system

Abstract

A system is provided.SOLUTION: A system comprising: means for reading, by a user terminal, a QR code corresponding to a different service and transmitting data of the QR code to a generative artificial intelligence server; means for analyzing, by the generative artificial intelligence server, the data of the QR code and converting the data into a format specific to the service; means for acquiring information of the service based on the converted data and performing user authentication; means for transmitting authenticated data to a server of the service; and means for notifying the user terminal of a response from the server of the service.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] Currently, the use of QR codes for different services is widespread, but it is necessary to install a different application for each service and read the QR code separately. This places a burden on users, who have to manage many applications and install new applications at the register. Furthermore, each service requires the management of a separate login ID and password, making it difficult for users to use the service. Therefore, there is a need for a system that allows users to read and use QR codes seamlessly across different services. [Means for solving the problem]

[0005] This invention solves the above problems by providing a means for a user device to read QR codes corresponding to different services and send the QR code data to a generation artificial intelligence server, a means for the generation artificial intelligence server to analyze the QR code data and convert it into a service-specific format, a means for obtaining service information based on the converted data and performing user authentication, a means for sending the authenticated data to the service's server, and a means for notifying the user device of the response from the service's server.This system allows users to seamlessly use QR codes for different services simply by installing a dedicated QR code reader and management app.Security is also ensured by using data signed with a private key for user authentication.

[0006] A "QR code" is a two-dimensional barcode with a rectangular pattern, an information storage format that can be easily scanned with the camera on a mobile or portable device.

[0007] "User terminal" refers to a computer device carried and used by a user, such as a smartphone or tablet.

[0008] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze input data and convert it into a specific format.

[0009] A "server" is a computer system that provides services to other computers within a computer network.

[0010] A "service-specific format" refers to a specific data format defined for each service (for example, a payment service or a personal authentication service).

[0011] "Store information" is data about the store identified by the QR code (e.g., store name, location, services offered).

[0012] "User authentication" is a procedure for verifying that a user is a legitimate user.

[0013] "Private key signed data" means data that has been digitally signed using cryptography, and is used for authentication and to ensure data authenticity.

[0014] "Service Server" means a server dedicated to providing a particular service (e.g., payment platform, membership service, etc.). [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[0037] Overall system flow

[0038] The system operates in the following steps.

[0039] 1. Scan the QR code

[0040] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and operates the app to scan the QR code.

[0041] The device scans the QR code and retrieves the data, which includes the service's identification information and transaction information.

[0042] 2. Data submission and analysis

[0043] The device sends the acquired QR code data to the AI ​​server. This data transmission also includes user information (e.g., user ID and session ID).

[0044] The server's generated artificial intelligence analyzes the received QR code data and identifies the corresponding service (e.g., payment service or membership service).

[0045] 3. Data Conversion

[0046] The server's artificial intelligence will then convert the data into a format specific to the particular service based on the identification results. For example, if the QR code corresponds to a payment service, it will be converted into payment request data.

[0047] 4. Obtaining store information and user authentication

[0048] The server retrieves store information from the converted data, including store name, address, transaction details, etc.

[0049] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[0050] The server verifies the user information and if authentication is successful, proceeds to the next step.

[0051] 5. Sending data to the server

[0052] The server generates the authenticated data and sends it to the server of the corresponding service, for example, payment request data in the case of a payment service.

[0053] 6. Notification of Results

[0054] The server receives the response from the service server and sends the result (success, failure, error message, etc.) to the terminal.

[0055] The user will see the result in the app. For example, if the payment is successful, the app will display "Payment Completed."

[0056] Specific examples

[0057] As an example, a case where a user purchases a product at a convenience store and makes a payment will be described.

[0058] 1. Scan the QR code

[0059] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[0060] The device acquires the QR code data and sends it to the generating artificial intelligence server.

[0061] 2. Data Analysis and Transformation

[0062] The server analyzes the QR code data and identifies it as compatible with payment services.

[0063] The server converts the data into payment service specific payment request data.

[0064] 3. Acquisition and authentication of store information

[0065] The server retrieves the store information and processes it along with the user's payment request.

[0066] The device sends data signed with the user's private key and requests authentication.

[0067] The server performs authentication and, if authentication is successful, sends the payment request data to the payment service's server.

[0068] 4. Notification of Results

[0069] The payment service's server processes the payment and, if successful, sends an acknowledgement to the server.

[0070] The server notifies the terminal of the result, and the user receives "payment completed" information via the app.

[0071] This will allow users to seamlessly use different services within a single app, greatly improving convenience.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[0075] Step 2:

[0076] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[0077] Step 3:

[0078] The device sends the acquired QR code data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[0079] Step 4:

[0080] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[0081] Step 5:

[0082] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[0083] Step 6:

[0084] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[0085] Step 7:

[0086] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[0087] Step 8:

[0088] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[0089] Step 9:

[0090] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[0091] Step 10:

[0092] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[0093] Step 11:

[0094] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[0095] Step 12:

[0096] The server transmits the processing result received from the service server to the terminal.

[0097] Step 13:

[0098] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[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] Conventional QR code scanning systems required separate applications for each different service, resulting in reduced user convenience. Furthermore, because each service used a different data format, data analysis and conversion were complex and processing was inefficient. Furthermore, user authentication processes were often insufficient, potentially resulting in security 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 a means for analyzing QR code data and converting it into a service-specific format, a means for acquiring service information based on the converted data and performing user authentication, and a means for transmitting the authenticated data to an external system for the service. This allows different services to be used seamlessly with a single application. Furthermore, the use of a generative AI model improves the accuracy of analysis and conversion, strengthening the user authentication process and improving security.

[0104] "User terminal" refers to a computer device or smart device operated by a user, which has the function of scanning QR codes and sending and receiving data.

[0105] The "server" is a central processing unit that has the function of analyzing the QR code data sent from the user terminal and converting it into a format specific to the service.

[0106] A "QR code" is a type of two-dimensional barcode, and is an information recording means for storing service identification information, transaction information, etc.

[0107] "Analysis" refers to a series of processes in which the server decodes the received QR code data and applies the information contained therein to service identification and transaction processing.

[0108] A "service-specific format" is the parsed data converted into a format required by a particular service.

[0109] "User authentication" refers to the process of verifying that a user is a legitimate user using a private key or other means.

[0110] A "generative AI model" is a large-scale artificial intelligence model used to analyze QR code data and identify services with high accuracy.

[0111] An "external system" refers to a third-party system other than a user terminal or server, which performs processing specific to the service.

[0112] "Security" refers to the state in which systems and data are protected from unauthorized access, tampering, and information leakage.

[0113] "Notification" refers to the means of transmitting information by which a server notifies a user terminal or other system of usage status or processing results.

[0114] The present invention provides a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. This system is composed of a user terminal, a server, and an external system.

[0115] The system of the present invention begins when a user launches a dedicated QR code reader and management app. The user terminal scans the QR code using hardware such as a smart device. This QR code contains service identification information and transaction information. Once the user scans the QR code, the terminal converts the data into an analyzable format and sends it to the server.

[0116] The server uses a generative AI model to analyze the received QR code data and user information (such as user ID and session ID). An example of an AI model is OpenAI's GPT-4. The AI ​​model analyzes the QR code data and identifies the corresponding service (payment service, membership service, etc.).

[0117] The server then converts the data into a format specific to the service based on the analysis results. This converted data includes payment request data, membership information, etc. The server then uses the converted data to obtain transaction location information (store name, address, transaction details, etc.).

[0118] The terminal requests authentication from the server using the user's pre-registered information. Authentication uses data signed with the user's private key. The server verifies this authentication data, and if user authentication is successful, it sends the authenticated data to an external system. The external system could be, for example, a payment service server or a member management server.

[0119] The external system returns a response to the server, and the server receives the result (success, failure, error message, etc.). The server notifies the device of this result, and the user confirms the result through a display within the app. For example, if the payment is successful, "Payment completed" is displayed.

[0120] Specific examples

[0121] As an example, let's consider the case where a user purchases an item at a convenience store and pays using a dedicated QR code reader / management app. When the user scans the QR code at the register, the device obtains the QR code data and sends it to the server. The server uses a generative AI model to analyze the QR code data and identifies it as compatible with the payment service. The server converts the data into payment request data and performs authentication using data signed with the user's private key. If authentication is successful, the payment request data is sent to the payment service's server. The payment service's server processes the payment and returns the result to the server. The server notifies the device of the result, and the user receives information that "payment completed."

[0122] Prompt Sentence Examples

[0123] 1. "QR code scan": The user launches the dedicated app and scans the QR code.

[0124] 2. "Send data to server": The device sends the QR code data and user information to the server.

[0125] 3. "Data Analysis": The server analyzes the QR code data and identifies the corresponding service.

[0126] 4. "Data Conversion": The server converts the data into a format for a specific service.

[0127] This allows users to seamlessly use different services within a single application, improving the accuracy of analysis and authentication and strengthening security.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Flow and explanation for each processing step

[0130] Step 1:

[0131] The user launches the dedicated QR code reader and management app on their smart device and displays the QR code scanning screen. The user then uses the camera to scan the QR code. The device analyzes the image captured by the camera and extracts the QR code data. The input data is the QR code image, and the output data is the analyzed QR code information. The device then converts the scanned data into a specific format.

[0132] Specific behavior:

[0133] The user taps on the app to open the QR code scanning screen.

[0134] The camera on your smart device will launch and take a picture of the QR code.

[0135] The device analyzes the image data from the camera and extracts the QR code information as text data.

[0136] Step 2:

[0137] The device sends the acquired QR code data to the server. At this time, user information (user ID and session ID) is also included. The input data is the QR code data and user information, and the output data is the data sent to the server. The server confirms receipt.

[0138] Specific behavior:

[0139] The device sends data to the server using Wi-Fi or a mobile network.

[0140] The server receives the data and returns a response confirming receipt to the terminal.

[0141] Step 3:

[0142] The server uses the generated AI model to analyze the QR code data and identify the corresponding service. The input data is the QR code data, and the output data is the service information identified by the analysis. The server saves the analysis results as a specific record.

[0143] Specific behavior:

[0144] The server invokes a generative AI model (e.g., GPT-4) to begin the analysis process.

[0145] The model analyzes the QR code data and identifies the service type (payment, membership).

[0146] The identification results are stored in a database.

[0147] Step 4:

[0148] The server converts the data into a format specific to the specific service based on the identification result. The input data is the identified service information, and the output data is the data converted into the format of the specific service. The server temporarily stores the converted data.

[0149] Specific behavior:

[0150] The server invokes a specific transformation function to convert the data into a service-specific format.

[0151] The transformed data is temporarily stored for the next processing step.

[0152] Step 5:

[0153] The server retrieves the transaction location information from the transformed data. The input data is the transformed data, and the output data is the transaction location details. The server associates this information with the user's payment request.

[0154] Specific behavior:

[0155] The server searches the database for transaction location information (store name, location).

[0156] Link the information found to the payment request data.

[0157] Step 6:

[0158] The terminal generates data signed with the user's private key and requests authentication from the server. The input data is the user's authentication information, and the output data is the signed authentication data. The server verifies this authentication data.

[0159] Specific behavior:

[0160] The device generates authentication data signed using the user's private key.

[0161] The server receives the signed data and performs an authentication process.

[0162] Step 7:

[0163] The server sends the authenticated data to the external system. The input data is the authenticated data, and the output data is the data sent to the external system. The external system returns the processing result.

[0164] Specific behavior:

[0165] The server sends the payment request data to the external system via API.

[0166] The external system returns a response, which the server receives.

[0167] Step 8:

[0168] The server notifies the device of the response result from the external system. The input data is the response result from the external system, and the output data is the notification message. The user can check the result through the app.

[0169] Specific behavior:

[0170] The response received by the server is analyzed.

[0171] The analysis results are sent to the terminal as a notification message.

[0172] The device displays the notification on the screen and the user confirms the results.

[0173] Through these steps, users can seamlessly access different services using a single application. At each processing step, appropriate data processing and calculations are performed based on specific input data, and the output data is passed on to the next step.

[0174] (Application example 1)

[0175] 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."

[0176] Currently, when using different electronic payment services or point card services, users must operate multiple applications individually, placing a heavy operational burden on them. Furthermore, each service requires different operating methods and authentication methods, which can be difficult for users to understand and often leads to confusion. This can lead to operational errors and reduced convenience.

[0177] 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.

[0178] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generating AI server; means for the generating AI server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for the generating AI server to analyze the contents of the QR code and comprehensively identify and process multiple electronic payment services, point card services, membership card services, coupon usage, etc.; and means for notifying the user terminal of responses from the service servers. This enables users to easily and seamlessly use multiple services with a single application.

[0179] A "user terminal" is an electronic device that has the function of reading a QR code and sending the data to the generating artificial intelligence server.

[0180] The "generative artificial intelligence server" is a server that has the ability to analyze QR code data and convert it into a format specific to the service.

[0181] A "QR code" is a form of two-dimensional barcode and a means of storing information digitally.

[0182] A "service-specific format" is a specific data format required by each service, and is a data structure that satisfies the different requirements for each service.

[0183] "Converted data" refers to the data after the QR code data analyzed by the generation artificial intelligence server is converted into a format specific to the service.

[0184] "User authentication" is a procedure for verifying that a user is a legitimate user, and may involve the use of data signed with a private key.

[0185] "Electronic payment services" are payment services carried out using digital technology.

[0186] A "point card service" is a system in which users earn points based on purchases and service usage, and manage and use those points.

[0187] A "membership card service" is a card system issued by stores or service providers to prove membership and receive benefits and services.

[0188] A "coupon" is a ticket or digital code that entitles you to a discount or special offer on a specific product or service.

[0189] A "server response" is the result or information that a service's server returns in response to a request.

[0190] "Notification" refers to the act of sending the server's response result to the user's terminal to notify the user.

[0191] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[0192] Overall system flow

[0193] 1. Scan the QR code

[0194] The user launches a dedicated QR code reader and management app, displays the QR code scanning screen, and uses the camera on their smartphone or tablet to scan the QR code and obtain the data. For example, the ZXing library can be used to parse the QR code.

[0195] 2. Data submission and analysis

[0196] The smartphone sends the acquired QR code data to the AI ​​server, along with user information (e.g., user ID and session ID).

[0197] The generative artificial intelligence server uses generative AI models such as the OpenAI GPT-4 API to analyze the QR code data and identify the relevant service (electronic payment service, point card service, membership card service, coupon use, etc.).

[0198] 3. Data Conversion

[0199] Based on the analysis results, the generation AI server converts the data into a format specific to a particular service. For example, if the QR code corresponds to an electronic payment service, it converts it into payment request data.

[0200] 4. Obtaining store information and user authentication

[0201] The artificial intelligence server then obtains store information from the converted data, including the store name, location, and transaction details.

[0202] The smartphone sends the data signed with the user's private key to the generation artificial intelligence server and requests authentication.

[0203] The generation artificial intelligence server verifies the user information and, if authentication is successful, proceeds to the next step.

[0204] 5. Sending data to the service server

[0205] The generating artificial intelligence server generates authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[0206] 6. Notification of Results

[0207] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[0208] The AI ​​server then sends the results to the smartphone, where the user can confirm the results. For example, if the payment is successful, the app will display "Payment Complete."

[0209] Specific examples

[0210] Consider a case where a user purchases an item at a convenience store and makes a payment.

[0211] 1. The user launches the dedicated QR code reader and management app at the cash register and scans the QR code.

[0212] 2. The QR code data is sent from the smartphone to the generation artificial intelligence server.

[0213] 3. The generating artificial intelligence server analyzes the QR code data and identifies that it corresponds to an electronic payment service.

[0214] 4. The data is converted into payment request data.

[0215] 5. The artificial intelligence server generated based on the signed data sent from the smartphone performs user authentication, and the authentication is successful.

[0216] 6. The payment request data is sent to the appropriate electronic payment service server.

[0217] 7. The service server processes the payment, and if successful, notifies the smartphone of the result, and the user receives a "payment completed" notification.

[0218] Example prompts to input to the generative AI model

[0219] "Analyze the QR code data scanned by the user using a dedicated app, identify which service it corresponds to, and convert the data into the format of the corresponding service.

[0220] QR code data: {QR_CODE_DATA}

[0221] User information: {USER_INFO}"

[0222] In this way, the present invention realizes a system that allows users to seamlessly use a variety of services with a single application by utilizing user devices such as smartphones and tablets and linking them with a generation artificial intelligence server.

[0223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0224] Step 1:

[0225] The user launches the dedicated QR code reader and management app and displays the QR code scanning screen. The user's device uses the smartphone camera to scan the QR code and acquires the data. The acquired QR code data is temporarily stored on the device.

[0226] Input: QR code image

[0227] Output: QR code data

[0228] Specific operation: When the user presses the QR code scan button, the camera is activated and the QR code is read.

[0229] Step 2:

[0230] The user device sends the acquired QR code data to the AI ​​server, along with the user identification information (e.g., user ID and session ID).

[0231] Input: QR code data, user identification information

[0232] Output: Request data to the server

[0233] Specific operation: QR code data and user identification information are sent to the server as an HTTP request.

[0234] Step 3:

[0235] The generative AI server analyzes the received QR code data and identifies the applicable service. Using a generative AI model (e.g., OpenAI GPT-4), it analyzes the QR code data and identifies applicable services (e.g., electronic payment, point cards, membership cards, coupons, etc.).

[0236] Input: QR code data

[0237] Output: Service identification information

[0238] Specific operation: QR code data along with a prompt sentence is input into the generative AI model, and service identification information is obtained as the model output.

[0239] Step 4:

[0240] The generating artificial intelligence server converts the QR code data into a format specific to the particular service, such as payment request data for an electronic payment service, depending on the identified service.

[0241] Input: Service identification information, QR code data

[0242] Output: Data in service-specific format

[0243] Specific operation: Based on the identification information, convert the QR code data into the appropriate service format.

[0244] Step 5:

[0245] The artificial intelligence server then obtains store information from the converted data, including the store name, address, and transaction details.

[0246] Input: Data in service-specific format

[0247] Output: Store information

[0248] Specific operation: Search and obtain corresponding store information from the database.

[0249] Step 6:

[0250] To perform user authentication, the user terminal sends data signed with the private key to the generating artificial intelligence server.

[0251] Input: Data signed with the user's private key

[0252] Output: Authentication request data

[0253] Specific operation: The signature is sent to the server using the user's private key.

[0254] Step 7:

[0255] The generation artificial intelligence server verifies the sent signature and performs user authentication. If authentication is successful, proceed to the next step.

[0256] Input: Authentication request data

[0257] Output: Authentication result

[0258] Specific behavior: Decrypts the signature and verifies the user's authenticity.

[0259] Step 8:

[0260] The generating artificial intelligence server generates the authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[0261] Input: Authentication result, data in service-specific format

[0262] Output: Request data to the service server

[0263] Specific operation: Request data is generated based on the authentication result and sent as an HTTP request to the server of the corresponding service.

[0264] Step 9:

[0265] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[0266] Input: Request data to the service server

[0267] Output: Service server response

[0268] Specific behavior: Processes the request, generates a result, and sends it back.

[0269] Step 10:

[0270] The generating artificial intelligence server notifies the user's device of the results, and the user can confirm the results through the display within the app.

[0271] Input: Service server response

[0272] Output: Notification to user device

[0273] Specific operation: The response result is sent to the user's device and displayed within the app.

[0274] 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.

[0275] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[0276] Overall system flow

[0277] The system operates in the following steps.

[0278] 1. Scan the QR code

[0279] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[0280] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[0281] 2. Acquiring Emotion Data

[0282] When a user scans a QR code, the device's built-in emotion engine analyzes the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[0283] 3. Data submission and analysis

[0284] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[0285] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[0286] 4. Data Conversion

[0287] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[0288] 5. Obtaining store information and user authentication

[0289] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[0290] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[0291] The server verifies the user information and if authentication is successful, proceeds to the next step.

[0292] 6. Sending data to the server

[0293] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[0294] 7. Notification of Results

[0295] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[0296] The service's server generates the processing result and sends it to the server.

[0297] The server transmits the processing result received from the service server to the terminal.

[0298] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[0299] 8. Feedback based on emotional data

[0300] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[0301] The server improves the user experience by notifying the device of appropriate messages and service suggestions based on the user's emotions.

[0302] Specific examples

[0303] As an example, consider a case where a user purchases an item at a retail store and pays using a QR code.

[0304] 1. Scan the QR code

[0305] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[0306] The device acquires the QR code data, and the emotion engine analyzes the user's facial expressions to acquire emotional data.

[0307] 2. Data submission and analysis

[0308] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[0309] The server analyzes the content of the QR code and identifies it as compatible with the payment service.

[0310] 3. Data Conversion and Authentication

[0311] The server converts the QR code data into payment request data and obtains store information.

[0312] The device sends data signed with the user's private key, and the server performs authentication.

[0313] 4. Payment Processing

[0314] The server sends the authenticated data to the payment service's server, which processes the payment.

[0315] The service server notifies the server of the payment result, which is then transmitted to the terminal.

[0316] 5. Notification of results and emotional feedback

[0317] The user confirms the payment in the app.

[0318] At the same time, based on the emotional data analyzed by the emotion engine, the server displays an appropriate message to the user (e.g., thank you for your satisfaction) or information about special offers for the next use.

[0319] This not only allows users to seamlessly use different services within a single app, but also provides services tailored to their emotions, resulting in a more comfortable user experience.

[0320] The processing flow will be explained below.

[0321] Step 1:

[0322] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[0323] Step 2:

[0324] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[0325] Step 3:

[0326] The device activates its built-in emotion engine to analyze the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[0327] Step 4:

[0328] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[0329] Step 5:

[0330] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[0331] Step 6:

[0332] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[0333] Step 7:

[0334] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[0335] Step 8:

[0336] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[0337] Step 9:

[0338] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[0339] Step 10:

[0340] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[0341] Step 11:

[0342] The service's server analyzes the received data and takes appropriate action, for example approving or denying the payment.

[0343] Step 12:

[0344] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[0345] Step 13:

[0346] The server transmits the processing result received from the service server to the terminal.

[0347] Step 14:

[0348] The terminal notifies the user, who then checks the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[0349] Step 15:

[0350] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[0351] Step 16:

[0352] The server then sends appropriate messages and service suggestions to the device based on the user's emotions, improving the user experience. For example, if the user is surprised, the server will display a message such as "Thank you for the surprise" to provide services that reflect the user's emotions.

[0353] Example 2

[0354] 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."

[0355] Conventional QR code readers and management apps have difficulty seamlessly supporting multiple different services, forcing users to switch between apps for each service, resulting in a lack of convenience. Furthermore, services are provided without consideration of user feelings, which can result in a poor user experience. Therefore, there is a need for a system that can manage multiple services in one place and provide optimal services that respond to user feelings.

[0356] 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.

[0357] In this invention, the server includes means for the user terminal to read QR codes corresponding to different services and transmit the QR code data to the generation artificial intelligence server, means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service, means for acquiring service information based on the converted data and performing user authentication, means for acquiring user emotion data using a built-in emotion engine, means for transmitting the acquired emotion data to the generation artificial intelligence server, means for generating feedback based on the emotion data and notifying the user terminal, means for transmitting the authenticated data to the service server, and means for notifying the user terminal of a response from the service server. This not only allows the user to seamlessly use different services with a single app, but also enables the provision of optimal services according to emotions.

[0358] "User terminal" means an electronic device operated by a user that has the functions of reading QR codes, acquiring emotion data, and sending and receiving data.

[0359] The "generative artificial intelligence server" is a server system that analyzes the received QR code data and emotion data, identifies appropriate services, converts data, and supports the provision of optimal services.

[0360] A "QR code" is a two-dimensional barcode that encodes specific information and allows the data to be decoded using a reading device or application.

[0361] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and tone of voice to detect emotional states such as joy, surprise, or anger.

[0362] A "service server" is a server used to provide specific services (e.g., payment services, membership services), which receives data from user terminals and the generation artificial intelligence server and performs corresponding processing.

[0363] "Feedback" refers to additional information or messages provided to users after a service is provided based on their reactions and status, and is used to inform and improve the user experience.

[0364] A "private key" is key information used for digital signatures and data encryption, and is held individually by a user for access control and authentication purposes.

[0365] A "signature" is a digital signature generated using a private key to verify the authenticity and origin of data.

[0366] A "digital signature" is a signature generated for electronic data, and is used to prevent data tampering and to certify the sender.

[0367] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[0368] First, the user launches a dedicated QR code reader and management app on their device, such as a smartphone or tablet. This app functions as a centralized management system for various services (e.g., payment, membership services, etc.). When the user performs an operation to scan the QR code, the QR code is read using the device's camera and the data is temporarily saved. This data includes service identification information and transaction information.

[0369] Furthermore, the user's device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice when scanning the QR code to obtain emotional data. This emotional data is temporarily stored within the app. Typical examples of emotion engines used here include Microsoft Azure's Face API and Emotion API.

[0370] The acquired QR code data and emotion data are sent from the device to the generation AI server. This transmission is secured by using protocols such as HTTPS to ensure data security. The generation AI server analyzes the QR code data and identifies which service it corresponds to. The generation AI model used here includes, for example, a QR code analysis algorithm and data mining technology.

[0371] Based on the identified service, the server converts the QR code data into a service-specific format (e.g., payment request data). The server then retrieves store information from a database, including store name, location, transaction details, etc.

[0372] To authenticate the user, the device generates a digital signature using the user's private key and sends it to the server, which then verifies the signature using public key cryptography and proceeds to the next step if authentication is successful.

[0373] The authenticated data is then sent to the service server. For example, payment request data is sent to the payment service server, which processes the payment by approving or rejecting it. The service server then sends the processing result back to the AI ​​server, which notifies the device. The user can check the results, such as whether the payment was successful, through a dedicated QR code reader and management app.

[0374] Finally, feedback based on emotional data is important. The server analyzes the emotional data and optimizes the service it provides, for example, by displaying priority support messages or special offers if the user is feeling stressed. This series of feedback is extremely useful for improving the user experience.

[0375] As a concrete example, in a scenario where a user is purchasing a product at a retail store, the following prompt sentence is input into the generative AI model:

[0376] "The user launches the dedicated QR code reader and management app at the cash register and scans the QR code attached to the product. The device acquires the QR code data, and the emotion engine analyzes the user's facial expression to acquire emotion data."

[0377] This not only allows users to seamlessly use different services within a single app, but also enables optimal service delivery based on their emotions, resulting in a pleasant user experience.

[0378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0379] Step 1:

[0380] The user launches a dedicated QR code reader and management app.

[0381] Specifically, the user taps the dedicated app icon on the home screen of their smartphone or tablet to launch the app.

[0382] Input: A user taps the app icon.

[0383] Output: The app will launch and show the QR code scanning screen.

[0384] Step 2:

[0385] The user scans the QR code.

[0386] The user taps the "Scan" button in the app and the device uses the camera to scan the QR code.

[0387] Input: QR code.

[0388] Output: The scanned QR code data is retrieved.

[0389] Step 3:

[0390] The device acquires emotion data.

[0391] The device's built-in camera captures the user's face, and the emotion engine analyzes facial expressions to generate emotion data. The device also captures the user's voice with a microphone and analyzes the tone of their voice.

[0392] Input: The user's facial expression and tone of voice.

[0393] Output: Emotion data representing the user's emotional state.

[0394] Step 4:

[0395] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[0396] The device sends the acquired QR code data, emotion data, and user information to the server using the HTTPS protocol.

[0397] Input: QR code data, emotion data, user information.

[0398] Output: The QR code data, emotion data, and user information are sent to the server.

[0399] Step 5:

[0400] The server parses the received data.

[0401] The server analyzes the received QR code data using the generated AI model and identifies the corresponding service.

[0402] Input: Received QR code data.

[0403] Output: Information identifying which service the QR code relates to.

[0404] Step 6:

[0405] The server converts the data into a service-specific format.

[0406] The server converts the QR code data into a service-specific format (e.g., payment request data) based on the identified service.

[0407] Input: Service identification information, QR code data.

[0408] Output: Data converted into a service-specific format.

[0409] Step 7:

[0410] The server obtains store information and performs user authentication.

[0411] The server retrieves store information from the database based on the converted data and verifies the data signed with the user's private key to authenticate the user.

[0412] Input: Transformed data, data signed with private key.

[0413] Output: Retrieved store information, user authentication results.

[0414] Step 8:

[0415] The authenticated data is sent to the service server.

[0416] The server sends the authenticated data to the target service server, which then analyzes the received data and performs the appropriate processing.

[0417] Input: Authenticated data.

[0418] Output: Data sent to the service server, response from the service server.

[0419] Step 9:

[0420] The results are notified to the user's terminal.

[0421] The server receives the response from the service server and notifies the user's device, and the user can check the result through the app.

[0422] Input: Response from the service server.

[0423] Output: The result is notified to the user's terminal and the user can check the result.

[0424] Step 10:

[0425] Provide feedback based on emotional data.

[0426] The server analyzes the emotional data, generates messages and suggestions tailored to the user, and notifies the user's device.

[0427] Input: User emotion data.

[0428] Output: Customized feedback messages and suggestions.

[0429] (Application example 2)

[0430] 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."

[0431] Conventional QR code-based service provision systems require users to use multiple QR code reader apps when using different services, resulting in a lack of convenience. Furthermore, services are provided uniformly without considering the user's emotional state, resulting in a suboptimal user experience. It is necessary to solve these issues and build a system that provides consistent, emotionally appropriate services to users.

[0432] 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.

[0433] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generation artificial intelligence server; means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for notifying the user terminal of a response from the service server; means including an emotion engine for obtaining user emotion data; and means for generating feedback to optimize the service based on the emotion data. This allows a user to seamlessly use multiple QR codes with a single application and also enables the provision of optimal services according to emotions.

[0434] A "user terminal" is a portable information terminal that can read QR codes and communicate with a server via the Internet.

[0435] A "QR code" is a type of two-dimensional barcode that uses a rectangular grid pattern to represent digital information.

[0436] The "generative artificial intelligence server" is a computer server that analyzes the received data, identifies the corresponding service, and provides the user with the most suitable information.

[0437] The "emotion engine" is software that analyzes the user's facial expressions and voice data obtained using a camera and microphone to determine the user's emotional state.

[0438] A "service-specific format" is a data format required by a particular service, such as a payment request or membership information.

[0439] "User authentication" is the process of verifying a user's identity and verifying their identity before providing services.

[0440] "Data signed with a private key" refers to data that has a digital signature encrypted with the user's private key, which verifies the authenticity of the data.

[0441] A "means for generating feedback" is a system or software for generating optimized services or messages based on analysis results and past data.

[0442] As an embodiment of the present invention, a specific method thereof will be described below.

[0443] This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[0444] Hardware and Software Use

[0445] First, a smartphone is used as the user device, which is equipped with a camera and microphone and is used to scan the QR code and to detect the user's facial expression and tone of voice.

[0446] The software used is an "emotion engine" for emotion analysis. For example, "Microsoft's Azure Face API" or "Google Cloud Vision API" are suitable. The generative AI server also includes a "generative artificial intelligence model" that analyzes data and identifies services.

[0447] Data processing and calculation

[0448] When a user scans a QR code, the smartphone camera captures the QR code data, while the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to collect emotional data.

[0449] This data is sent from the smartphone to the generation AI server. The generation AI server analyzes the QR code data and identifies which service it corresponds to. At this time, the QR code data is converted into a format specific to each service, such as payment request data or membership service data. Authentication is also performed using data signed with the user's private key.

[0450] The server retrieves the necessary store information from the database based on the converted data. Based on this information, the authenticated data is sent to the service server, where the service is processed. The response from the service server is notified to the user's device via the generation AI server.

[0451] At the same time, emotional data is used to generate feedback to optimize service delivery: for example, if a user is feeling stressed, priority support messages or special offers for the next visit will be displayed.

[0452] Specific examples

[0453] As a concrete example, consider the case where a user is shopping at a convenience store and makes an electronic payment using a QR code. The user launches a dedicated app and scans the QR code at the cash register. The smartphone camera reads the QR code, and at the same time, the emotion engine analyzes the facial expression. The QR code data and emotion data are sent to the generation artificial intelligence server, where they are analyzed and converted.

[0454] The server sends the payment request data to the payment service server and returns the result to the user's device. Once the payment is completed, messages such as "Thank you for your use" or "You will receive a special offer for your next use" are displayed based on the emotion data.

[0455] Prompt Sentence Examples

[0456] Analyze QR code data and user sentiment data to provide optimal service feedback.

[0457] QR code data: {qr_data}

[0458] Emotion data: {emotion_data}

[0459] This allows users to seamlessly use multiple QR codes in a single application and receive optimal services tailored to their emotions.

[0460] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0461] Step 1:

[0462] The user scans the QR code with their smartphone.

[0463] Input: QR code image captured by camera.

[0464] Processing: The QR code reader app on your smartphone analyzes the image and extracts the QR code data.

[0465] Output: QR code data.

[0466] Step 2:

[0467] When a user scans a QR code, the smartphone's built-in emotion engine analyzes the user's facial expressions and tone of voice.

[0468] Input: User's camera footage (facial expressions) and microphone audio (tone of voice).

[0469] Processing: The emotion engine performs facial expression recognition and voice analysis to generate emotion data.

[0470] Output: Emotion data.

[0471] Step 3:

[0472] The smartphone sends the acquired QR code data and emotion data to the generation AI server.

[0473] Input: QR code data, emotion data.

[0474] Processing: Data is sent to the generating AI server using a secure communication protocol (e.g., HTTPS).

[0475] Output: Data sent to the generating AI server.

[0476] Step 4:

[0477] The generation AI server analyzes the received QR code data and identifies the corresponding service.

[0478] Input: QR code data.

[0479] Processing: A generative AI model parses the QR code data and identifies services (e.g., payment, membership services).

[0480] Output: Service identity information.

[0481] Step 5:

[0482] Based on the identification results, the generation AI server converts the QR code data into a format specific to the service.

[0483] Input: Service identification information, QR code data.

[0484] Processing: Reconstruct the QR code data into a data format specific to each service (e.g., payment request data, membership information).

[0485] Output: The transformed service-specific data.

[0486] Step 6:

[0487] The server retrieves the necessary store and user information from the database based on the converted data.

[0488] Input: Transformed service-specific data.

[0489] Processing: Executes a database query to retrieve relevant store and user information.

[0490] Output: Store information, user information.

[0491] Step 7:

[0492] The generating AI server authenticates the user using data signed with the user's private key.

[0493] Input: User information, data signed with private key.

[0494] Processing: Verify the private key signature and perform user authentication.

[0495] Output: Authentication result.

[0496] Step 8:

[0497] The generation AI server sends the authenticated data to the service server, which processes the service.

[0498] Input: Authenticated data, service specific data.

[0499] Processing: Sends the authentication data and service-specific data as a request to the service's server to perform the service's processing (e.g., payment processing).

[0500] Output: Processing results from the service's server.

[0501] Step 9:

[0502] The response result from the service server is notified to the user device via the generated AI server.

[0503] Input: Processing result from the service's server.

[0504] Processing: The generation AI server receives the response result and notifies the user terminal.

[0505] Output: The response result sent to the user's device.

[0506] Step 10:

[0507] The generation AI server generates an appropriate feedback message based on the emotional data and notifies the user device.

[0508] Input: Emotion data, response results.

[0509] Processing: Emotional data is used to generate feedback messages (e.g., special offers for next use, support messages) to optimize service delivery.

[0510] Output: Feedback message.

[0511] 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.

[0512] 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.

[0513] 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.

[0514] [Second embodiment]

[0515] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0516] 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.

[0517] 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).

[0518] 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.

[0519] 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.

[0520] 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).

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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.

[0526] 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."

[0527] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[0528] Overall system flow

[0529] The system operates in the following steps.

[0530] 1. Scan the QR code

[0531] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and operates the app to scan the QR code.

[0532] The device scans the QR code and retrieves the data, which includes the service's identification information and transaction information.

[0533] 2. Data submission and analysis

[0534] The device sends the acquired QR code data to the AI ​​server. This data transmission also includes user information (e.g., user ID and session ID).

[0535] The server's generated artificial intelligence analyzes the received QR code data and identifies the corresponding service (e.g., payment service or membership service).

[0536] 3. Data Conversion

[0537] The server's artificial intelligence will then convert the data into a format specific to the particular service based on the identification results. For example, if the QR code corresponds to a payment service, it will be converted into payment request data.

[0538] 4. Obtaining store information and user authentication

[0539] The server retrieves store information from the converted data, including store name, address, transaction details, etc.

[0540] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[0541] The server verifies the user information and if authentication is successful, proceeds to the next step.

[0542] 5. Sending data to the server

[0543] The server generates the authenticated data and sends it to the server of the corresponding service, for example, payment request data in the case of a payment service.

[0544] 6. Notification of Results

[0545] The server receives the response from the service server and sends the result (success, failure, error message, etc.) to the terminal.

[0546] The user will see the result in the app. For example, if the payment is successful, the app will display "Payment Completed."

[0547] Specific examples

[0548] As an example, a case where a user purchases a product at a convenience store and makes a payment will be described.

[0549] 1. Scan the QR code

[0550] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[0551] The device acquires the QR code data and sends it to the generating artificial intelligence server.

[0552] 2. Data Analysis and Transformation

[0553] The server analyzes the QR code data and identifies it as compatible with payment services.

[0554] The server converts the data into payment service specific payment request data.

[0555] 3. Acquisition and authentication of store information

[0556] The server retrieves the store information and processes it along with the user's payment request.

[0557] The device sends data signed with the user's private key and requests authentication.

[0558] The server performs authentication and, if authentication is successful, sends the payment request data to the payment service's server.

[0559] 4. Notification of Results

[0560] The payment service's server processes the payment and, if successful, sends an acknowledgement to the server.

[0561] The server notifies the terminal of the result, and the user receives "payment completed" information via the app.

[0562] This will allow users to seamlessly use different services within a single app, greatly improving convenience.

[0563] The processing flow will be explained below.

[0564] Step 1:

[0565] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[0566] Step 2:

[0567] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[0568] Step 3:

[0569] The device sends the acquired QR code data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[0570] Step 4:

[0571] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[0572] Step 5:

[0573] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[0574] Step 6:

[0575] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[0576] Step 7:

[0577] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[0578] Step 8:

[0579] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[0580] Step 9:

[0581] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[0582] Step 10:

[0583] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[0584] Step 11:

[0585] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[0586] Step 12:

[0587] The server transmits the processing result received from the service server to the terminal.

[0588] Step 13:

[0589] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[0590] Example 1

[0591] 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."

[0592] Conventional QR code scanning systems required separate applications for each different service, resulting in reduced user convenience. Furthermore, because each service used a different data format, data analysis and conversion were complex and processing was inefficient. Furthermore, user authentication processes were often insufficient, potentially resulting in security issues.

[0593] 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.

[0594] In this invention, the server includes a means for analyzing QR code data and converting it into a service-specific format, a means for acquiring service information based on the converted data and performing user authentication, and a means for transmitting the authenticated data to an external system for the service. This allows different services to be used seamlessly with a single application. Furthermore, the use of a generative AI model improves the accuracy of analysis and conversion, strengthening the user authentication process and improving security.

[0595] "User terminal" refers to a computer device or smart device operated by a user, which has the function of scanning QR codes and sending and receiving data.

[0596] The "server" is a central processing unit that has the function of analyzing the QR code data sent from the user terminal and converting it into a format specific to the service.

[0597] A "QR code" is a type of two-dimensional barcode, and is an information recording means for storing service identification information, transaction information, etc.

[0598] "Analysis" refers to a series of processes in which the server decodes the received QR code data and applies the information contained therein to service identification and transaction processing.

[0599] A "service-specific format" is the parsed data converted into a format required by a particular service.

[0600] "User authentication" refers to the process of verifying that a user is a legitimate user using a private key or other means.

[0601] A "generative AI model" is a large-scale artificial intelligence model used to analyze QR code data and identify services with high accuracy.

[0602] An "external system" refers to a third-party system other than a user terminal or server, which performs processing specific to the service.

[0603] "Security" refers to the state in which systems and data are protected from unauthorized access, tampering, and information leakage.

[0604] "Notification" refers to the means of transmitting information by which a server notifies a user terminal or other system of usage status or processing results.

[0605] The present invention provides a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. This system is composed of a user terminal, a server, and an external system.

[0606] The system of the present invention begins when a user launches a dedicated QR code reader and management app. The user terminal scans the QR code using hardware such as a smart device. This QR code contains service identification information and transaction information. Once the user scans the QR code, the terminal converts the data into an analyzable format and sends it to the server.

[0607] The server uses a generative AI model to analyze the received QR code data and user information (such as user ID and session ID). An example of an AI model is OpenAI's GPT-4. The AI ​​model analyzes the QR code data and identifies the corresponding service (payment service, membership service, etc.).

[0608] The server then converts the data into a format specific to the service based on the analysis results. This converted data includes payment request data, membership information, etc. The server then uses the converted data to obtain transaction location information (store name, address, transaction details, etc.).

[0609] The terminal requests authentication from the server using the user's pre-registered information. Authentication uses data signed with the user's private key. The server verifies this authentication data, and if user authentication is successful, it sends the authenticated data to an external system. The external system could be, for example, a payment service server or a member management server.

[0610] The external system returns a response to the server, and the server receives the result (success, failure, error message, etc.). The server notifies the device of this result, and the user confirms the result through a display within the app. For example, if the payment is successful, "Payment completed" is displayed.

[0611] Specific examples

[0612] As an example, let's consider the case where a user purchases an item at a convenience store and pays using a dedicated QR code reader / management app. When the user scans the QR code at the register, the device obtains the QR code data and sends it to the server. The server uses a generative AI model to analyze the QR code data and identifies it as compatible with the payment service. The server converts the data into payment request data and performs authentication using data signed with the user's private key. If authentication is successful, the payment request data is sent to the payment service's server. The payment service's server processes the payment and returns the result to the server. The server notifies the device of the result, and the user receives information that "payment completed."

[0613] Prompt Sentence Examples

[0614] 1. "QR code scan": The user launches the dedicated app and scans the QR code.

[0615] 2. "Send data to server": The device sends the QR code data and user information to the server.

[0616] 3. "Data Analysis": The server analyzes the QR code data and identifies the corresponding service.

[0617] 4. "Data Conversion": The server converts the data into a format for a specific service.

[0618] This allows users to seamlessly use different services within a single application, improving the accuracy of analysis and authentication and strengthening security.

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] Flow and explanation for each processing step

[0621] Step 1:

[0622] The user launches the dedicated QR code reader and management app on their smart device and displays the QR code scanning screen. The user then uses the camera to scan the QR code. The device analyzes the image captured by the camera and extracts the QR code data. The input data is the QR code image, and the output data is the analyzed QR code information. The device then converts the scanned data into a specific format.

[0623] Specific behavior:

[0624] The user taps on the app to open the QR code scanning screen.

[0625] The camera on your smart device will launch and take a picture of the QR code.

[0626] The device analyzes the image data from the camera and extracts the QR code information as text data.

[0627] Step 2:

[0628] The device sends the acquired QR code data to the server. At this time, user information (user ID and session ID) is also included. The input data is the QR code data and user information, and the output data is the data sent to the server. The server confirms receipt.

[0629] Specific behavior:

[0630] The device sends data to the server using Wi-Fi or a mobile network.

[0631] The server receives the data and returns a response confirming receipt to the terminal.

[0632] Step 3:

[0633] The server uses the generated AI model to analyze the QR code data and identify the corresponding service. The input data is the QR code data, and the output data is the service information identified by the analysis. The server saves the analysis results as a specific record.

[0634] Specific behavior:

[0635] The server invokes a generative AI model (e.g., GPT-4) to begin the analysis process.

[0636] The model analyzes the QR code data and identifies the service type (payment, membership).

[0637] The identification results are stored in a database.

[0638] Step 4:

[0639] The server converts the data into a format specific to the specific service based on the identification result. The input data is the identified service information, and the output data is the data converted into the format of the specific service. The server temporarily stores the converted data.

[0640] Specific behavior:

[0641] The server invokes a specific transformation function to convert the data into a service-specific format.

[0642] The transformed data is temporarily stored for the next processing step.

[0643] Step 5:

[0644] The server retrieves the transaction location information from the transformed data. The input data is the transformed data, and the output data is the transaction location details. The server associates this information with the user's payment request.

[0645] Specific behavior:

[0646] The server searches the database for transaction location information (store name, location).

[0647] Link the information found to the payment request data.

[0648] Step 6:

[0649] The terminal generates data signed with the user's private key and requests authentication from the server. The input data is the user's authentication information, and the output data is the signed authentication data. The server verifies this authentication data.

[0650] Specific behavior:

[0651] The device generates authentication data signed using the user's private key.

[0652] The server receives the signed data and performs an authentication process.

[0653] Step 7:

[0654] The server sends the authenticated data to the external system. The input data is the authenticated data, and the output data is the data sent to the external system. The external system returns the processing result.

[0655] Specific behavior:

[0656] The server sends the payment request data to the external system via API.

[0657] The external system returns a response, which the server receives.

[0658] Step 8:

[0659] The server notifies the device of the response result from the external system. The input data is the response result from the external system, and the output data is the notification message. The user can check the result through the app.

[0660] Specific behavior:

[0661] The response received by the server is analyzed.

[0662] The analysis results are sent to the terminal as a notification message.

[0663] The device displays the notification on the screen and the user confirms the results.

[0664] Through these steps, users can seamlessly access different services using a single application. At each processing step, appropriate data processing and calculations are performed based on specific input data, and the output data is passed on to the next step.

[0665] (Application example 1)

[0666] 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."

[0667] Currently, when using different electronic payment services or point card services, users must operate multiple applications individually, placing a heavy operational burden on them. Furthermore, each service requires different operating methods and authentication methods, which can be difficult for users to understand and often leads to confusion. This can lead to operational errors and reduced convenience.

[0668] 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.

[0669] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generating AI server; means for the generating AI server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for the generating AI server to analyze the contents of the QR code and comprehensively identify and process multiple electronic payment services, point card services, membership card services, coupon usage, etc.; and means for notifying the user terminal of responses from the service servers. This enables users to easily and seamlessly use multiple services with a single application.

[0670] A "user terminal" is an electronic device that has the function of reading a QR code and sending the data to the generating artificial intelligence server.

[0671] The "generative artificial intelligence server" is a server that has the ability to analyze QR code data and convert it into a format specific to the service.

[0672] A "QR code" is a form of two-dimensional barcode and a means of storing information digitally.

[0673] A "service-specific format" is a specific data format required by each service, and is a data structure that satisfies the different requirements for each service.

[0674] "Converted data" refers to the data after the QR code data analyzed by the generation artificial intelligence server is converted into a format specific to the service.

[0675] "User authentication" is a procedure for verifying that a user is a legitimate user, and may involve the use of data signed with a private key.

[0676] "Electronic payment services" are payment services carried out using digital technology.

[0677] A "point card service" is a system in which users earn points based on purchases and service usage, and manage and use those points.

[0678] A "membership card service" is a card system issued by stores or service providers to prove membership and receive benefits and services.

[0679] A "coupon" is a ticket or digital code that entitles you to a discount or special offer on a specific product or service.

[0680] A "server response" is the result or information that a service's server returns in response to a request.

[0681] "Notification" refers to the act of sending the server's response result to the user's terminal to notify the user.

[0682] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[0683] Overall system flow

[0684] 1. Scan the QR code

[0685] The user launches a dedicated QR code reader and management app, displays the QR code scanning screen, and uses the camera on their smartphone or tablet to scan the QR code and obtain the data. For example, the ZXing library can be used to parse the QR code.

[0686] 2. Data submission and analysis

[0687] The smartphone sends the acquired QR code data to the AI ​​server, along with user information (e.g., user ID and session ID).

[0688] The generative artificial intelligence server uses generative AI models such as the OpenAI GPT-4 API to analyze the QR code data and identify the relevant service (electronic payment service, point card service, membership card service, coupon use, etc.).

[0689] 3. Data Conversion

[0690] Based on the analysis results, the generation AI server converts the data into a format specific to a particular service. For example, if the QR code corresponds to an electronic payment service, it converts it into payment request data.

[0691] 4. Obtaining store information and user authentication

[0692] The artificial intelligence server then obtains store information from the converted data, including the store name, location, and transaction details.

[0693] The smartphone sends the data signed with the user's private key to the generation artificial intelligence server and requests authentication.

[0694] The generation artificial intelligence server verifies the user information and, if authentication is successful, proceeds to the next step.

[0695] 5. Sending data to the service server

[0696] The generating artificial intelligence server generates authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[0697] 6. Notification of Results

[0698] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[0699] The AI ​​server then sends the results to the smartphone, where the user can confirm the results. For example, if the payment is successful, the app will display "Payment Complete."

[0700] Specific examples

[0701] Consider a case where a user purchases an item at a convenience store and makes a payment.

[0702] 1. The user launches the dedicated QR code reader and management app at the cash register and scans the QR code.

[0703] 2. The QR code data is sent from the smartphone to the generation artificial intelligence server.

[0704] 3. The generating artificial intelligence server analyzes the QR code data and identifies that it corresponds to an electronic payment service.

[0705] 4. The data is converted into payment request data.

[0706] 5. The artificial intelligence server generated based on the signed data sent from the smartphone performs user authentication, and the authentication is successful.

[0707] 6. The payment request data is sent to the appropriate electronic payment service server.

[0708] 7. The service server processes the payment, and if successful, notifies the smartphone of the result, and the user receives a "payment completed" notification.

[0709] Example prompts to input to the generative AI model

[0710] "Analyze the QR code data scanned by the user using a dedicated app, identify which service it corresponds to, and convert the data into the format of the corresponding service.

[0711] QR code data: {QR_CODE_DATA}

[0712] User information: {USER_INFO}"

[0713] In this way, the present invention realizes a system that allows users to seamlessly use a variety of services with a single application by utilizing user devices such as smartphones and tablets and linking them with a generation artificial intelligence server.

[0714] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0715] Step 1:

[0716] The user launches the dedicated QR code reader and management app and displays the QR code scanning screen. The user's device uses the smartphone camera to scan the QR code and acquires the data. The acquired QR code data is temporarily stored on the device.

[0717] Input: QR code image

[0718] Output: QR code data

[0719] Specific operation: When the user presses the QR code scan button, the camera is activated and the QR code is read.

[0720] Step 2:

[0721] The user device sends the acquired QR code data to the AI ​​server, along with the user identification information (e.g., user ID and session ID).

[0722] Input: QR code data, user identification information

[0723] Output: Request data to the server

[0724] Specific operation: QR code data and user identification information are sent to the server as an HTTP request.

[0725] Step 3:

[0726] The generative AI server analyzes the received QR code data and identifies the applicable service. Using a generative AI model (e.g., OpenAI GPT-4), it analyzes the QR code data and identifies applicable services (e.g., electronic payment, point cards, membership cards, coupons, etc.).

[0727] Input: QR code data

[0728] Output: Service identification information

[0729] Specific operation: QR code data along with a prompt sentence is input into the generative AI model, and service identification information is obtained as the model output.

[0730] Step 4:

[0731] The generating artificial intelligence server converts the QR code data into a format specific to the particular service, such as payment request data for an electronic payment service, depending on the identified service.

[0732] Input: Service identification information, QR code data

[0733] Output: Data in service-specific format

[0734] Specific operation: Based on the identification information, convert the QR code data into the appropriate service format.

[0735] Step 5:

[0736] The artificial intelligence server then obtains store information from the converted data, including the store name, address, and transaction details.

[0737] Input: Data in service-specific format

[0738] Output: Store information

[0739] Specific operation: Search and obtain corresponding store information from the database.

[0740] Step 6:

[0741] To perform user authentication, the user terminal sends data signed with the private key to the generating artificial intelligence server.

[0742] Input: Data signed with the user's private key

[0743] Output: Authentication request data

[0744] Specific operation: The signature is sent to the server using the user's private key.

[0745] Step 7:

[0746] The generation artificial intelligence server verifies the sent signature and performs user authentication. If authentication is successful, proceed to the next step.

[0747] Input: Authentication request data

[0748] Output: Authentication result

[0749] Specific behavior: Decrypts the signature and verifies the user's authenticity.

[0750] Step 8:

[0751] The generating artificial intelligence server generates the authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[0752] Input: Authentication result, data in service-specific format

[0753] Output: Request data to the service server

[0754] Specific operation: Request data is generated based on the authentication result and sent as an HTTP request to the server of the corresponding service.

[0755] Step 9:

[0756] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[0757] Input: Request data to the service server

[0758] Output: Service server response

[0759] Specific behavior: Processes the request, generates a result, and sends it back.

[0760] Step 10:

[0761] The generating artificial intelligence server notifies the user's device of the results, and the user can confirm the results through the display within the app.

[0762] Input: Service server response

[0763] Output: Notification to user device

[0764] Specific operation: The response result is sent to the user's device and displayed within the app.

[0765] 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.

[0766] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[0767] Overall system flow

[0768] The system operates in the following steps.

[0769] 1. Scan the QR code

[0770] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[0771] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[0772] 2. Acquiring Emotion Data

[0773] When a user scans a QR code, the device's built-in emotion engine analyzes the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[0774] 3. Data submission and analysis

[0775] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[0776] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[0777] 4. Data Conversion

[0778] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[0779] 5. Obtaining store information and user authentication

[0780] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[0781] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[0782] The server verifies the user information and if authentication is successful, proceeds to the next step.

[0783] 6. Sending data to the server

[0784] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[0785] 7. Notification of Results

[0786] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[0787] The service's server generates the processing result and sends it to the server.

[0788] The server transmits the processing result received from the service server to the terminal.

[0789] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[0790] 8. Feedback based on emotional data

[0791] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[0792] The server improves the user experience by notifying the device of appropriate messages and service suggestions based on the user's emotions.

[0793] Specific examples

[0794] As an example, consider a case where a user purchases an item at a retail store and pays using a QR code.

[0795] 1. Scan the QR code

[0796] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[0797] The device acquires the QR code data, and the emotion engine analyzes the user's facial expressions to acquire emotional data.

[0798] 2. Data submission and analysis

[0799] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[0800] The server analyzes the content of the QR code and identifies it as compatible with the payment service.

[0801] 3. Data Conversion and Authentication

[0802] The server converts the QR code data into payment request data and obtains store information.

[0803] The device sends data signed with the user's private key, and the server performs authentication.

[0804] 4. Payment Processing

[0805] The server sends the authenticated data to the payment service's server, which processes the payment.

[0806] The service server notifies the server of the payment result, which is then transmitted to the terminal.

[0807] 5. Notification of results and emotional feedback

[0808] The user confirms the payment in the app.

[0809] At the same time, based on the emotional data analyzed by the emotion engine, the server displays an appropriate message to the user (e.g., thank you for your satisfaction) or information about special offers for the next use.

[0810] This not only allows users to seamlessly use different services within a single app, but also provides services tailored to their emotions, resulting in a more comfortable user experience.

[0811] The processing flow will be explained below.

[0812] Step 1:

[0813] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[0814] Step 2:

[0815] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[0816] Step 3:

[0817] The device activates its built-in emotion engine to analyze the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[0818] Step 4:

[0819] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[0820] Step 5:

[0821] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[0822] Step 6:

[0823] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[0824] Step 7:

[0825] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[0826] Step 8:

[0827] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[0828] Step 9:

[0829] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[0830] Step 10:

[0831] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[0832] Step 11:

[0833] The service's server analyzes the received data and takes appropriate action, for example approving or denying the payment.

[0834] Step 12:

[0835] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[0836] Step 13:

[0837] The server transmits the processing result received from the service server to the terminal.

[0838] Step 14:

[0839] The terminal notifies the user, who then checks the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[0840] Step 15:

[0841] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[0842] Step 16:

[0843] The server then sends appropriate messages and service suggestions to the device based on the user's emotions, improving the user experience. For example, if the user is surprised, the server will display a message such as "Thank you for the surprise" to provide services that reflect the user's emotions.

[0844] Example 2

[0845] 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."

[0846] Conventional QR code readers and management apps have difficulty seamlessly supporting multiple different services, forcing users to switch between apps for each service, resulting in a lack of convenience. Furthermore, services are provided without consideration of user feelings, which can result in a poor user experience. Therefore, there is a need for a system that can manage multiple services in one place and provide optimal services that respond to user feelings.

[0847] 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.

[0848] In this invention, the server includes means for the user terminal to read QR codes corresponding to different services and transmit the QR code data to the generation artificial intelligence server, means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service, means for acquiring service information based on the converted data and performing user authentication, means for acquiring user emotion data using a built-in emotion engine, means for transmitting the acquired emotion data to the generation artificial intelligence server, means for generating feedback based on the emotion data and notifying the user terminal, means for transmitting the authenticated data to the service server, and means for notifying the user terminal of a response from the service server. This not only allows the user to seamlessly use different services with a single app, but also enables the provision of optimal services according to emotions.

[0849] "User terminal" means an electronic device operated by a user that has the functions of reading QR codes, acquiring emotion data, and sending and receiving data.

[0850] The "generative artificial intelligence server" is a server system that analyzes the received QR code data and emotion data, identifies appropriate services, converts data, and supports the provision of optimal services.

[0851] A "QR code" is a two-dimensional barcode that encodes specific information and allows the data to be decoded using a reading device or application.

[0852] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and tone of voice to detect emotional states such as joy, surprise, or anger.

[0853] A "service server" is a server used to provide specific services (e.g., payment services, membership services), which receives data from user terminals and the generation artificial intelligence server and performs corresponding processing.

[0854] "Feedback" refers to additional information or messages provided to users after a service is provided based on their reactions and status, and is used to inform and improve the user experience.

[0855] A "private key" is key information used for digital signatures and data encryption, and is held individually by a user for access control and authentication purposes.

[0856] A "signature" is a digital signature generated using a private key to verify the authenticity and origin of data.

[0857] A "digital signature" is a signature generated for electronic data, and is used to prevent data tampering and to certify the sender.

[0858] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[0859] First, the user launches a dedicated QR code reader and management app on their device, such as a smartphone or tablet. This app functions as a centralized management system for various services (e.g., payment, membership services, etc.). When the user performs an operation to scan the QR code, the QR code is read using the device's camera and the data is temporarily saved. This data includes service identification information and transaction information.

[0860] Furthermore, the user's device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice when scanning the QR code to obtain emotional data. This emotional data is temporarily stored within the app. Typical examples of emotion engines used here include Microsoft Azure's Face API and Emotion API.

[0861] The acquired QR code data and emotion data are sent from the device to the generation AI server. This transmission is secured by using protocols such as HTTPS to ensure data security. The generation AI server analyzes the QR code data and identifies which service it corresponds to. The generation AI model used here includes, for example, a QR code analysis algorithm and data mining technology.

[0862] Based on the identified service, the server converts the QR code data into a service-specific format (e.g., payment request data). The server then retrieves store information from a database, including store name, location, transaction details, etc.

[0863] To authenticate the user, the device generates a digital signature using the user's private key and sends it to the server, which then verifies the signature using public key cryptography and proceeds to the next step if authentication is successful.

[0864] The authenticated data is then sent to the service server. For example, payment request data is sent to the payment service server, which processes the payment by approving or rejecting it. The service server then sends the processing result back to the AI ​​server, which notifies the device. The user can check the results, such as whether the payment was successful, through a dedicated QR code reader and management app.

[0865] Finally, feedback based on emotional data is important. The server analyzes the emotional data and optimizes the service it provides, for example, by displaying priority support messages or special offers if the user is feeling stressed. This series of feedback is extremely useful for improving the user experience.

[0866] As a concrete example, in a scenario where a user is purchasing a product at a retail store, the following prompt sentence is input into the generative AI model:

[0867] "The user launches the dedicated QR code reader and management app at the cash register and scans the QR code attached to the product. The device acquires the QR code data, and the emotion engine analyzes the user's facial expression to acquire emotion data."

[0868] This not only allows users to seamlessly use different services within a single app, but also enables optimal service delivery based on their emotions, resulting in a pleasant user experience.

[0869] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0870] Step 1:

[0871] The user launches a dedicated QR code reader and management app.

[0872] Specifically, the user taps the dedicated app icon on the home screen of their smartphone or tablet to launch the app.

[0873] Input: A user taps the app icon.

[0874] Output: The app will launch and show the QR code scanning screen.

[0875] Step 2:

[0876] The user scans the QR code.

[0877] The user taps the "Scan" button in the app and the device uses the camera to scan the QR code.

[0878] Input: QR code.

[0879] Output: The scanned QR code data is retrieved.

[0880] Step 3:

[0881] The device acquires emotion data.

[0882] The device's built-in camera captures the user's face, and the emotion engine analyzes facial expressions to generate emotion data. The device also captures the user's voice with a microphone and analyzes the tone of their voice.

[0883] Input: The user's facial expression and tone of voice.

[0884] Output: Emotion data representing the user's emotional state.

[0885] Step 4:

[0886] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[0887] The device sends the acquired QR code data, emotion data, and user information to the server using the HTTPS protocol.

[0888] Input: QR code data, emotion data, user information.

[0889] Output: The QR code data, emotion data, and user information are sent to the server.

[0890] Step 5:

[0891] The server parses the received data.

[0892] The server analyzes the received QR code data using the generated AI model and identifies the corresponding service.

[0893] Input: Received QR code data.

[0894] Output: Information identifying which service the QR code relates to.

[0895] Step 6:

[0896] The server converts the data into a service-specific format.

[0897] The server converts the QR code data into a service-specific format (e.g., payment request data) based on the identified service.

[0898] Input: Service identification information, QR code data.

[0899] Output: Data converted into a service-specific format.

[0900] Step 7:

[0901] The server obtains store information and performs user authentication.

[0902] The server retrieves store information from the database based on the converted data and verifies the data signed with the user's private key to authenticate the user.

[0903] Input: Transformed data, data signed with private key.

[0904] Output: Retrieved store information, user authentication results.

[0905] Step 8:

[0906] The authenticated data is sent to the service server.

[0907] The server sends the authenticated data to the target service server, which then analyzes the received data and performs the appropriate processing.

[0908] Input: Authenticated data.

[0909] Output: Data sent to the service server, response from the service server.

[0910] Step 9:

[0911] The results are notified to the user's terminal.

[0912] The server receives the response from the service server and notifies the user's device, and the user can check the result through the app.

[0913] Input: Response from the service server.

[0914] Output: The result is notified to the user's terminal and the user can check the result.

[0915] Step 10:

[0916] Provide feedback based on emotional data.

[0917] The server analyzes the emotional data, generates messages and suggestions tailored to the user, and notifies the user's device.

[0918] Input: User emotion data.

[0919] Output: Customized feedback messages and suggestions.

[0920] (Application example 2)

[0921] 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."

[0922] Conventional QR code-based service provision systems require users to use multiple QR code reader apps when using different services, resulting in a lack of convenience. Furthermore, services are provided uniformly without considering the user's emotional state, resulting in a suboptimal user experience. It is necessary to solve these issues and build a system that provides consistent, emotionally appropriate services to users.

[0923] 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.

[0924] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generation artificial intelligence server; means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for notifying the user terminal of a response from the service server; means including an emotion engine for obtaining user emotion data; and means for generating feedback to optimize the service based on the emotion data. This allows a user to seamlessly use multiple QR codes with a single application and also enables the provision of optimal services according to emotions.

[0925] A "user terminal" is a portable information terminal that can read QR codes and communicate with a server via the Internet.

[0926] A "QR code" is a type of two-dimensional barcode that uses a rectangular grid pattern to represent digital information.

[0927] The "generative artificial intelligence server" is a computer server that analyzes the received data, identifies the corresponding service, and provides the user with the most suitable information.

[0928] The "emotion engine" is software that analyzes the user's facial expressions and voice data obtained using a camera and microphone to determine the user's emotional state.

[0929] A "service-specific format" is a data format required by a particular service, such as a payment request or membership information.

[0930] "User authentication" is the process of verifying a user's identity and verifying their identity before providing services.

[0931] "Data signed with a private key" refers to data that has a digital signature encrypted with the user's private key, which verifies the authenticity of the data.

[0932] A "means for generating feedback" is a system or software for generating optimized services or messages based on analysis results and past data.

[0933] As an embodiment of the present invention, a specific method thereof will be described below.

[0934] This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[0935] Hardware and Software Use

[0936] First, a smartphone is used as the user device, which is equipped with a camera and microphone and is used to scan the QR code and to detect the user's facial expression and tone of voice.

[0937] The software used is an "emotion engine" for emotion analysis. For example, "Microsoft's Azure Face API" or "Google Cloud Vision API" are suitable. The generative AI server also includes a "generative artificial intelligence model" that analyzes data and identifies services.

[0938] Data processing and calculation

[0939] When a user scans a QR code, the smartphone camera captures the QR code data, while the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to collect emotional data.

[0940] This data is sent from the smartphone to the generation AI server. The generation AI server analyzes the QR code data and identifies which service it corresponds to. At this time, the QR code data is converted into a format specific to each service, such as payment request data or membership service data. Authentication is also performed using data signed with the user's private key.

[0941] The server retrieves the necessary store information from the database based on the converted data. Based on this information, the authenticated data is sent to the service server, where the service is processed. The response from the service server is notified to the user's device via the generation AI server.

[0942] At the same time, emotional data is used to generate feedback to optimize service delivery: for example, if a user is feeling stressed, priority support messages or special offers for the next visit will be displayed.

[0943] Specific examples

[0944] As a concrete example, consider the case where a user is shopping at a convenience store and makes an electronic payment using a QR code. The user launches a dedicated app and scans the QR code at the cash register. The smartphone camera reads the QR code, and at the same time, the emotion engine analyzes the facial expression. The QR code data and emotion data are sent to the generation artificial intelligence server, where they are analyzed and converted.

[0945] The server sends the payment request data to the payment service server and returns the result to the user's device. Once the payment is completed, messages such as "Thank you for your use" or "You will receive a special offer for your next use" are displayed based on the emotion data.

[0946] Prompt Sentence Examples

[0947] Analyze QR code data and user sentiment data to provide optimal service feedback.

[0948] QR code data: {qr_data}

[0949] Emotion data: {emotion_data}

[0950] This allows users to seamlessly use multiple QR codes in a single application and receive optimal services tailored to their emotions.

[0951] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0952] Step 1:

[0953] The user scans the QR code with their smartphone.

[0954] Input: QR code image captured by camera.

[0955] Processing: The QR code reader app on your smartphone analyzes the image and extracts the QR code data.

[0956] Output: QR code data.

[0957] Step 2:

[0958] When a user scans a QR code, the smartphone's built-in emotion engine analyzes the user's facial expressions and tone of voice.

[0959] Input: User's camera footage (facial expressions) and microphone audio (tone of voice).

[0960] Processing: The emotion engine performs facial expression recognition and voice analysis to generate emotion data.

[0961] Output: Emotion data.

[0962] Step 3:

[0963] The smartphone sends the acquired QR code data and emotion data to the generation AI server.

[0964] Input: QR code data, emotion data.

[0965] Processing: Data is sent to the generating AI server using a secure communication protocol (e.g., HTTPS).

[0966] Output: Data sent to the generating AI server.

[0967] Step 4:

[0968] The generation AI server analyzes the received QR code data and identifies the corresponding service.

[0969] Input: QR code data.

[0970] Processing: A generative AI model parses the QR code data and identifies services (e.g., payment, membership services).

[0971] Output: Service identity information.

[0972] Step 5:

[0973] Based on the identification results, the generation AI server converts the QR code data into a format specific to the service.

[0974] Input: Service identification information, QR code data.

[0975] Processing: Reconstruct the QR code data into a data format specific to each service (e.g., payment request data, membership information).

[0976] Output: The transformed service-specific data.

[0977] Step 6:

[0978] The server retrieves the necessary store and user information from the database based on the converted data.

[0979] Input: Transformed service-specific data.

[0980] Processing: Executes a database query to retrieve relevant store and user information.

[0981] Output: Store information, user information.

[0982] Step 7:

[0983] The generating AI server authenticates the user using data signed with the user's private key.

[0984] Input: User information, data signed with private key.

[0985] Processing: Verify the private key signature and perform user authentication.

[0986] Output: Authentication result.

[0987] Step 8:

[0988] The generation AI server sends the authenticated data to the service server, which processes the service.

[0989] Input: Authenticated data, service specific data.

[0990] Processing: Sends the authentication data and service-specific data as a request to the service's server to perform the service's processing (e.g., payment processing).

[0991] Output: Processing results from the service's server.

[0992] Step 9:

[0993] The response result from the service server is notified to the user device via the generated AI server.

[0994] Input: Processing result from the service's server.

[0995] Processing: The generation AI server receives the response result and notifies the user terminal.

[0996] Output: The response result sent to the user's device.

[0997] Step 10:

[0998] The generation AI server generates an appropriate feedback message based on the emotional data and notifies the user device.

[0999] Input: Emotion data, response results.

[1000] Processing: Emotional data is used to generate feedback messages (e.g., special offers for next use, support messages) to optimize service delivery.

[1001] Output: Feedback message.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] [Third embodiment]

[1006] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1007] 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.

[1008] 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).

[1009] 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.

[1010] 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.

[1011] 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).

[1012] 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.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] 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."

[1018] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[1019] Overall system flow

[1020] The system operates in the following steps.

[1021] 1. Scan the QR code

[1022] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and operates the app to scan the QR code.

[1023] The device scans the QR code and retrieves the data, which includes the service's identification information and transaction information.

[1024] 2. Data submission and analysis

[1025] The device sends the acquired QR code data to the AI ​​server. This data transmission also includes user information (e.g., user ID and session ID).

[1026] The server's generated artificial intelligence analyzes the received QR code data and identifies the corresponding service (e.g., payment service or membership service).

[1027] 3. Data Conversion

[1028] The server's artificial intelligence will then convert the data into a format specific to the particular service based on the identification results. For example, if the QR code corresponds to a payment service, it will be converted into payment request data.

[1029] 4. Obtaining store information and user authentication

[1030] The server retrieves store information from the converted data, including store name, address, transaction details, etc.

[1031] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[1032] The server verifies the user information and if authentication is successful, proceeds to the next step.

[1033] 5. Sending data to the server

[1034] The server generates the authenticated data and sends it to the server of the corresponding service, for example, payment request data in the case of a payment service.

[1035] 6. Notification of Results

[1036] The server receives the response from the service server and sends the result (success, failure, error message, etc.) to the terminal.

[1037] The user will see the result in the app. For example, if the payment is successful, the app will display "Payment Completed."

[1038] Specific examples

[1039] As an example, a case where a user purchases a product at a convenience store and makes a payment will be described.

[1040] 1. Scan the QR code

[1041] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[1042] The device acquires the QR code data and sends it to the generating artificial intelligence server.

[1043] 2. Data Analysis and Transformation

[1044] The server analyzes the QR code data and identifies it as compatible with payment services.

[1045] The server converts the data into payment service specific payment request data.

[1046] 3. Acquisition and authentication of store information

[1047] The server retrieves the store information and processes it along with the user's payment request.

[1048] The device sends data signed with the user's private key and requests authentication.

[1049] The server performs authentication and, if authentication is successful, sends the payment request data to the payment service's server.

[1050] 4. Notification of Results

[1051] The payment service's server processes the payment and, if successful, sends an acknowledgement to the server.

[1052] The server notifies the terminal of the result, and the user receives "payment completed" information via the app.

[1053] This will allow users to seamlessly use different services within a single app, greatly improving convenience.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[1057] Step 2:

[1058] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[1059] Step 3:

[1060] The device sends the acquired QR code data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[1061] Step 4:

[1062] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[1063] Step 5:

[1064] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[1065] Step 6:

[1066] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[1067] Step 7:

[1068] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[1069] Step 8:

[1070] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[1071] Step 9:

[1072] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[1073] Step 10:

[1074] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[1075] Step 11:

[1076] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[1077] Step 12:

[1078] The server transmits the processing result received from the service server to the terminal.

[1079] Step 13:

[1080] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[1081] Example 1

[1082] 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."

[1083] Conventional QR code scanning systems required separate applications for each different service, resulting in reduced user convenience. Furthermore, because each service used a different data format, data analysis and conversion were complex and processing was inefficient. Furthermore, user authentication processes were often insufficient, potentially resulting in security issues.

[1084] 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.

[1085] In this invention, the server includes a means for analyzing QR code data and converting it into a service-specific format, a means for acquiring service information based on the converted data and performing user authentication, and a means for transmitting the authenticated data to an external system for the service. This allows different services to be used seamlessly with a single application. Furthermore, the use of a generative AI model improves the accuracy of analysis and conversion, strengthening the user authentication process and improving security.

[1086] "User terminal" refers to a computer device or smart device operated by a user, which has the function of scanning QR codes and sending and receiving data.

[1087] The "server" is a central processing unit that has the function of analyzing the QR code data sent from the user terminal and converting it into a format specific to the service.

[1088] A "QR code" is a type of two-dimensional barcode, and is an information recording means for storing service identification information, transaction information, etc.

[1089] "Analysis" refers to a series of processes in which the server decodes the received QR code data and applies the information contained therein to service identification and transaction processing.

[1090] A "service-specific format" is the parsed data converted into a format required by a particular service.

[1091] "User authentication" refers to the process of verifying that a user is a legitimate user using a private key or other means.

[1092] A "generative AI model" is a large-scale artificial intelligence model used to analyze QR code data and identify services with high accuracy.

[1093] An "external system" refers to a third-party system other than a user terminal or server, which performs processing specific to the service.

[1094] "Security" refers to the state in which systems and data are protected from unauthorized access, tampering, and information leakage.

[1095] "Notification" refers to the means of transmitting information by which a server notifies a user terminal or other system of usage status or processing results.

[1096] The present invention provides a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. This system is composed of a user terminal, a server, and an external system.

[1097] The system of the present invention begins when a user launches a dedicated QR code reader and management app. The user terminal scans the QR code using hardware such as a smart device. This QR code contains service identification information and transaction information. Once the user scans the QR code, the terminal converts the data into an analyzable format and sends it to the server.

[1098] The server uses a generative AI model to analyze the received QR code data and user information (such as user ID and session ID). An example of an AI model is OpenAI's GPT-4. The AI ​​model analyzes the QR code data and identifies the corresponding service (payment service, membership service, etc.).

[1099] The server then converts the data into a format specific to the service based on the analysis results. This converted data includes payment request data, membership information, etc. The server then uses the converted data to obtain transaction location information (store name, address, transaction details, etc.).

[1100] The terminal requests authentication from the server using the user's pre-registered information. Authentication uses data signed with the user's private key. The server verifies this authentication data, and if user authentication is successful, it sends the authenticated data to an external system. The external system could be, for example, a payment service server or a member management server.

[1101] The external system returns a response to the server, and the server receives the result (success, failure, error message, etc.). The server notifies the device of this result, and the user confirms the result through a display within the app. For example, if the payment is successful, "Payment completed" is displayed.

[1102] Specific examples

[1103] As an example, let's consider the case where a user purchases an item at a convenience store and pays using a dedicated QR code reader / management app. When the user scans the QR code at the register, the device obtains the QR code data and sends it to the server. The server uses a generative AI model to analyze the QR code data and identifies it as compatible with the payment service. The server converts the data into payment request data and performs authentication using data signed with the user's private key. If authentication is successful, the payment request data is sent to the payment service's server. The payment service's server processes the payment and returns the result to the server. The server notifies the device of the result, and the user receives information that "payment completed."

[1104] Prompt Sentence Examples

[1105] 1. "QR code scan": The user launches the dedicated app and scans the QR code.

[1106] 2. "Send data to server": The device sends the QR code data and user information to the server.

[1107] 3. "Data Analysis": The server analyzes the QR code data and identifies the corresponding service.

[1108] 4. "Data Conversion": The server converts the data into a format for a specific service.

[1109] This allows users to seamlessly use different services within a single application, improving the accuracy of analysis and authentication and strengthening security.

[1110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1111] Flow and explanation for each processing step

[1112] Step 1:

[1113] The user launches the dedicated QR code reader and management app on their smart device and displays the QR code scanning screen. The user then uses the camera to scan the QR code. The device analyzes the image captured by the camera and extracts the QR code data. The input data is the QR code image, and the output data is the analyzed QR code information. The device then converts the scanned data into a specific format.

[1114] Specific behavior:

[1115] The user taps on the app to open the QR code scanning screen.

[1116] The camera on your smart device will launch and take a picture of the QR code.

[1117] The device analyzes the image data from the camera and extracts the QR code information as text data.

[1118] Step 2:

[1119] The device sends the acquired QR code data to the server. At this time, user information (user ID and session ID) is also included. The input data is the QR code data and user information, and the output data is the data sent to the server. The server confirms receipt.

[1120] Specific behavior:

[1121] The device sends data to the server using Wi-Fi or a mobile network.

[1122] The server receives the data and returns a response confirming receipt to the terminal.

[1123] Step 3:

[1124] The server uses the generated AI model to analyze the QR code data and identify the corresponding service. The input data is the QR code data, and the output data is the service information identified by the analysis. The server saves the analysis results as a specific record.

[1125] Specific behavior:

[1126] The server invokes a generative AI model (e.g., GPT-4) to begin the analysis process.

[1127] The model analyzes the QR code data and identifies the service type (payment, membership).

[1128] The identification results are stored in a database.

[1129] Step 4:

[1130] The server converts the data into a format specific to the specific service based on the identification result. The input data is the identified service information, and the output data is the data converted into the format of the specific service. The server temporarily stores the converted data.

[1131] Specific behavior:

[1132] The server invokes a specific transformation function to convert the data into a service-specific format.

[1133] The transformed data is temporarily stored for the next processing step.

[1134] Step 5:

[1135] The server retrieves the transaction location information from the transformed data. The input data is the transformed data, and the output data is the transaction location details. The server associates this information with the user's payment request.

[1136] Specific behavior:

[1137] The server searches the database for transaction location information (store name, location).

[1138] Link the information found to the payment request data.

[1139] Step 6:

[1140] The terminal generates data signed with the user's private key and requests authentication from the server. The input data is the user's authentication information, and the output data is the signed authentication data. The server verifies this authentication data.

[1141] Specific behavior:

[1142] The device generates authentication data signed using the user's private key.

[1143] The server receives the signed data and performs an authentication process.

[1144] Step 7:

[1145] The server sends the authenticated data to the external system. The input data is the authenticated data, and the output data is the data sent to the external system. The external system returns the processing result.

[1146] Specific behavior:

[1147] The server sends the payment request data to the external system via API.

[1148] The external system returns a response, which the server receives.

[1149] Step 8:

[1150] The server notifies the device of the response result from the external system. The input data is the response result from the external system, and the output data is the notification message. The user can check the result through the app.

[1151] Specific behavior:

[1152] The response received by the server is analyzed.

[1153] The analysis results are sent to the terminal as a notification message.

[1154] The device displays the notification on the screen and the user confirms the results.

[1155] Through these steps, users can seamlessly access different services using a single application. At each processing step, appropriate data processing and calculations are performed based on specific input data, and the output data is passed on to the next step.

[1156] (Application example 1)

[1157] 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."

[1158] Currently, when using different electronic payment services or point card services, users must operate multiple applications individually, placing a heavy operational burden on them. Furthermore, each service requires different operating methods and authentication methods, which can be difficult for users to understand and often leads to confusion. This can lead to operational errors and reduced convenience.

[1159] 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.

[1160] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generating AI server; means for the generating AI server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for the generating AI server to analyze the contents of the QR code and comprehensively identify and process multiple electronic payment services, point card services, membership card services, coupon usage, etc.; and means for notifying the user terminal of responses from the service servers. This enables users to easily and seamlessly use multiple services with a single application.

[1161] A "user terminal" is an electronic device that has the function of reading a QR code and sending the data to the generating artificial intelligence server.

[1162] The "generative artificial intelligence server" is a server that has the ability to analyze QR code data and convert it into a format specific to the service.

[1163] A "QR code" is a form of two-dimensional barcode and a means of storing information digitally.

[1164] A "service-specific format" is a specific data format required by each service, and is a data structure that satisfies the different requirements for each service.

[1165] "Converted data" refers to the data after the QR code data analyzed by the generation artificial intelligence server is converted into a format specific to the service.

[1166] "User authentication" is a procedure for verifying that a user is a legitimate user, and may involve the use of data signed with a private key.

[1167] "Electronic payment services" are payment services carried out using digital technology.

[1168] A "point card service" is a system in which users earn points based on purchases and service usage, and manage and use those points.

[1169] A "membership card service" is a card system issued by stores or service providers to prove membership and receive benefits and services.

[1170] A "coupon" is a ticket or digital code that entitles you to a discount or special offer on a specific product or service.

[1171] A "server response" is the result or information that a service's server returns in response to a request.

[1172] "Notification" refers to the act of sending the server's response result to the user's terminal to notify the user.

[1173] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[1174] Overall system flow

[1175] 1. Scan the QR code

[1176] The user launches a dedicated QR code reader and management app, displays the QR code scanning screen, and uses the camera on their smartphone or tablet to scan the QR code and obtain the data. For example, the ZXing library can be used to parse the QR code.

[1177] 2. Data submission and analysis

[1178] The smartphone sends the acquired QR code data to the AI ​​server, along with user information (e.g., user ID and session ID).

[1179] The generative artificial intelligence server uses generative AI models such as the OpenAI GPT-4 API to analyze the QR code data and identify the relevant service (electronic payment service, point card service, membership card service, coupon use, etc.).

[1180] 3. Data Conversion

[1181] Based on the analysis results, the generation AI server converts the data into a format specific to a particular service. For example, if the QR code corresponds to an electronic payment service, it converts it into payment request data.

[1182] 4. Obtaining store information and user authentication

[1183] The artificial intelligence server then obtains store information from the converted data, including the store name, location, and transaction details.

[1184] The smartphone sends the data signed with the user's private key to the generation artificial intelligence server and requests authentication.

[1185] The generation artificial intelligence server verifies the user information and, if authentication is successful, proceeds to the next step.

[1186] 5. Sending data to the service server

[1187] The generating artificial intelligence server generates authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[1188] 6. Notification of Results

[1189] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[1190] The AI ​​server then sends the results to the smartphone, where the user can confirm the results. For example, if the payment is successful, the app will display "Payment Complete."

[1191] Specific examples

[1192] Consider a case where a user purchases an item at a convenience store and makes a payment.

[1193] 1. The user launches the dedicated QR code reader and management app at the cash register and scans the QR code.

[1194] 2. The QR code data is sent from the smartphone to the generation artificial intelligence server.

[1195] 3. The generating artificial intelligence server analyzes the QR code data and identifies that it corresponds to an electronic payment service.

[1196] 4. The data is converted into payment request data.

[1197] 5. The artificial intelligence server generated based on the signed data sent from the smartphone performs user authentication, and the authentication is successful.

[1198] 6. The payment request data is sent to the appropriate electronic payment service server.

[1199] 7. The service server processes the payment, and if successful, notifies the smartphone of the result, and the user receives a "payment completed" notification.

[1200] Example prompts to input to the generative AI model

[1201] "Analyze the QR code data scanned by the user using a dedicated app, identify which service it corresponds to, and convert the data into the format of the corresponding service.

[1202] QR code data: {QR_CODE_DATA}

[1203] User information: {USER_INFO}"

[1204] In this way, the present invention realizes a system that allows users to seamlessly use a variety of services with a single application by utilizing user devices such as smartphones and tablets and linking them with a generation artificial intelligence server.

[1205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1206] Step 1:

[1207] The user launches the dedicated QR code reader and management app and displays the QR code scanning screen. The user's device uses the smartphone camera to scan the QR code and acquires the data. The acquired QR code data is temporarily stored on the device.

[1208] Input: QR code image

[1209] Output: QR code data

[1210] Specific operation: When the user presses the QR code scan button, the camera is activated and the QR code is read.

[1211] Step 2:

[1212] The user device sends the acquired QR code data to the AI ​​server, along with the user identification information (e.g., user ID and session ID).

[1213] Input: QR code data, user identification information

[1214] Output: Request data to the server

[1215] Specific operation: QR code data and user identification information are sent to the server as an HTTP request.

[1216] Step 3:

[1217] The generative AI server analyzes the received QR code data and identifies the applicable service. Using a generative AI model (e.g., OpenAI GPT-4), it analyzes the QR code data and identifies applicable services (e.g., electronic payment, point cards, membership cards, coupons, etc.).

[1218] Input: QR code data

[1219] Output: Service identification information

[1220] Specific operation: QR code data along with a prompt sentence is input into the generative AI model, and service identification information is obtained as the model output.

[1221] Step 4:

[1222] The generating artificial intelligence server converts the QR code data into a format specific to the particular service, such as payment request data for an electronic payment service, depending on the identified service.

[1223] Input: Service identification information, QR code data

[1224] Output: Data in service-specific format

[1225] Specific operation: Based on the identification information, convert the QR code data into the appropriate service format.

[1226] Step 5:

[1227] The artificial intelligence server then obtains store information from the converted data, including the store name, address, and transaction details.

[1228] Input: Data in service-specific format

[1229] Output: Store information

[1230] Specific operation: Search and obtain corresponding store information from the database.

[1231] Step 6:

[1232] To perform user authentication, the user terminal sends data signed with the private key to the generating artificial intelligence server.

[1233] Input: Data signed with the user's private key

[1234] Output: Authentication request data

[1235] Specific operation: The signature is sent to the server using the user's private key.

[1236] Step 7:

[1237] The generation artificial intelligence server verifies the sent signature and performs user authentication. If authentication is successful, proceed to the next step.

[1238] Input: Authentication request data

[1239] Output: Authentication result

[1240] Specific behavior: Decrypts the signature and verifies the user's authenticity.

[1241] Step 8:

[1242] The generating artificial intelligence server generates the authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[1243] Input: Authentication result, data in service-specific format

[1244] Output: Request data to the service server

[1245] Specific operation: Request data is generated based on the authentication result and sent as an HTTP request to the server of the corresponding service.

[1246] Step 9:

[1247] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[1248] Input: Request data to the service server

[1249] Output: Service server response

[1250] Specific behavior: Processes the request, generates a result, and sends it back.

[1251] Step 10:

[1252] The generating artificial intelligence server notifies the user's device of the results, and the user can confirm the results through the display within the app.

[1253] Input: Service server response

[1254] Output: Notification to user device

[1255] Specific operation: The response result is sent to the user's device and displayed within the app.

[1256] 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.

[1257] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[1258] Overall system flow

[1259] The system operates in the following steps.

[1260] 1. Scan the QR code

[1261] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[1262] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[1263] 2. Acquiring Emotion Data

[1264] When a user scans a QR code, the device's built-in emotion engine analyzes the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[1265] 3. Data submission and analysis

[1266] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[1267] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[1268] 4. Data Conversion

[1269] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[1270] 5. Obtaining store information and user authentication

[1271] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[1272] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[1273] The server verifies the user information and if authentication is successful, proceeds to the next step.

[1274] 6. Sending data to the server

[1275] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[1276] 7. Notification of Results

[1277] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[1278] The service's server generates the processing result and sends it to the server.

[1279] The server transmits the processing result received from the service server to the terminal.

[1280] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[1281] 8. Feedback based on emotional data

[1282] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[1283] The server improves the user experience by notifying the device of appropriate messages and service suggestions based on the user's emotions.

[1284] Specific examples

[1285] As an example, consider a case where a user purchases an item at a retail store and pays using a QR code.

[1286] 1. Scan the QR code

[1287] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[1288] The device acquires the QR code data, and the emotion engine analyzes the user's facial expressions to acquire emotional data.

[1289] 2. Data submission and analysis

[1290] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[1291] The server analyzes the content of the QR code and identifies it as compatible with the payment service.

[1292] 3. Data Conversion and Authentication

[1293] The server converts the QR code data into payment request data and obtains store information.

[1294] The device sends data signed with the user's private key, and the server performs authentication.

[1295] 4. Payment Processing

[1296] The server sends the authenticated data to the payment service's server, which processes the payment.

[1297] The service server notifies the server of the payment result, which is then transmitted to the terminal.

[1298] 5. Notification of results and emotional feedback

[1299] The user confirms the payment in the app.

[1300] At the same time, based on the emotional data analyzed by the emotion engine, the server displays an appropriate message to the user (e.g., thank you for your satisfaction) or information about special offers for the next use.

[1301] This not only allows users to seamlessly use different services within a single app, but also provides services tailored to their emotions, resulting in a more comfortable user experience.

[1302] The processing flow will be explained below.

[1303] Step 1:

[1304] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[1305] Step 2:

[1306] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[1307] Step 3:

[1308] The device activates its built-in emotion engine to analyze the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[1309] Step 4:

[1310] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[1311] Step 5:

[1312] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[1313] Step 6:

[1314] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[1315] Step 7:

[1316] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[1317] Step 8:

[1318] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[1319] Step 9:

[1320] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[1321] Step 10:

[1322] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[1323] Step 11:

[1324] The service's server analyzes the received data and takes appropriate action, for example approving or denying the payment.

[1325] Step 12:

[1326] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[1327] Step 13:

[1328] The server transmits the processing result received from the service server to the terminal.

[1329] Step 14:

[1330] The terminal notifies the user, who then checks the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[1331] Step 15:

[1332] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[1333] Step 16:

[1334] The server then sends appropriate messages and service suggestions to the device based on the user's emotions, improving the user experience. For example, if the user is surprised, the server will display a message such as "Thank you for the surprise" to provide services that reflect the user's emotions.

[1335] Example 2

[1336] 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."

[1337] Conventional QR code readers and management apps have difficulty seamlessly supporting multiple different services, forcing users to switch between apps for each service, resulting in a lack of convenience. Furthermore, services are provided without consideration of user feelings, which can result in a poor user experience. Therefore, there is a need for a system that can manage multiple services in one place and provide optimal services that respond to user feelings.

[1338] 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.

[1339] In this invention, the server includes means for the user terminal to read QR codes corresponding to different services and transmit the QR code data to the generation artificial intelligence server, means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service, means for acquiring service information based on the converted data and performing user authentication, means for acquiring user emotion data using a built-in emotion engine, means for transmitting the acquired emotion data to the generation artificial intelligence server, means for generating feedback based on the emotion data and notifying the user terminal, means for transmitting the authenticated data to the service server, and means for notifying the user terminal of a response from the service server. This not only allows the user to seamlessly use different services with a single app, but also enables the provision of optimal services according to emotions.

[1340] "User terminal" means an electronic device operated by a user that has the functions of reading QR codes, acquiring emotion data, and sending and receiving data.

[1341] The "generative artificial intelligence server" is a server system that analyzes the received QR code data and emotion data, identifies appropriate services, converts data, and supports the provision of optimal services.

[1342] A "QR code" is a two-dimensional barcode that encodes specific information and allows the data to be decoded using a reading device or application.

[1343] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and tone of voice to detect emotional states such as joy, surprise, or anger.

[1344] A "service server" is a server used to provide specific services (e.g., payment services, membership services), which receives data from user terminals and the generation artificial intelligence server and performs corresponding processing.

[1345] "Feedback" refers to additional information or messages provided to users after a service is provided based on their reactions and status, and is used to inform and improve the user experience.

[1346] A "private key" is key information used for digital signatures and data encryption, and is held individually by a user for access control and authentication purposes.

[1347] A "signature" is a digital signature generated using a private key to verify the authenticity and origin of data.

[1348] A "digital signature" is a signature generated for electronic data, and is used to prevent data tampering and to certify the sender.

[1349] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[1350] First, the user launches a dedicated QR code reader and management app on their device, such as a smartphone or tablet. This app functions as a centralized management system for various services (e.g., payment, membership services, etc.). When the user performs an operation to scan the QR code, the QR code is read using the device's camera and the data is temporarily saved. This data includes service identification information and transaction information.

[1351] Furthermore, the user's device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice when scanning the QR code to obtain emotional data. This emotional data is temporarily stored within the app. Typical examples of emotion engines used here include Microsoft Azure's Face API and Emotion API.

[1352] The acquired QR code data and emotion data are sent from the device to the generation AI server. This transmission is secured by using protocols such as HTTPS to ensure data security. The generation AI server analyzes the QR code data and identifies which service it corresponds to. The generation AI model used here includes, for example, a QR code analysis algorithm and data mining technology.

[1353] Based on the identified service, the server converts the QR code data into a service-specific format (e.g., payment request data). The server then retrieves store information from a database, including store name, location, transaction details, etc.

[1354] To authenticate the user, the device generates a digital signature using the user's private key and sends it to the server, which then verifies the signature using public key cryptography and proceeds to the next step if authentication is successful.

[1355] The authenticated data is then sent to the service server. For example, payment request data is sent to the payment service server, which processes the payment by approving or rejecting it. The service server then sends the processing result back to the AI ​​server, which notifies the device. The user can check the results, such as whether the payment was successful, through a dedicated QR code reader and management app.

[1356] Finally, feedback based on emotional data is important. The server analyzes the emotional data and optimizes the service it provides, for example, by displaying priority support messages or special offers if the user is feeling stressed. This series of feedback is extremely useful for improving the user experience.

[1357] As a concrete example, in a scenario where a user is purchasing a product at a retail store, the following prompt sentence is input into the generative AI model:

[1358] "The user launches the dedicated QR code reader and management app at the cash register and scans the QR code attached to the product. The device acquires the QR code data, and the emotion engine analyzes the user's facial expression to acquire emotion data."

[1359] This not only allows users to seamlessly use different services within a single app, but also enables optimal service delivery based on their emotions, resulting in a pleasant user experience.

[1360] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1361] Step 1:

[1362] The user launches a dedicated QR code reader and management app.

[1363] Specifically, the user taps the dedicated app icon on the home screen of their smartphone or tablet to launch the app.

[1364] Input: A user taps the app icon.

[1365] Output: The app will launch and show the QR code scanning screen.

[1366] Step 2:

[1367] The user scans the QR code.

[1368] The user taps the "Scan" button in the app and the device uses the camera to scan the QR code.

[1369] Input: QR code.

[1370] Output: The scanned QR code data is retrieved.

[1371] Step 3:

[1372] The device acquires emotion data.

[1373] The device's built-in camera captures the user's face, and the emotion engine analyzes facial expressions to generate emotion data. The device also captures the user's voice with a microphone and analyzes the tone of their voice.

[1374] Input: The user's facial expression and tone of voice.

[1375] Output: Emotion data representing the user's emotional state.

[1376] Step 4:

[1377] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[1378] The device sends the acquired QR code data, emotion data, and user information to the server using the HTTPS protocol.

[1379] Input: QR code data, emotion data, user information.

[1380] Output: The QR code data, emotion data, and user information are sent to the server.

[1381] Step 5:

[1382] The server parses the received data.

[1383] The server analyzes the received QR code data using the generated AI model and identifies the corresponding service.

[1384] Input: Received QR code data.

[1385] Output: Information identifying which service the QR code relates to.

[1386] Step 6:

[1387] The server converts the data into a service-specific format.

[1388] The server converts the QR code data into a service-specific format (e.g., payment request data) based on the identified service.

[1389] Input: Service identification information, QR code data.

[1390] Output: Data converted into a service-specific format.

[1391] Step 7:

[1392] The server obtains store information and performs user authentication.

[1393] The server retrieves store information from the database based on the converted data and verifies the data signed with the user's private key to authenticate the user.

[1394] Input: Transformed data, data signed with private key.

[1395] Output: Retrieved store information, user authentication results.

[1396] Step 8:

[1397] The authenticated data is sent to the service server.

[1398] The server sends the authenticated data to the target service server, which then analyzes the received data and performs the appropriate processing.

[1399] Input: Authenticated data.

[1400] Output: Data sent to the service server, response from the service server.

[1401] Step 9:

[1402] The results are notified to the user's terminal.

[1403] The server receives the response from the service server and notifies the user's device, and the user can check the result through the app.

[1404] Input: Response from the service server.

[1405] Output: The result is notified to the user's terminal and the user can check the result.

[1406] Step 10:

[1407] Provide feedback based on emotional data.

[1408] The server analyzes the emotional data, generates messages and suggestions tailored to the user, and notifies the user's device.

[1409] Input: User emotion data.

[1410] Output: Customized feedback messages and suggestions.

[1411] (Application example 2)

[1412] 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."

[1413] Conventional QR code-based service provision systems require users to use multiple QR code reader apps when using different services, resulting in a lack of convenience. Furthermore, services are provided uniformly without considering the user's emotional state, resulting in a suboptimal user experience. It is necessary to solve these issues and build a system that provides consistent, emotionally appropriate services to users.

[1414] 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.

[1415] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generation artificial intelligence server; means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for notifying the user terminal of a response from the service server; means including an emotion engine for obtaining user emotion data; and means for generating feedback to optimize the service based on the emotion data. This allows a user to seamlessly use multiple QR codes with a single application and also enables the provision of optimal services according to emotions.

[1416] A "user terminal" is a portable information terminal that can read QR codes and communicate with a server via the Internet.

[1417] A "QR code" is a type of two-dimensional barcode that uses a rectangular grid pattern to represent digital information.

[1418] The "generative artificial intelligence server" is a computer server that analyzes the received data, identifies the corresponding service, and provides the user with the most suitable information.

[1419] The "emotion engine" is software that analyzes the user's facial expressions and voice data obtained using a camera and microphone to determine the user's emotional state.

[1420] A "service-specific format" is a data format required by a particular service, such as a payment request or membership information.

[1421] "User authentication" is the process of verifying a user's identity and verifying their identity before providing services.

[1422] "Data signed with a private key" refers to data that has a digital signature encrypted with the user's private key, which verifies the authenticity of the data.

[1423] A "means for generating feedback" is a system or software for generating optimized services or messages based on analysis results and past data.

[1424] As an embodiment of the present invention, a specific method thereof will be described below.

[1425] This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[1426] Hardware and Software Use

[1427] First, a smartphone is used as the user device, which is equipped with a camera and microphone and is used to scan the QR code and to detect the user's facial expression and tone of voice.

[1428] The software used is an "emotion engine" for emotion analysis. For example, "Microsoft's Azure Face API" or "Google Cloud Vision API" are suitable. The generative AI server also includes a "generative artificial intelligence model" that analyzes data and identifies services.

[1429] Data processing and calculation

[1430] When a user scans a QR code, the smartphone camera captures the QR code data, while the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to collect emotional data.

[1431] This data is sent from the smartphone to the generation AI server. The generation AI server analyzes the QR code data and identifies which service it corresponds to. At this time, the QR code data is converted into a format specific to each service, such as payment request data or membership service data. Authentication is also performed using data signed with the user's private key.

[1432] The server retrieves the necessary store information from the database based on the converted data. Based on this information, the authenticated data is sent to the service server, where the service is processed. The response from the service server is notified to the user's device via the generation AI server.

[1433] At the same time, emotional data is used to generate feedback to optimize service delivery: for example, if a user is feeling stressed, priority support messages or special offers for the next visit will be displayed.

[1434] Specific examples

[1435] As a concrete example, consider the case where a user is shopping at a convenience store and makes an electronic payment using a QR code. The user launches a dedicated app and scans the QR code at the cash register. The smartphone camera reads the QR code, and at the same time, the emotion engine analyzes the facial expression. The QR code data and emotion data are sent to the generation artificial intelligence server, where they are analyzed and converted.

[1436] The server sends the payment request data to the payment service server and returns the result to the user's device. Once the payment is completed, messages such as "Thank you for your use" or "You will receive a special offer for your next use" are displayed based on the emotion data.

[1437] Prompt Sentence Examples

[1438] Analyze QR code data and user sentiment data to provide optimal service feedback.

[1439] QR code data: {qr_data}

[1440] Emotion data: {emotion_data}

[1441] This allows users to seamlessly use multiple QR codes in a single application and receive optimal services tailored to their emotions.

[1442] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1443] Step 1:

[1444] The user scans the QR code with their smartphone.

[1445] Input: QR code image captured by camera.

[1446] Processing: The QR code reader app on your smartphone analyzes the image and extracts the QR code data.

[1447] Output: QR code data.

[1448] Step 2:

[1449] When a user scans a QR code, the smartphone's built-in emotion engine analyzes the user's facial expressions and tone of voice.

[1450] Input: User's camera footage (facial expressions) and microphone audio (tone of voice).

[1451] Processing: The emotion engine performs facial expression recognition and voice analysis to generate emotion data.

[1452] Output: Emotion data.

[1453] Step 3:

[1454] The smartphone sends the acquired QR code data and emotion data to the generation AI server.

[1455] Input: QR code data, emotion data.

[1456] Processing: Data is sent to the generating AI server using a secure communication protocol (e.g., HTTPS).

[1457] Output: Data sent to the generating AI server.

[1458] Step 4:

[1459] The generation AI server analyzes the received QR code data and identifies the corresponding service.

[1460] Input: QR code data.

[1461] Processing: A generative AI model parses the QR code data and identifies services (e.g., payment, membership services).

[1462] Output: Service identity information.

[1463] Step 5:

[1464] Based on the identification results, the generation AI server converts the QR code data into a format specific to the service.

[1465] Input: Service identification information, QR code data.

[1466] Processing: Reconstruct the QR code data into a data format specific to each service (e.g., payment request data, membership information).

[1467] Output: The transformed service-specific data.

[1468] Step 6:

[1469] The server retrieves the necessary store and user information from the database based on the converted data.

[1470] Input: Transformed service-specific data.

[1471] Processing: Executes a database query to retrieve relevant store and user information.

[1472] Output: Store information, user information.

[1473] Step 7:

[1474] The generating AI server authenticates the user using data signed with the user's private key.

[1475] Input: User information, data signed with private key.

[1476] Processing: Verify the private key signature and perform user authentication.

[1477] Output: Authentication result.

[1478] Step 8:

[1479] The generation AI server sends the authenticated data to the service server, which processes the service.

[1480] Input: Authenticated data, service specific data.

[1481] Processing: Sends the authentication data and service-specific data as a request to the service's server to perform the service's processing (e.g., payment processing).

[1482] Output: Processing results from the service's server.

[1483] Step 9:

[1484] The response result from the service server is notified to the user device via the generated AI server.

[1485] Input: Processing result from the service's server.

[1486] Processing: The generation AI server receives the response result and notifies the user terminal.

[1487] Output: The response result sent to the user's device.

[1488] Step 10:

[1489] The generation AI server generates an appropriate feedback message based on the emotional data and notifies the user device.

[1490] Input: Emotion data, response results.

[1491] Processing: Emotional data is used to generate feedback messages (e.g., special offers for next use, support messages) to optimize service delivery.

[1492] Output: Feedback message.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] [Fourth embodiment]

[1497] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1498] 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.

[1499] 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).

[1500] 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.

[1501] 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.

[1502] 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).

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] 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.

[1509] 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."

[1510] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[1511] Overall system flow

[1512] The system operates in the following steps.

[1513] 1. Scan the QR code

[1514] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and operates the app to scan the QR code.

[1515] The device scans the QR code and retrieves the data, which includes the service's identification information and transaction information.

[1516] 2. Data submission and analysis

[1517] The device sends the acquired QR code data to the AI ​​server. This data transmission also includes user information (e.g., user ID and session ID).

[1518] The server's generated artificial intelligence analyzes the received QR code data and identifies the corresponding service (e.g., payment service or membership service).

[1519] 3. Data Conversion

[1520] The server's artificial intelligence will then convert the data into a format specific to the particular service based on the identification results. For example, if the QR code corresponds to a payment service, it will be converted into payment request data.

[1521] 4. Obtaining store information and user authentication

[1522] The server retrieves store information from the converted data, including store name, address, transaction details, etc.

[1523] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[1524] The server verifies the user information and if authentication is successful, proceeds to the next step.

[1525] 5. Sending data to the server

[1526] The server generates the authenticated data and sends it to the server of the corresponding service, for example, payment request data in the case of a payment service.

[1527] 6. Notification of Results

[1528] The server receives the response from the service server and sends the result (success, failure, error message, etc.) to the terminal.

[1529] The user will see the result in the app. For example, if the payment is successful, the app will display "Payment Completed."

[1530] Specific examples

[1531] As an example, a case where a user purchases a product at a convenience store and makes a payment will be described.

[1532] 1. Scan the QR code

[1533] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[1534] The device acquires the QR code data and sends it to the generating artificial intelligence server.

[1535] 2. Data Analysis and Transformation

[1536] The server analyzes the QR code data and identifies it as compatible with payment services.

[1537] The server converts the data into payment service specific payment request data.

[1538] 3. Acquisition and authentication of store information

[1539] The server retrieves the store information and processes it along with the user's payment request.

[1540] The device sends data signed with the user's private key and requests authentication.

[1541] The server performs authentication and, if authentication is successful, sends the payment request data to the payment service's server.

[1542] 4. Notification of Results

[1543] The payment service's server processes the payment and, if successful, sends an acknowledgement to the server.

[1544] The server notifies the terminal of the result, and the user receives "payment completed" information via the app.

[1545] This will allow users to seamlessly use different services within a single app, greatly improving convenience.

[1546] The processing flow will be explained below.

[1547] Step 1:

[1548] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[1549] Step 2:

[1550] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[1551] Step 3:

[1552] The device sends the acquired QR code data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[1553] Step 4:

[1554] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[1555] Step 5:

[1556] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[1557] Step 6:

[1558] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[1559] Step 7:

[1560] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[1561] Step 8:

[1562] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[1563] Step 9:

[1564] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[1565] Step 10:

[1566] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[1567] Step 11:

[1568] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[1569] Step 12:

[1570] The server transmits the processing result received from the service server to the terminal.

[1571] Step 13:

[1572] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[1573] Example 1

[1574] 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."

[1575] Conventional QR code scanning systems required separate applications for each different service, resulting in reduced user convenience. Furthermore, because each service used a different data format, data analysis and conversion were complex and processing was inefficient. Furthermore, user authentication processes were often insufficient, potentially resulting in security issues.

[1576] 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.

[1577] In this invention, the server includes a means for analyzing QR code data and converting it into a service-specific format, a means for acquiring service information based on the converted data and performing user authentication, and a means for transmitting the authenticated data to an external system for the service. This allows different services to be used seamlessly with a single application. Furthermore, the use of a generative AI model improves the accuracy of analysis and conversion, strengthening the user authentication process and improving security.

[1578] "User terminal" refers to a computer device or smart device operated by a user, which has the function of scanning QR codes and sending and receiving data.

[1579] The "server" is a central processing unit that has the function of analyzing the QR code data sent from the user terminal and converting it into a format specific to the service.

[1580] A "QR code" is a type of two-dimensional barcode, and is an information recording means for storing service identification information, transaction information, etc.

[1581] "Analysis" refers to a series of processes in which the server decodes the received QR code data and applies the information contained therein to service identification and transaction processing.

[1582] A "service-specific format" is the parsed data converted into a format required by a particular service.

[1583] "User authentication" refers to the process of verifying that a user is a legitimate user using a private key or other means.

[1584] A "generative AI model" is a large-scale artificial intelligence model used to analyze QR code data and identify services with high accuracy.

[1585] An "external system" refers to a third-party system other than a user terminal or server, which performs processing specific to the service.

[1586] "Security" refers to the state in which systems and data are protected from unauthorized access, tampering, and information leakage.

[1587] "Notification" refers to the means of transmitting information by which a server notifies a user terminal or other system of usage status or processing results.

[1588] The present invention provides a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. This system is composed of a user terminal, a server, and an external system.

[1589] The system of the present invention begins when a user launches a dedicated QR code reader and management app. The user terminal scans the QR code using hardware such as a smart device. This QR code contains service identification information and transaction information. Once the user scans the QR code, the terminal converts the data into an analyzable format and sends it to the server.

[1590] The server uses a generative AI model to analyze the received QR code data and user information (such as user ID and session ID). An example of an AI model is OpenAI's GPT-4. The AI ​​model analyzes the QR code data and identifies the corresponding service (payment service, membership service, etc.).

[1591] The server then converts the data into a format specific to the service based on the analysis results. This converted data includes payment request data, membership information, etc. The server then uses the converted data to obtain transaction location information (store name, address, transaction details, etc.).

[1592] The terminal requests authentication from the server using the user's pre-registered information. Authentication uses data signed with the user's private key. The server verifies this authentication data, and if user authentication is successful, it sends the authenticated data to an external system. The external system could be, for example, a payment service server or a member management server.

[1593] The external system returns a response to the server, and the server receives the result (success, failure, error message, etc.). The server notifies the device of this result, and the user confirms the result through a display within the app. For example, if the payment is successful, "Payment completed" is displayed.

[1594] Specific examples

[1595] As an example, let's consider the case where a user purchases an item at a convenience store and pays using a dedicated QR code reader / management app. When the user scans the QR code at the register, the device obtains the QR code data and sends it to the server. The server uses a generative AI model to analyze the QR code data and identifies it as compatible with the payment service. The server converts the data into payment request data and performs authentication using data signed with the user's private key. If authentication is successful, the payment request data is sent to the payment service's server. The payment service's server processes the payment and returns the result to the server. The server notifies the device of the result, and the user receives information that "payment completed."

[1596] Prompt Sentence Examples

[1597] 1. "QR code scan": The user launches the dedicated app and scans the QR code.

[1598] 2. "Send data to server": The device sends the QR code data and user information to the server.

[1599] 3. "Data Analysis": The server analyzes the QR code data and identifies the corresponding service.

[1600] 4. "Data Conversion": The server converts the data into a format for a specific service.

[1601] This allows users to seamlessly use different services within a single application, improving the accuracy of analysis and authentication and strengthening security.

[1602] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1603] Flow and explanation for each processing step

[1604] Step 1:

[1605] The user launches the dedicated QR code reader and management app on their smart device and displays the QR code scanning screen. The user then uses the camera to scan the QR code. The device analyzes the image captured by the camera and extracts the QR code data. The input data is the QR code image, and the output data is the analyzed QR code information. The device then converts the scanned data into a specific format.

[1606] Specific behavior:

[1607] The user taps on the app to open the QR code scanning screen.

[1608] The camera on your smart device will launch and take a picture of the QR code.

[1609] The device analyzes the image data from the camera and extracts the QR code information as text data.

[1610] Step 2:

[1611] The device sends the acquired QR code data to the server. At this time, user information (user ID and session ID) is also included. The input data is the QR code data and user information, and the output data is the data sent to the server. The server confirms receipt.

[1612] Specific behavior:

[1613] The device sends data to the server using Wi-Fi or a mobile network.

[1614] The server receives the data and returns a response confirming receipt to the terminal.

[1615] Step 3:

[1616] The server uses the generated AI model to analyze the QR code data and identify the corresponding service. The input data is the QR code data, and the output data is the service information identified by the analysis. The server saves the analysis results as a specific record.

[1617] Specific behavior:

[1618] The server invokes a generative AI model (e.g., GPT-4) to begin the analysis process.

[1619] The model analyzes the QR code data and identifies the service type (payment, membership).

[1620] The identification results are stored in a database.

[1621] Step 4:

[1622] The server converts the data into a format specific to the specific service based on the identification result. The input data is the identified service information, and the output data is the data converted into the format of the specific service. The server temporarily stores the converted data.

[1623] Specific behavior:

[1624] The server invokes a specific transformation function to convert the data into a service-specific format.

[1625] The transformed data is temporarily stored for the next processing step.

[1626] Step 5:

[1627] The server retrieves the transaction location information from the transformed data. The input data is the transformed data, and the output data is the transaction location details. The server associates this information with the user's payment request.

[1628] Specific behavior:

[1629] The server searches the database for transaction location information (store name, location).

[1630] Link the information found to the payment request data.

[1631] Step 6:

[1632] The terminal generates data signed with the user's private key and requests authentication from the server. The input data is the user's authentication information, and the output data is the signed authentication data. The server verifies this authentication data.

[1633] Specific behavior:

[1634] The device generates authentication data signed using the user's private key.

[1635] The server receives the signed data and performs an authentication process.

[1636] Step 7:

[1637] The server sends the authenticated data to the external system. The input data is the authenticated data, and the output data is the data sent to the external system. The external system returns the processing result.

[1638] Specific behavior:

[1639] The server sends the payment request data to the external system via API.

[1640] The external system returns a response, which the server receives.

[1641] Step 8:

[1642] The server notifies the device of the response result from the external system. The input data is the response result from the external system, and the output data is the notification message. The user can check the result through the app.

[1643] Specific behavior:

[1644] The response received by the server is analyzed.

[1645] The analysis results are sent to the terminal as a notification message.

[1646] The device displays the notification on the screen and the user confirms the results.

[1647] Through these steps, users can seamlessly access different services using a single application. At each processing step, appropriate data processing and calculations are performed based on specific input data, and the output data is passed on to the next step.

[1648] (Application example 1)

[1649] 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."

[1650] Currently, when using different electronic payment services or point card services, users must operate multiple applications individually, placing a heavy operational burden on them. Furthermore, each service requires different operating methods and authentication methods, which can be difficult for users to understand and often leads to confusion. This can lead to operational errors and reduced convenience.

[1651] 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.

[1652] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generating AI server; means for the generating AI server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for the generating AI server to analyze the contents of the QR code and comprehensively identify and process multiple electronic payment services, point card services, membership card services, coupon usage, etc.; and means for notifying the user terminal of responses from the service servers. This enables users to easily and seamlessly use multiple services with a single application.

[1653] A "user terminal" is an electronic device that has the function of reading a QR code and sending the data to the generating artificial intelligence server.

[1654] The "generative artificial intelligence server" is a server that has the ability to analyze QR code data and convert it into a format specific to the service.

[1655] A "QR code" is a form of two-dimensional barcode and a means of storing information digitally.

[1656] A "service-specific format" is a specific data format required by each service, and is a data structure that satisfies the different requirements for each service.

[1657] "Converted data" refers to the data after the QR code data analyzed by the generation artificial intelligence server is converted into a format specific to the service.

[1658] "User authentication" is a procedure for verifying that a user is a legitimate user, and may involve the use of data signed with a private key.

[1659] "Electronic payment services" are payment services carried out using digital technology.

[1660] A "point card service" is a system in which users earn points based on purchases and service usage, and manage and use those points.

[1661] A "membership card service" is a card system issued by stores or service providers to prove membership and receive benefits and services.

[1662] A "coupon" is a ticket or digital code that entitles you to a discount or special offer on a specific product or service.

[1663] A "server response" is the result or information that a service's server returns in response to a request.

[1664] "Notification" refers to the act of sending the server's response result to the user's terminal to notify the user.

[1665] This invention is a system that allows users to seamlessly use QR codes from different services using a single dedicated QR code reader and management app. The system consists of a user terminal, a generation AI server, and a service server.

[1666] Overall system flow

[1667] 1. Scan the QR code

[1668] The user launches a dedicated QR code reader and management app, displays the QR code scanning screen, and uses the camera on their smartphone or tablet to scan the QR code and obtain the data. For example, the ZXing library can be used to parse the QR code.

[1669] 2. Data submission and analysis

[1670] The smartphone sends the acquired QR code data to the AI ​​server, along with user information (e.g., user ID and session ID).

[1671] The generative artificial intelligence server uses generative AI models such as the OpenAI GPT-4 API to analyze the QR code data and identify the relevant service (electronic payment service, point card service, membership card service, coupon use, etc.).

[1672] 3. Data Conversion

[1673] Based on the analysis results, the generation AI server converts the data into a format specific to a particular service. For example, if the QR code corresponds to an electronic payment service, it converts it into payment request data.

[1674] 4. Obtaining store information and user authentication

[1675] The artificial intelligence server then obtains store information from the converted data, including the store name, location, and transaction details.

[1676] The smartphone sends the data signed with the user's private key to the generation artificial intelligence server and requests authentication.

[1677] The generation artificial intelligence server verifies the user information and, if authentication is successful, proceeds to the next step.

[1678] 5. Sending data to the service server

[1679] The generating artificial intelligence server generates authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[1680] 6. Notification of Results

[1681] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[1682] The AI ​​server then sends the results to the smartphone, where the user can confirm the results. For example, if the payment is successful, the app will display "Payment Complete."

[1683] Specific examples

[1684] Consider a case where a user purchases an item at a convenience store and makes a payment.

[1685] 1. The user launches the dedicated QR code reader and management app at the cash register and scans the QR code.

[1686] 2. The QR code data is sent from the smartphone to the generation artificial intelligence server.

[1687] 3. The generating artificial intelligence server analyzes the QR code data and identifies that it corresponds to an electronic payment service.

[1688] 4. The data is converted into payment request data.

[1689] 5. The artificial intelligence server generated based on the signed data sent from the smartphone performs user authentication, and the authentication is successful.

[1690] 6. The payment request data is sent to the appropriate electronic payment service server.

[1691] 7. The service server processes the payment, and if successful, notifies the smartphone of the result, and the user receives a "payment completed" notification.

[1692] Example prompts to input to the generative AI model

[1693] "Analyze the QR code data scanned by the user using a dedicated app, identify which service it corresponds to, and convert the data into the format of the corresponding service.

[1694] QR code data: {QR_CODE_DATA}

[1695] User information: {USER_INFO}"

[1696] In this way, the present invention realizes a system that allows users to seamlessly use a variety of services with a single application by utilizing user devices such as smartphones and tablets and linking them with a generation artificial intelligence server.

[1697] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1698] Step 1:

[1699] The user launches the dedicated QR code reader and management app and displays the QR code scanning screen. The user's device uses the smartphone camera to scan the QR code and acquires the data. The acquired QR code data is temporarily stored on the device.

[1700] Input: QR code image

[1701] Output: QR code data

[1702] Specific operation: When the user presses the QR code scan button, the camera is activated and the QR code is read.

[1703] Step 2:

[1704] The user device sends the acquired QR code data to the AI ​​server, along with the user identification information (e.g., user ID and session ID).

[1705] Input: QR code data, user identification information

[1706] Output: Request data to the server

[1707] Specific operation: QR code data and user identification information are sent to the server as an HTTP request.

[1708] Step 3:

[1709] The generative AI server analyzes the received QR code data and identifies the applicable service. Using a generative AI model (e.g., OpenAI GPT-4), it analyzes the QR code data and identifies applicable services (e.g., electronic payment, point cards, membership cards, coupons, etc.).

[1710] Input: QR code data

[1711] Output: Service identification information

[1712] Specific operation: QR code data along with a prompt sentence is input into the generative AI model, and service identification information is obtained as the model output.

[1713] Step 4:

[1714] The generating artificial intelligence server converts the QR code data into a format specific to the particular service, such as payment request data for an electronic payment service, depending on the identified service.

[1715] Input: Service identification information, QR code data

[1716] Output: Data in service-specific format

[1717] Specific operation: Based on the identification information, convert the QR code data into the appropriate service format.

[1718] Step 5:

[1719] The artificial intelligence server then obtains store information from the converted data, including the store name, address, and transaction details.

[1720] Input: Data in service-specific format

[1721] Output: Store information

[1722] Specific operation: Search and obtain corresponding store information from the database.

[1723] Step 6:

[1724] To perform user authentication, the user terminal sends data signed with the private key to the generating artificial intelligence server.

[1725] Input: Data signed with the user's private key

[1726] Output: Authentication request data

[1727] Specific operation: The signature is sent to the server using the user's private key.

[1728] Step 7:

[1729] The generation artificial intelligence server verifies the sent signature and performs user authentication. If authentication is successful, proceed to the next step.

[1730] Input: Authentication request data

[1731] Output: Authentication result

[1732] Specific behavior: Decrypts the signature and verifies the user's authenticity.

[1733] Step 8:

[1734] The generating artificial intelligence server generates the authenticated data and sends it to the server of the corresponding service, for example, in the case of an electronic payment service, it sends payment request data.

[1735] Input: Authentication result, data in service-specific format

[1736] Output: Request data to the service server

[1737] Specific operation: Request data is generated based on the authentication result and sent as an HTTP request to the server of the corresponding service.

[1738] Step 9:

[1739] The service's server processes the request and returns the result (success, failure, error message, etc.) to the generating artificial intelligence server.

[1740] Input: Request data to the service server

[1741] Output: Service server response

[1742] Specific behavior: Processes the request, generates a result, and sends it back.

[1743] Step 10:

[1744] The generating artificial intelligence server notifies the user's device of the results, and the user can confirm the results through the display within the app.

[1745] Input: Service server response

[1746] Output: Notification to user device

[1747] Specific operation: The response result is sent to the user's device and displayed within the app.

[1748] 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.

[1749] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[1750] Overall system flow

[1751] The system operates in the following steps.

[1752] 1. Scan the QR code

[1753] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[1754] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[1755] 2. Acquiring Emotion Data

[1756] When a user scans a QR code, the device's built-in emotion engine analyzes the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[1757] 3. Data submission and analysis

[1758] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[1759] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[1760] 4. Data Conversion

[1761] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[1762] 5. Obtaining store information and user authentication

[1763] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[1764] The device requests authentication using the user's pre-configured information, using data signed with the private key.

[1765] The server verifies the user information and if authentication is successful, proceeds to the next step.

[1766] 6. Sending data to the server

[1767] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[1768] 7. Notification of Results

[1769] The service's server analyzes the received data and takes appropriate action (e.g., approves or denies payment).

[1770] The service's server generates the processing result and sends it to the server.

[1771] The server transmits the processing result received from the service server to the terminal.

[1772] The user can check the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[1773] 8. Feedback based on emotional data

[1774] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[1775] The server improves the user experience by notifying the device of appropriate messages and service suggestions based on the user's emotions.

[1776] Specific examples

[1777] As an example, consider a case where a user purchases an item at a retail store and pays using a QR code.

[1778] 1. Scan the QR code

[1779] The user launches a dedicated QR code reader and management app at the cash register and scans the QR code.

[1780] The device acquires the QR code data, and the emotion engine analyzes the user's facial expressions to acquire emotional data.

[1781] 2. Data submission and analysis

[1782] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[1783] The server analyzes the content of the QR code and identifies it as compatible with the payment service.

[1784] 3. Data Conversion and Authentication

[1785] The server converts the QR code data into payment request data and obtains store information.

[1786] The device sends data signed with the user's private key, and the server performs authentication.

[1787] 4. Payment Processing

[1788] The server sends the authenticated data to the payment service's server, which processes the payment.

[1789] The service server notifies the server of the payment result, which is then transmitted to the terminal.

[1790] 5. Notification of results and emotional feedback

[1791] The user confirms the payment in the app.

[1792] At the same time, based on the emotional data analyzed by the emotion engine, the server displays an appropriate message to the user (e.g., thank you for your satisfaction) or information about special offers for the next use.

[1793] This not only allows users to seamlessly use different services within a single app, but also provides services tailored to their emotions, resulting in a more comfortable user experience.

[1794] The processing flow will be explained below.

[1795] Step 1:

[1796] The user launches the dedicated QR code reader and management app, displays the QR code scanning screen, and prepares to scan the QR code by operating the app.

[1797] Step 2:

[1798] The device scans the QR code with its camera and reads the data, which includes the service's identification information and transaction information.

[1799] Step 3:

[1800] The device activates its built-in emotion engine to analyze the user's facial expressions and tone of voice to obtain emotion data, including the user's emotional state, such as joy, surprise, or anger.

[1801] Step 4:

[1802] The device sends the acquired QR code data and emotion data to the AI ​​server. This data also includes user information (e.g., user ID and session ID).

[1803] Step 5:

[1804] The server's generated artificial intelligence analyzes the received QR code data and automatically identifies the corresponding service (e.g., payment service, membership service, etc.).

[1805] Step 6:

[1806] Based on the identification results, the server's artificial intelligence will convert the QR code data into a service-specific format, such as payment request data for a payment service.

[1807] Step 7:

[1808] The server retrieves store information from the database based on the converted data, including store name, address, transaction details, etc.

[1809] Step 8:

[1810] The device requests authentication using pre-configured information from the user, e.g., sending data signed with the user's private key.

[1811] Step 9:

[1812] The server performs authentication based on the received user information. If authentication is successful, proceed to the next step.

[1813] Step 10:

[1814] The server generates and sends the authenticated data to the service's server, e.g., payment request data to the payment service's server.

[1815] Step 11:

[1816] The service's server analyzes the received data and takes appropriate action, for example approving or denying the payment.

[1817] Step 12:

[1818] The service's server generates a processing result (success, failure, error message, etc.) and sends it to the server.

[1819] Step 13:

[1820] The server transmits the processing result received from the service server to the terminal.

[1821] Step 14:

[1822] The terminal notifies the user, who then checks the results through a dedicated QR code reader and management app. For example, if the payment is successful, the app will display "Payment Completed."

[1823] Step 15:

[1824] The server analyzes the emotion data received from the emotion engine and generates feedback to optimize service provision. For example, if a user is feeling stressed, it will display a priority support message.

[1825] Step 16:

[1826] The server then sends appropriate messages and service suggestions to the device based on the user's emotions, improving the user experience. For example, if the user is surprised, the server will display a message such as "Thank you for the surprise" to provide services that reflect the user's emotions.

[1827] Example 2

[1828] 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."

[1829] Conventional QR code readers and management apps have difficulty seamlessly supporting multiple different services, forcing users to switch between apps for each service, resulting in a lack of convenience. Furthermore, services are provided without consideration of user feelings, which can result in a poor user experience. Therefore, there is a need for a system that can manage multiple services in one place and provide optimal services that respond to user feelings.

[1830] 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.

[1831] In this invention, the server includes means for the user terminal to read QR codes corresponding to different services and transmit the QR code data to the generation artificial intelligence server, means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service, means for acquiring service information based on the converted data and performing user authentication, means for acquiring user emotion data using a built-in emotion engine, means for transmitting the acquired emotion data to the generation artificial intelligence server, means for generating feedback based on the emotion data and notifying the user terminal, means for transmitting the authenticated data to the service server, and means for notifying the user terminal of a response from the service server. This not only allows the user to seamlessly use different services with a single app, but also enables the provision of optimal services according to emotions.

[1832] "User terminal" means an electronic device operated by a user that has the functions of reading QR codes, acquiring emotion data, and sending and receiving data.

[1833] The "generative artificial intelligence server" is a server system that analyzes the received QR code data and emotion data, identifies appropriate services, converts data, and supports the provision of optimal services.

[1834] A "QR code" is a two-dimensional barcode that encodes specific information and allows the data to be decoded using a reading device or application.

[1835] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and tone of voice to detect emotional states such as joy, surprise, or anger.

[1836] A "service server" is a server used to provide specific services (e.g., payment services, membership services), which receives data from user terminals and the generation artificial intelligence server and performs corresponding processing.

[1837] "Feedback" refers to additional information or messages provided to users after a service is provided based on their reactions and status, and is used to inform and improve the user experience.

[1838] A "private key" is key information used for digital signatures and data encryption, and is held individually by a user for access control and authentication purposes.

[1839] A "signature" is a digital signature generated using a private key to verify the authenticity and origin of data.

[1840] A "digital signature" is a signature generated for electronic data, and is used to prevent data tampering and to certify the sender.

[1841] This invention is a system that allows users to seamlessly use QR codes for different services using a single dedicated QR code reader and management app, and optimizes service provision by recognizing user emotions. This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[1842] First, the user launches a dedicated QR code reader and management app on their device, such as a smartphone or tablet. This app functions as a centralized management system for various services (e.g., payment, membership services, etc.). When the user performs an operation to scan the QR code, the QR code is read using the device's camera and the data is temporarily saved. This data includes service identification information and transaction information.

[1843] Furthermore, the user's device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice when scanning the QR code to obtain emotional data. This emotional data is temporarily stored within the app. Typical examples of emotion engines used here include Microsoft Azure's Face API and Emotion API.

[1844] The acquired QR code data and emotion data are sent from the device to the generation AI server. This transmission is secured by using protocols such as HTTPS to ensure data security. The generation AI server analyzes the QR code data and identifies which service it corresponds to. The generation AI model used here includes, for example, a QR code analysis algorithm and data mining technology.

[1845] Based on the identified service, the server converts the QR code data into a service-specific format (e.g., payment request data). The server then retrieves store information from a database, including store name, location, transaction details, etc.

[1846] To authenticate the user, the device generates a digital signature using the user's private key and sends it to the server, which then verifies the signature using public key cryptography and proceeds to the next step if authentication is successful.

[1847] The authenticated data is then sent to the service server. For example, payment request data is sent to the payment service server, which processes the payment by approving or rejecting it. The service server then sends the processing result back to the AI ​​server, which notifies the device. The user can check the results, such as whether the payment was successful, through a dedicated QR code reader and management app.

[1848] Finally, feedback based on emotional data is important. The server analyzes the emotional data and optimizes the service it provides, for example, by displaying priority support messages or special offers if the user is feeling stressed. This series of feedback is extremely useful for improving the user experience.

[1849] As a concrete example, in a scenario where a user is purchasing a product at a retail store, the following prompt sentence is input into the generative AI model:

[1850] "The user launches the dedicated QR code reader and management app at the cash register and scans the QR code attached to the product. The device acquires the QR code data, and the emotion engine analyzes the user's facial expression to acquire emotion data."

[1851] This not only allows users to seamlessly use different services within a single app, but also enables optimal service delivery based on their emotions, resulting in a pleasant user experience.

[1852] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1853] Step 1:

[1854] The user launches a dedicated QR code reader and management app.

[1855] Specifically, the user taps the dedicated app icon on the home screen of their smartphone or tablet to launch the app.

[1856] Input: A user taps the app icon.

[1857] Output: The app will launch and show the QR code scanning screen.

[1858] Step 2:

[1859] The user scans the QR code.

[1860] The user taps the "Scan" button in the app and the device uses the camera to scan the QR code.

[1861] Input: QR code.

[1862] Output: The scanned QR code data is retrieved.

[1863] Step 3:

[1864] The device acquires emotion data.

[1865] The device's built-in camera captures the user's face, and the emotion engine analyzes facial expressions to generate emotion data. The device also captures the user's voice with a microphone and analyzes the tone of their voice.

[1866] Input: The user's facial expression and tone of voice.

[1867] Output: Emotion data representing the user's emotional state.

[1868] Step 4:

[1869] The device sends the QR code data and emotion data to the generation artificial intelligence server.

[1870] The device sends the acquired QR code data, emotion data, and user information to the server using the HTTPS protocol.

[1871] Input: QR code data, emotion data, user information.

[1872] Output: The QR code data, emotion data, and user information are sent to the server.

[1873] Step 5:

[1874] The server parses the received data.

[1875] The server analyzes the received QR code data using the generated AI model and identifies the corresponding service.

[1876] Input: Received QR code data.

[1877] Output: Information identifying which service the QR code relates to.

[1878] Step 6:

[1879] The server converts the data into a service-specific format.

[1880] The server converts the QR code data into a service-specific format (e.g., payment request data) based on the identified service.

[1881] Input: Service identification information, QR code data.

[1882] Output: Data converted into a service-specific format.

[1883] Step 7:

[1884] The server obtains store information and performs user authentication.

[1885] The server retrieves store information from the database based on the converted data and verifies the data signed with the user's private key to authenticate the user.

[1886] Input: Transformed data, data signed with private key.

[1887] Output: Retrieved store information, user authentication results.

[1888] Step 8:

[1889] The authenticated data is sent to the service server.

[1890] The server sends the authenticated data to the target service server, which then analyzes the received data and performs the appropriate processing.

[1891] Input: Authenticated data.

[1892] Output: Data sent to the service server, response from the service server.

[1893] Step 9:

[1894] The results are notified to the user's terminal.

[1895] The server receives the response from the service server and notifies the user's device, and the user can check the result through the app.

[1896] Input: Response from the service server.

[1897] Output: The result is notified to the user's terminal and the user can check the result.

[1898] Step 10:

[1899] Provide feedback based on emotional data.

[1900] The server analyzes the emotional data, generates messages and suggestions tailored to the user, and notifies the user's device.

[1901] Input: User emotion data.

[1902] Output: Customized feedback messages and suggestions.

[1903] (Application example 2)

[1904] 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."

[1905] Conventional QR code-based service provision systems require users to use multiple QR code reader apps when using different services, resulting in a lack of convenience. Furthermore, services are provided uniformly without considering the user's emotional state, resulting in a suboptimal user experience. It is necessary to solve these issues and build a system that provides consistent, emotionally appropriate services to users.

[1906] 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.

[1907] In this invention, the server includes: means for a user terminal to read QR codes corresponding to different services and transmit the QR code data to a generation artificial intelligence server; means for the generation artificial intelligence server to analyze the QR code data and convert it into a format specific to the service; means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to the service server; means for notifying the user terminal of a response from the service server; means including an emotion engine for obtaining user emotion data; and means for generating feedback to optimize the service based on the emotion data. This allows a user to seamlessly use multiple QR codes with a single application and also enables the provision of optimal services according to emotions.

[1908] A "user terminal" is a portable information terminal that can read QR codes and communicate with a server via the Internet.

[1909] A "QR code" is a type of two-dimensional barcode that uses a rectangular grid pattern to represent digital information.

[1910] The "generative artificial intelligence server" is a computer server that analyzes the received data, identifies the corresponding service, and provides the user with the most suitable information.

[1911] The "emotion engine" is software that analyzes the user's facial expressions and voice data obtained using a camera and microphone to determine the user's emotional state.

[1912] A "service-specific format" is a data format required by a particular service, such as a payment request or membership information.

[1913] "User authentication" is the process of verifying a user's identity and verifying their identity before providing services.

[1914] "Data signed with a private key" refers to data that has a digital signature encrypted with the user's private key, which verifies the authenticity of the data.

[1915] A "means for generating feedback" is a system or software for generating optimized services or messages based on analysis results and past data.

[1916] As an embodiment of the present invention, a specific method thereof will be described below.

[1917] This system consists of a user terminal, a generative AI server, an emotion engine, and a service server.

[1918] Hardware and Software Use

[1919] First, a smartphone is used as the user device, which is equipped with a camera and microphone and is used to scan the QR code and to detect the user's facial expression and tone of voice.

[1920] The software used is an "emotion engine" for emotion analysis. For example, "Microsoft's Azure Face API" or "Google Cloud Vision API" are suitable. The generative AI server also includes a "generative artificial intelligence model" that analyzes data and identifies services.

[1921] Data processing and calculation

[1922] When a user scans a QR code, the smartphone camera captures the QR code data, while the emotion engine simultaneously analyzes the user's facial expressions and tone of voice to collect emotional data.

[1923] This data is sent from the smartphone to the generation AI server. The generation AI server analyzes the QR code data and identifies which service it corresponds to. At this time, the QR code data is converted into a format specific to each service, such as payment request data or membership service data. Authentication is also performed using data signed with the user's private key.

[1924] The server retrieves the necessary store information from the database based on the converted data. Based on this information, the authenticated data is sent to the service server, where the service is processed. The response from the service server is notified to the user's device via the generation AI server.

[1925] At the same time, emotional data is used to generate feedback to optimize service delivery: for example, if a user is feeling stressed, priority support messages or special offers for the next visit will be displayed.

[1926] Specific examples

[1927] As a concrete example, consider the case where a user is shopping at a convenience store and makes an electronic payment using a QR code. The user launches a dedicated app and scans the QR code at the cash register. The smartphone camera reads the QR code, and at the same time, the emotion engine analyzes the facial expression. The QR code data and emotion data are sent to the generation artificial intelligence server, where they are analyzed and converted.

[1928] The server sends the payment request data to the payment service server and returns the result to the user's device. Once the payment is completed, messages such as "Thank you for your use" or "You will receive a special offer for your next use" are displayed based on the emotion data.

[1929] Prompt Sentence Examples

[1930] Analyze QR code data and user sentiment data to provide optimal service feedback.

[1931] QR code data: {qr_data}

[1932] Emotion data: {emotion_data}

[1933] This allows users to seamlessly use multiple QR codes in a single application and receive optimal services tailored to their emotions.

[1934] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1935] Step 1:

[1936] The user scans the QR code with their smartphone.

[1937] Input: QR code image captured by camera.

[1938] Processing: The QR code reader app on your smartphone analyzes the image and extracts the QR code data.

[1939] Output: QR code data.

[1940] Step 2:

[1941] When a user scans a QR code, the smartphone's built-in emotion engine analyzes the user's facial expressions and tone of voice.

[1942] Input: User's camera footage (facial expressions) and microphone audio (tone of voice).

[1943] Processing: The emotion engine performs facial expression recognition and voice analysis to generate emotion data.

[1944] Output: Emotion data.

[1945] Step 3:

[1946] The smartphone sends the acquired QR code data and emotion data to the generation AI server.

[1947] Input: QR code data, emotion data.

[1948] Processing: Data is sent to the generating AI server using a secure communication protocol (e.g., HTTPS).

[1949] Output: Data sent to the generating AI server.

[1950] Step 4:

[1951] The generation AI server analyzes the received QR code data and identifies the corresponding service.

[1952] Input: QR code data.

[1953] Processing: A generative AI model parses the QR code data and identifies services (e.g., payment, membership services).

[1954] Output: Service identity information.

[1955] Step 5:

[1956] Based on the identification results, the generation AI server converts the QR code data into a format specific to the service.

[1957] Input: Service identification information, QR code data.

[1958] Processing: Reconstruct the QR code data into a data format specific to each service (e.g., payment request data, membership information).

[1959] Output: The transformed service-specific data.

[1960] Step 6:

[1961] The server retrieves the necessary store and user information from the database based on the converted data.

[1962] Input: Transformed service-specific data.

[1963] Processing: Executes a database query to retrieve relevant store and user information.

[1964] Output: Store information, user information.

[1965] Step 7:

[1966] The generating AI server authenticates the user using data signed with the user's private key.

[1967] Input: User information, data signed with private key.

[1968] Processing: Verify the private key signature and perform user authentication.

[1969] Output: Authentication result.

[1970] Step 8:

[1971] The generation AI server sends the authenticated data to the service server, which processes the service.

[1972] Input: Authenticated data, service specific data.

[1973] Processing: Sends the authentication data and service-specific data as a request to the service's server to perform the service's processing (e.g., payment processing).

[1974] Output: Processing results from the service's server.

[1975] Step 9:

[1976] The response result from the service server is notified to the user device via the generated AI server.

[1977] Input: Processing result from the service's server.

[1978] Processing: The generation AI server receives the response result and notifies the user terminal.

[1979] Output: The response result sent to the user's device.

[1980] Step 10:

[1981] The generation AI server generates an appropriate feedback message based on the emotional data and notifies the user device.

[1982] Input: Emotion data, response results.

[1983] Processing: Emotional data is used to generate feedback messages (e.g., special offers for next use, support messages) to optimize service delivery.

[1984] Output: Feedback message.

[1985] 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.

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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.

[1991] 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).

[1992] 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.

[1993] 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."

[1994] 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.

[1995] 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).

[1996] 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.

[1997] 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.

[1998] 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.

[1999] 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.

[2000] 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.

[2001] 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.

[2002] 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.

[2003] 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.

[2004] 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.

[2005] 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.

[2006] The following is further disclosed regarding the above embodiment.

[2007] (Claim 1)

[2008] A means for a user terminal to read QR codes corresponding to different services and transmit the data of the QR code to a generating artificial intelligence server;

[2009] A means for the generating artificial intelligence server to analyze the QR code data and convert it into a format specific to the service;

[2010] A means for obtaining service information based on the converted data and performing user authentication;

[2011] means for transmitting the authenticated data to a server of the service;

[2012] and means for notifying the user terminal of a response from the server of the service.

[2013] (Claim 2)

[2014] The system according to claim 1, wherein the generation artificial intelligence server acquires store information based on data analysis of the QR code and notifies the user terminal of the store information.

[2015] (Claim 3)

[2016] 2. The system of claim 1, wherein the generating artificial intelligence server includes means for verifying data signed with a private key during user authentication.

[2017] "Example 1"

[2018] (Claim 1)

[2019] A means for a user device to read QR codes corresponding to different services and transmit the data of the QR codes to a server;

[2020] A means for the server to analyze the QR code data and convert it into a service-specific format;

[2021] A means for obtaining service information based on the converted data and performing user authentication;

[2022] a means for transmitting authenticated data to a system external to the service;

[2023] a means for notifying a user terminal of a response from the external system;

[2024] A means for the user device to obtain data including service identification information and transaction information via a dedicated QR code reader and management app;

[2025] A means for the server to analyze the QR code data using the generated AI model and identify the corresponding service;

[2026] A system including:

[2027] (Claim 2)

[2028] The system according to claim 1, wherein the server acquires trading location information based on data analysis of the QR code and notifies the user terminal of the trading location information.

[2029] (Claim 3)

[2030] 2. The system of claim 1, wherein the server includes means for verifying data signed with the private key during user authentication.

[2031] "Application Example 1"

[2032] (Claim 1)

[2033] A means for a user terminal to read QR codes corresponding to different services and transmit the data of the QR code to a generating artificial intelligence server;

[2034] A means for the generating artificial intelligence server to analyze the QR code data and convert it into a format specific to the service;

[2035] A means for obtaining service information based on the converted data and performing user authentication;

[2036] means for transmitting the authenticated data to a server of the service;

[2037] The artificial intelligence server analyzes the contents of the QR code and comprehensively identifies and processes multiple electronic payment services, point card services, membership card services, coupon usage, etc.;

[2038] and means for notifying the user terminal of a response from the server of the service.

[2039] (Claim 2)

[2040] The system according to claim 1, wherein the generation artificial intelligence server acquires store information based on data analysis of the QR code and notifies the user terminal of the store information.

[2041] (Claim 3)

[2042] The system of claim 1 includes a means for the generating artificial intelligence server to verify data signed with a private key during user authentication, a means for processing appropriate services based on the analysis results, and a means for unifying different response formats for each service and notifying the user terminal.

[2043] "Example 2: Combining Emotion Engines"

[2044] (Claim 1)

[2045] A means for a user terminal to read QR codes corresponding to different services and transmit the data of the QR code to a generating artificial intelligence server;

[2046] A means for the generating artificial intelligence server to analyze the QR code data and convert it into a format specific to the service;

[2047] A means for obtaining service information based on the converted data and performing user authentication;

[2048] a means for obtaining user emotion data using a built-in emotion engine;

[2049] means for transmitting the acquired emotion data to a generating artificial intelligence server;

[2050] A means for generating feedback based on the emotion data and notifying the feedback to a user terminal;

[2051] means for transmitting the authenticated data to a server of the service;

[2052] a means for notifying a user terminal of a response from a server of the service;

[2053] A system including:

[2054] (Claim 2)

[2055] The system according to claim 1, wherein the generation artificial intelligence server obtains service information based on data analysis of the QR code and notifies the user terminal of the service information.

[2056] (Claim 3)

[2057] 2. The system of claim 1, wherein the generating artificial intelligence server includes means for verifying data signed with a private key during user authentication.

[2058] "Application example 2 when combining emotion engines"

[2059] (Claim 1)

[2060] A means for a user terminal to read QR codes corresponding to different services and transmit the data of the QR code to a generating artificial intelligence server;

[2061] A means for the generating artificial intelligence server to analyze the QR code data and convert it into a format specific to the service;

[2062] A means for obtaining service information based on the converted data and performing user authentication;

[2063] means for transmitting the authenticated data to a server of the service;

[2064] a means for notifying a user terminal of a response from a server of the service;

[2065] means including an emotion engine for obtaining emotion data of a user;

[2066] and means for generating feedback to optimize the service based on the emotion data.

[2067] (Claim 2)

[2068] The system according to claim 1, wherein the generation artificial intelligence server acquires store information based on data analysis of the QR code and notifies the user terminal of the store information.

[2069] (Claim 3)

[2070] 2. The system of claim 1, wherein the generating artificial intelligence server includes means for verifying data signed with a private key during user authentication. [Explanation of symbols]

[2071] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user terminal to read QR codes corresponding to different services and transmit the data of the QR code to a generating artificial intelligence server; A means for the generating artificial intelligence server to analyze the QR code data and convert it into a format specific to the service; A means for obtaining service information based on the converted data and performing user authentication; means for transmitting the authenticated data to a server of the service; and means for notifying the user terminal of a response from the server of the service.

2. 2. The system according to claim 1, wherein the generation artificial intelligence server acquires store information based on data analysis of the QR code and notifies the user terminal of the store information.

3. 2. The system according to claim 1, wherein the generating artificial intelligence server includes means for verifying data signed with a private key during user authentication.

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