sends the received voice data to a generative AI model and converts it into text data.

[1772] Input: Audio data

[1773] Output: Text data

[1774] How it works: The server sends a request to the generative AI model via a REST API to generate text data from the audio data, using the Google Cloud Speech-to-Text API or the Watson Speech to Text API.

[1775] Step 6:

[1776] The server passes the generated text data to an emotion identification means for emotion analysis.

[1777] Input: Text data

[1778] Output: Emotional information

[1779] Specific operation: Text data is passed to a natural language processing engine (such as IBM Watson or Microsoft Azure Text Analytics) to extract sentiment information such as positive or negative.

[1780] Step 7:

[1781] The server summarizes and analyzes the text data and emotional information, extracts important information, filters out negative expressions and unwanted words, and creates summary data that emphasizes positive opinions.

[1782] Input: Text data, emotion information

[1783] Output: Summary data

[1784] What it does: It uses natural language processing algorithms to extract important information from text data and highlight and reframe positive opinions.

[1785] Step 8:

[1786] The generated summary data is saved in a database, along with metadata such as the user ID, store ID, and date and time.

[1787] Input: Abstract data, metadata

[1788] Output: Summary data stored in a database

[1789] What it does: It saves summary data and metadata to databases such as MySQL or Amazon RDS using INSERT queries.

[1790] Step 9:

[1791] The server transmits the saved summary data to the terminal and provides an interface for the user to make a final confirmation.

[1792] Input: Summary data retrieved from the database

[1793] Output: Interface for final confirmation

[1794] Specific operation: The saved summary data is sent to the terminal as an HTTP response and displayed in the summary display area of ​​the web page.

[1795] Step 10:

[1796] If the user makes a final check and there are no problems with the content, the review will be officially posted by pressing the "Confirm" button.

[1797] Input: User confirmation operation

[1798] Output: Review officially posted

[1799] Specific operation: Upon user action, the final confirmation completion information is sent to the server and finally made public to other users and store managers.

[1800] This process allows user feedback to be processed quickly and accurately, and summary data that reflects appropriate emotions is generated.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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.

[1806] 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.

[1807] 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).

[1808] 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.

[1809] 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."

[1810] 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.

[1811] 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).

[1812] 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.

[1813] 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.

[1814] 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.

[1815] 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.

[1816] 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.

[1817] 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.

[1818] 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.

[1819] 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.

[1820] 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.

[1821] 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.

[1822] The following is further disclosed regarding the above embodiment.

[1823] (Claim 1)

[1824] an input means for a user to input voice;

[1825] a server means for receiving voice data;

[1826] A generating means for converting the voice data into text data;

[1827] processing means for summarizing and analyzing the text data;

[1828] a storage means for storing the generated summary data;

[1829] providing means for providing the data stored in said storage means to a user;

[1830] A system including:

[1831] (Claim 2)

[1832] a terminal for a user to input voice;

[1833] a server that receives the voice data transmitted from the terminal and converts and summarizes the voice data;

[1834] A database storing word-of-mouth data summarized by the server.

[1835] 10. The system of claim 1, comprising:

[1836] (Claim 3)

[1837] This includes filtering out negative expressions and NG words from the user's voice input and emphasizing positive opinions.

[1838] 10. The system of claim 1.

[1839] "Example 1"

[1840] (Claim 1)

[1841] an input means for a user to input voice;

[1842] a server means for receiving voice data;

[1843] A generating means for converting the voice data into text data;

[1844] processing means for summarizing and analyzing the text data;

[1845] a storage means for storing the generated summary data;

[1846] providing means for providing the data stored in the storage means to a user;

[1847] An access means for a user to access using a QR code;

[1848] a display means for displaying a voice input interface provided by the access means;

[1849] A filtering means for filtering out negative expressions and NG words and emphasizing positive opinions when summarizing and analyzing the voice data.

[1850] A system including:

[1851] (Claim 2)

[1852] a terminal for a user to input voice;

[1853] a server that receives the voice data transmitted from the terminal and converts and summarizes the voice data;

[1854] A database storing word-of-mouth data summarized by the server.

[1855] 10. The system of claim 1, comprising:

[1856] (Claim 3)

[1857] This includes filtering out negative expressions and NG words from the user's voice input and emphasizing positive opinions.

[1858] 10. The system of claim 1.

[1859] "Application Example 1"

[1860] (Claim 1)

[1861] an input means for a user to input voice;

[1862] a server means for receiving voice data;

[1863] A generating means for converting the voice data into text data;

[1864] processing means for summarizing and analyzing the text data;

[1865] a storage means for storing the generated summary data;

[1866] providing means for providing the data stored in the storage means to a user;

[1867] means for reading the QR code and displaying a voice input interface;

[1868] a communication means for transmitting voice data to a server;

[1869] means for final review and posting of the abstract data retrieved from the server;

[1870] A system including:

[1871] (Claim 2)

[1872] a terminal for a user to input voice;

[1873] a server that receives the voice data transmitted from the terminal and converts and summarizes the voice data;

[1874] a database for storing the word-of-mouth data summarized by the server;

[1875] a reading means for reading the QR code;

[1876] A means for summarizing audio data using a generative AI model;

[1877] a means for reviewing and posting summary data;

[1878] 10. The system of claim 1, comprising:

[1879] (Claim 3)

[1880] A means for filtering out negative expressions and NG words from the user's voice input and emphasizing positive opinions;

[1881] means for final review and posting of the abstract data retrieved from the server;

[1882] 10. The system of claim 1, comprising:

[1883] "Example 2: Combining Emotion Engines"

[1884] (Claim 1)

[1885] an input means for a user to input voice;

[1886] a server means for receiving voice data;

[1887] A generating means for converting the voice data into text data;

[1888] emotion recognition means for performing emotion analysis of the text data;

[1889] processing means for summarizing and analyzing the text data;

[1890] a storage means for storing the generated summary data;

[1891] providing means for providing the data stored in said storage means to a user;

[1892] A system including:

[1893] (Claim 2)

[1894] a terminal for a user to input voice;

[1895] a server that receives the voice data transmitted from the terminal and converts and summarizes the voice data;

[1896] A database storing word-of-mouth data summarized by the server.

[1897] 10. The system of claim 1, comprising:

[1898] (Claim 3)

[1899] This includes filtering out negative expressions and NG words from the user's voice input and emphasizing positive opinions.

[1900] 10. The system of claim 1.

[1901] "Application example 2 when combining emotion engines"

[1902] (Claim 1)

[1903] a terminal means for a user to input voice;

[1904] means for providing an interface for the user to provide voice input;

[1905] a server means for receiving the voice data;

[1906] A generating means for converting the voice data into text data;

[1907] emotion identification means for identifying emotions based on the text data;

[1908] processing means for summarizing and analyzing the text data;

[1909] a storage means for storing the generated summary data;

[1910] providing means for providing the data stored in the storage means to a user;

[1911] a means for providing an interface for the user to finalize the summary data, by said providing means;

[1912] A system including:

[1913] (Claim 2)

[1914] a terminal for a user to input voice;

[1915] a server that receives the voice data transmitted from the terminal and converts and summarizes the voice data;

[1916] A database for storing the word-of-mouth data summarized by the server and a means for providing an interface for a user to finalize the summarized data.

[1917] 10. The system of claim 1, comprising:

[1918] (Claim 3)

[1919] The method includes a process for filtering negative expressions and NG words from the user's voice input and emphasizing positive opinions, and a means for providing an interface for the user to finalize the summary data.

[1920] 10. The system of claim 1. [Explanation of symbols]

[1921] 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. an input means for a user to input voice; a server means for receiving voice data; A generating means for converting the voice data into text data; processing means for summarizing and analyzing the text data; a storage means for storing the generated summary data; providing means for providing the data stored in said storage means to a user; A system including:

2. a terminal for a user to input voice; a server that receives the voice data transmitted from the terminal and converts and summarizes the voice data; A database storing word-of-mouth data summarized by the server. The system of claim 1 , comprising:

3. This includes filtering out negative expressions and NG words from the user's voice input and emphasizing positive opinions. The system of claim 1 .

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