system

Abstract

Provide a system. The method includes receiving transaction data acquired at a user device; means for storing said transaction data in a database; a generation module that analyzes the transaction data and generates value-added information; means for transmitting the value-added information to a user device; means for displaying the value-added information in the user device; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional receipts are often provided on paper, which poses challenges such as cumbersome storage and management and a significant environmental impact. Furthermore, the information obtainable from paper receipts is limited, making it difficult to provide added value to users. The main reasons for the lack of adoption of electronic receipts include a lack of convenience and a lack of user interest. The present invention aims to solve these challenges by promoting the widespread use of electronic receipts and providing a system that provides useful added-value information to users. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving transaction data acquired by a user device, a means for storing the transaction data in a database, a generation module for analyzing the transaction data and generating value-added information, a means for transmitting the value-added information to the user device, and a means for displaying the value-added information on the user device. Specifically, when a user conducts a transaction using an electronic payment method, the system automatically acquires and stores the transaction data, and analyzes the data to generate valuable information (e.g., recipe suggestions) based on the user's purchasing patterns and product information. This increases the convenience of electronic receipts, reduces paper usage, and enriches users' lives.

[0006] A "user device" is a device for making electronic payments, and includes smartphones, tablets, personal computers, etc.

[0007] "Transaction data" refers to payment-related data acquired by a user device, and includes the product name, price, transaction date and time, etc.

[0008] The "receiving means" refers to a communication module for transmitting transaction data acquired from a user device to a server and receiving the data.

[0009] "Database" means a data storage system for storing and managing transaction data.

[0010] "Means for storing" refers to the function for recording and storing received transaction data in a database.

[0011] "Analyzing" refers to the process of analyzing trading data using computer algorithms to extract specific patterns or information.

[0012] "Value-added information" is information that is useful to users and is obtained by analyzing transaction data, and includes, for example, recipe suggestions and promotion information.

[0013] "Generation Module" refers to a software or hardware component for generating value-added information based on transaction data.

[0014] "Means for transmitting" refers to a communication function for sending the generated value-added information to a user device.

[0015] The "means for displaying" refers to a display function for visually presenting the received value-added information to the user in the user device. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. Specific embodiments of this system and the program processing are described below in natural language.

[0038] System configuration

[0039] This system mainly consists of the following three components:

[0040] 1. User Device:

[0041] A device used by a user to make electronic payments, typically a smartphone or tablet, has an electronic payment application (e.g., an electronic money app) installed on it.

[0042] 2. Server:

[0043] This is a server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[0044] 3. Database:

[0045] A data storage for storing transaction data and generated value-added information.

[0046] Program processing flow

[0047] 1. Obtaining payment information

[0048] Device:

[0049] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment (e.g., the product purchased, the price, and the date and time of the transaction).

[0050] 2. Send receipt information

[0051] Device:

[0052] Once the payment is complete, the terminal encrypts the transaction data it receives and sends it to the server along with the user's unique account ID.

[0053] 3. Save your receipt

[0054] server:

[0055] The server checks the integrity of the received transaction data and stores it in a database, including the user ID, purchased item, price, transaction date and time, etc.

[0056] 4. Data analysis with generative AI

[0057] server:

[0058] The server passes the stored transaction data to a generation AI, which analyzes the data and generates value-added information (such as recipe suggestions) based on the user's purchasing patterns and product information.

[0059] 5. Providing value-added information

[0060] server:

[0061] The generated value-added information is associated with the user's account and transmitted to the user device.

[0062] 6. Display of Information

[0063] Device:

[0064] The user device displays the value-added information received from the server, and when the user opens the app, they can see the proposed value-added information along with the electronic receipt.

[0065] Specific examples

[0066] For example, suppose a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes).

[0067] 1. Obtaining payment information

[0068] Terminal: Once payment is completed, the transaction data "Items: Chicken, Onion, Potato, Price: 2,000 yen, Date and Time: 2023-10-01" is obtained.

[0069] 2. Send receipt information

[0070] Terminal: The transaction data is encrypted and sent to the server along with the user ID.

[0071] 3. Save your receipt

[0072] Server: Save "User ID: 12345, Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" in the database.

[0073] 4. Data analysis with generative AI

[0074] Server: The generative AI analyzes the transaction data and generates a curry recipe using "chicken, onion, and potato."

[0075] 5. Providing value-added information

[0076] Server: Sends the generated curry recipe information to the user device.

[0077] 6. Display of Information

[0078] Device: When the user opens the app, they can see the "Chicken Curry Recipe" along with "Purchased Items: Chicken, Onion, Potato, Price: 2,000 yen, Date: 2023-10-01."

[0079] This invention saves users the trouble of managing paper receipts and allows them to receive useful information based on transaction data. This improves the convenience of electronic receipts and is expected to encourage more users to use electronic receipts.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code (registered trademark) using a user device such as a smartphone or tablet, and the payment screen is displayed.

[0083] Step 2:

[0084] Terminal: The user device communicates with the store's payment system to obtain transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction.

[0085] Step 3:

[0086] Terminal: After the payment is completed, the obtained transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") is encrypted and sent to the server along with the user's unique account ID.

[0087] Step 4:

[0088] Server: The server checks the integrity of the transaction data it receives, verifies that the data is accurate, and stores the transaction data in the database after verifying that there is no fraud or tampering.

[0089] Step 5:

[0090] Server: When transaction data is saved in a database, it is recorded in association with information such as the user ID, purchased item, price, transaction date and time, etc. The database is secured to a certain extent.

[0091] Step 6:

[0092] Server: The generation AI starts working based on the stored transaction data. The generation AI analyzes the data and identifies the user's purchasing patterns and characteristics.

[0093] Step 7:

[0094] Server: The generation AI analyzes the transaction data and generates value-added information (e.g., recipes, promotional information). Specifically, it can suggest a "curry recipe" using "chicken, onions, and potatoes."

[0095] Step 8:

[0096] Server: Sends the generated value-added information to the user device, where it is converted into an appropriate format so that it can be easily understood by the user.

[0097] Step 9:

[0098] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[0099] Step 10:

[0100] User: The user opens the application and sees the latest transaction information (e-receipt) and suggested value-added information (e.g. curry recipe), which improves the user's life.

[0101] Through the above steps, the system of the present invention can acquire, store, and analyze electronic receipts, and provide added-value information in a single flow.

[0102] Example 1

[0103] 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."

[0104] With the spread of modern electronic payments, users are increasingly burdened with managing paper receipts. While systems exist that provide value-added information based on purchase data, there is a need for an automated, secure method for acquiring and providing this information that does not require user interaction. Therefore, a system is needed that integrates the encrypted transmission of purchase data, secure storage in a database, and the automatic generation and provision of value-added information using a generative AI model.

[0105] 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.

[0106] In this invention, the server includes means for receiving transaction data acquired by a user device, means for encrypting and transmitting the transaction data, means for storing the transaction data in a database, means for verifying the integrity of the stored transaction data, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information generated by the generation module to the user device, and means for displaying the value-added information on the user device. This eliminates the need for users to manage paper receipts, and further enables the system to automatically and safely acquire and store transaction data and provide useful value-added information based on it.

[0107] A "user device" is a device used by a user to make electronic payments, and typically includes a smartphone or tablet.

[0108] "Transaction data" is information generated when a user makes an electronic payment, and includes the name of the purchased product, its price, the date and time of the transaction, and the like.

[0109] "Encryption" is the process of converting transaction data using a specific algorithm to protect it from unauthorized access by third parties, ensuring that only those with the key can access the original data.

[0110] "Database" means data storage for the secure long-term preservation of transaction data and generated value-added information.

[0111] "Integrity verification" is the process of checking received transaction data for errors or unauthorized changes to ensure the authenticity of the data.

[0112] The term "generation module" refers to a program and its execution environment for analyzing transaction data and automatically generating value-added information useful to users.

[0113] "Added-value information" is information that is generated based on transaction data and that the user finds useful, and includes, for example, recipe information and product recommendation information.

[0114] A "generative AI model" is a machine learning model that has the ability to generate text data based on specific input data, and is used to generate recipe information and value-added information based on user transaction data.

[0115] "Transmission means" refers to the communication protocol and its execution environment used to safely and reliably send specific data to another device or server.

[0116] "Displaying means" refers to software and its interface for visually presenting information on the screen of a user device.

[0117] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. This system is mainly composed of a user device, a server, and a database.

[0118] System configuration

[0119] 1. User Device:

[0120] A device used by a user to make electronic payments. This device includes smartphones and tablets. An electronic payment application (e.g., an e-money app) is installed on the user device. When a user makes a purchase, payment is made using this application.

[0121] 2. Server:

[0122] The server receives transaction data and stores it in a database. It also analyzes the stored data and generates value-added information. A generative AI model is used for this analysis. The server checks the integrity of the data and ensures that the transaction data is accurate.

[0123] 3. Database:

[0124] The database is a data storage for saving transaction data and generated value-added information, allowing users' transaction history and generated value-added information to be maintained for a long period of time.

[0125] Program processing

[0126] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. This data includes the name of the purchased item, its price, the date and time of the transaction, etc. This transaction data is stored in a temporary file. Once the transaction is complete, the terminal encrypts this data and sends it to the server along with the user's unique account ID. The transmitted data is protected using encryption algorithms such as AES encryption and is sent via the HTTPS protocol.

[0127] The server verifies the integrity of the received transaction data. Specifically, it performs a data integrity check (e.g., checksum verification) to ensure that no unauthorized changes have been made. Once the integrity of the data is confirmed, information such as the user ID, purchased item, price, and transaction date and time is saved in a database. The saved transaction data is passed to a generative AI model. This generative AI model analyzes the transaction data and generates value-added information useful to the user (e.g., recipe information and recommended products).

[0128] The generated value-added information is converted back to JSON format, linked to the user ID, and sent to the user device via HTTPS. The user device displays the received value-added information within the application. When the user opens the app, they can see the proposed value-added information along with the products they purchased.

[0129] Specific examples

[0130] For example, if a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes), an example of transaction data would be as follows:

[0131] "Item: Chicken, onion, potato, Price: 2000 yen, Date: 2023-10-01"

[0132] This data is sent to a server and analyzed by a generative AI model, which then suggests a curry recipe using "chicken, onions, and potatoes." An example prompt is as follows:

[0133] Based on the purchase data of "chicken, onion, potato," the user is prompted to "suggest a recipe using these ingredients."

[0134] In this way, users can easily obtain useful information based on their purchase history, eliminating the need to manage paper receipts and allowing them to receive useful information based on transaction data.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] System configuration

[0137] This system mainly consists of the following three components:

[0138] 1. User device: A device used by a user to make electronic payments, typically a smartphone or tablet, on which an electronic payment application (e.g., an e-money app) is installed.

[0139] 2. Server: This server receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[0140] 3. Database: Data storage for storing transaction data and generated value-added information.

[0141] Program processing flow

[0142] Step 1: Get your payment information

[0143] Terminal: When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. Specifically, the application records transaction data such as the name of the purchased item, its price, and the transaction date and time. This data is saved in a temporary storage folder. The input is the item purchased by the user and its details, and the output is temporary storage of transaction data.

[0144] Step 2: Send receipt information

[0145] Terminal: Once the payment is completed, the terminal encrypts and transmits the acquired transaction data. Specifically, it encrypts the transaction data using the AES encryption algorithm and sends the encrypted data and the user's account ID to the server. The transmission uses the HTTPS protocol. The input is the temporarily stored transaction data, and the output is the encrypted data and the account ID sent to the server.

[0146] Step 3: Save your receipt

[0147] Server: The server checks the integrity of the received transaction data. Specifically, it verifies the integrity of the data using a checksum. Once the integrity is confirmed, the data is stored in a database. The stored data includes the user ID, purchased item, price, transaction date and time, etc. The input is the encrypted transaction data and account ID, and the output is the data stored in the database after integrity is confirmed.

[0148] Step 4: Data analysis with generative AI

[0149] Server: The server passes the stored transaction data to the generative AI model and analyzes the data. Specifically, the transaction data is converted into JSON format and input into the generative AI model. The generative AI model generates value-added information (e.g., recipe suggestions) based on the user's purchasing patterns and product information. The input is the transaction data stored in the database, and the output is the generated value-added information.

[0150] Step 5: Provide added value information

[0151] Server: Associates the generated value-added information with the user's account and sends it to the user device. Specifically, the generated information is converted back to JSON format, linked to the user ID, and sent to the user device via the HTTPS protocol. The input is the generated value-added information, and the output is the transmission to the user device.

[0152] Step 6: Viewing information

[0153] Terminal: The user device displays the value-added information received from the server. Specifically, the electronic payment app parses the received data and displays it on the user interface. When the user opens the app, they can see the proposed value-added information along with the electronic receipt. The input is the value-added information received from the server, and the output is the information displayed on the user interface.

[0154] (Application example 1)

[0155] 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."

[0156] With the spread of electronic payments, there is a need for efficient management of transaction data acquired daily by users and for utilizing that data to provide users with useful information. However, current systems are limited to simple recording of transaction data and do not adequately provide value-added information based on users' spending patterns. Furthermore, there is a need for systems that not only manage electronic receipts but also manage income and expenditures and provide relevant campaign information. The present invention aims to solve these problems and provide a system that is more convenient and useful for users.

[0157] 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.

[0158] In this invention, the server includes means for receiving transaction data acquired by a user device, means for storing the transaction data in a database, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information to the user device, means for displaying the value-added information on the user device, and means for providing campaign information based on the user's spending patterns and purchase details as the value-added information. This allows the user to not only manage transaction data but also efficiently obtain useful information based on their daily spending patterns.

[0159] definition statement

[0160] A "user device" is a communication device used by a user to make electronic payments and whose primary use includes capturing and storing transaction data and displaying value-added information.

[0161] "Transaction data" refers to information generated when an electronic payment is made, and includes primarily product name, price, purchase date and time, etc.

[0162] "Database" means a digital storage system for storing transaction data and generated value-added information.

[0163] A "generation module" is a software component that includes artificial intelligence for analyzing transaction data and generating value-added information.

[0164] "Value-added information" is information generated based on transaction data, and includes useful suggestions, advice, and special benefit information for the user.

[0165] "Campaign information" is promotional information such as benefits and discounts that are provided to users under certain conditions, and is intended to support users' purchasing activities.

[0166] A "generative AI model" is a machine learning model used to analyze transaction data and generate value-added information based on users' spending patterns and purchasing tendencies.

[0167] "Spending patterns" are data that indicate specific tendencies or behaviors based on a user's past purchasing history.

[0168] "Special offer information" refers to information including discounts, points, or other benefits offered to users to encourage them to make purchases.

[0169] MODE FOR CARRYING OUT THE INVENTION

[0170] The system of the present invention collects, stores, and analyzes electronic payment data of users, and provides value-added information based on the collected data. A specific embodiment of this system will be described below.

[0171] System configuration

[0172] This system mainly consists of the following three components:

[0173] 1. User Device

[0174] A communication device used by a user to make electronic payments, typically a smartphone, has an electronic payment application installed on it.

[0175] 2. Server

[0176] A server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[0177] 3. Database

[0178] A digital storage system for storing transaction data and generated value-added information.

[0179] Hardware and software used

[0180] Examples of specific hardware and software used in this system include:

[0181] Hardware: Smartphone

[0182] Software: Application frameworks (e.g., React Native), servers (e.g., AWS®, Firebase), databases (e.g., MongoDB, Firebase Realtime Database), generative AI models (e.g., OpenAI®'s GPT-4®)

[0183] Explanation of program processing

[0184] 1. Obtaining payment information

[0185] The electronic payment application on the user device automatically acquires transaction data (e.g., product name, price, purchase date and time) when payment is completed.

[0186] 2. Send receipt information

[0187] The acquired transaction data is encrypted and sent to a dedicated cloud server along with the user's unique ID.

[0188] 3. Save your receipt

[0189] The server verifies the integrity of the received transaction data and stores it in a database.

[0190] 4. Data analysis with generative AI

[0191] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information (e.g., recipe information, campaign information, and bonus information).

[0192] 5. Providing value-added information

[0193] The generated value-added information is associated with the user's account and transmitted to the user device.

[0194] 6. Display of Information

[0195] The user device displays the value-added information received from the server, and the user can open the app to view the proposed value-added information along with the electronic receipt.

[0196] Specific examples

[0197] For example, if a user uses an electronic payment app at a convenience store to purchase 1,000 yen worth of drinks and snacks, the following process occurs:

[0198] 1. Obtaining payment information

[0199] When payment is completed, the user device acquires the transaction data "Product: Beverage, Snack, Price: 1,000 yen, Date and Time: 2023-10-01."

[0200] 2. Send receipt information

[0201] This transaction data is encrypted and sent to the cloud server along with the user ID.

[0202] 3. Save your receipt

[0203] The server saves the data in the database as "User ID: 67890, Product: Drink, Snack, Price: 1,000 yen, Date and Time: 2023-10-01".

[0204] 4. Data analysis with generative AI

[0205] The generative AI model analyzes transaction data and generates campaign information for users, such as "a snack set perfect for watching a movie on the weekend."

[0206] 5. Providing value-added information

[0207] Campaign information "We recommend this snack for watching movies on the weekend! Enjoy it with a popular movie title" will be sent to users' smartphones.

[0208] 6. Display of Information

[0209] When a user opens the app, "Purchased items: drinks, snacks, price: 1,000 yen, date and time: 2023-10-01" along with "Recommended movie viewing information" will be displayed.

[0210] Prompt Sentence Examples

[0211] An example of a prompt is:

[0212] The user has purchased drinks and snacks. Please provide recommendations for how to enjoy the weekend.

[0213] In this way, the system according to the present invention effectively utilizes the user's expenditure data and provides useful information for daily life.

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

[0215] Program processing

[0216] Explain the process step by step

[0217] Step 1:

[0218] A user makes a payment using an electronic payment app. At this moment, the user device automatically obtains transaction data (e.g., product name, price, purchase date and time). Specifically, the app detects the payment completion event and calls an API to obtain payment information.

[0219] (Input): Electronic payment completion information

[0220] (Output): Transaction data related to the payment (e.g. product name, price, purchase date and time)

[0221] Step 2:

[0222] The acquired transaction data is encrypted and sent to a cloud server along with the user's unique ID. The user device then uses an encryption algorithm to secure the data before sending it to the server via the internet.

[0223] (Input): Payment transaction data, user ID

[0224] (Output): Encrypted transaction data, user ID

[0225] Step 3:

[0226] The server verifies the integrity of the received transaction data and stores it in the database. The server first decrypts the data, then performs an integrity check, and then calls an API to store it in the database.

[0227] (Input): Encrypted transaction data, user ID

[0228] (Output): Transaction data stored in the database

[0229] Step 4:

[0230] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information. The server first retrieves the transaction data from the database, then generates appropriate prompts for the generative AI model and performs the analysis.

[0231] (Input): Transaction data in the database

[0232] (Output): Added-value information generated by the generative AI model (e.g., recipe information, campaign information, special offer information)

[0233] Step 5:

[0234] The generated value-added information is associated with the user's account and transmitted to the user device. The server then links the value-added information to the user ID and transmits the data to the user device using a notification function.

[0235] (Input): Generated value-added information, user ID

[0236] (Output): Value-added information sent to the user device

[0237] Step 6:

[0238] The user device displays the value-added information received from the server. When the user opens the electronic payment app, they can view the value-added information along with the latest transaction data. Specifically, the app processes the received data and displays it on the user interface.

[0239] (Input): Value-added information received from the server

[0240] (Output): Value-added information displayed within the app (e.g., recipes, campaign information)

[0241] Through the above steps, a system is realized that allows users to effectively use electronic payment data and obtain useful added-value information.

[0242] 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.

[0243] The present invention relates to a system that uses transaction data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information, and further combines this with an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[0244] System configuration

[0245] The system includes the following major components:

[0246] 1. User Device:

[0247] A device such as a smartphone or tablet on which users make transactions.

[0248] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[0249] 2. Server:

[0250] It has the function of receiving transaction data and storing it in a database.

[0251] It includes analytical functions such as emotion engines and generation modules.

[0252] 3. Database:

[0253] Data storage for storing transaction data and generated value-added information.

[0254] 4. Emotion Engine:

[0255] It has the ability to analyze facial expression data and voice data sent from the user device and recognize the user's emotions.

[0256] 5. Generation module:

[0257] Transaction data and perceived user sentiment are analyzed and value-added information is generated based thereon.

[0258] Program processing flow

[0259] 1. Obtaining payment information

[0260] Device:

[0261] When a user makes a payment using an electronic payment app, the terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also acquires the user's emotional data (facial expressions and voice).

[0262] 2. Send receipt information

[0263] Device:

[0264] Transaction data and emotion data are encrypted and sent to the server along with the user's unique account ID.

[0265] 3. Save your receipt

[0266] server:

[0267] The consistency of the transaction data and sentiment data is checked and stored in the database.

[0268] 4. Emotion Data Analysis

[0269] server:

[0270] The emotion engine analyzes the user's emotional data and recognizes their current emotional state, for example, by identifying emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[0271] 5. Comprehensive data analysis

[0272] server:

[0273] The generation module analyzes the transaction data and the recognized emotion data, thereby generating the optimal value-added information that matches the user's emotions.

[0274] 6. Providing value-added information

[0275] server:

[0276] The generated value-added information (e.g., recipes, promotional information) is transmitted to the user device.

[0277] 7. Display of Information

[0278] Device:

[0279] The user device displays the received value-added information within the application.

[0280] Specific examples

[0281] For example, consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. When paying, the emotion engine uses a camera to detect the user's facial expressions and analyzes their voice to determine that the user is busy but happy.

[0282] 1. Obtaining payment information:

[0283] Terminal: Obtain transaction data "Product: Chicken, onion, potato, Price: 2000 yen, Date and time: 2023-10-01" and emotion data.

[0284] 2. Sending receipt information:

[0285] Terminal: Transaction data and emotional data are encrypted and sent to the server.

[0286] 3. Save your receipt:

[0287] Server: Stored in the database.

[0288] 4. Emotional Data Analysis:

[0289] Server: Emotional state analyzed as "busy but happy."

[0290] 5. Comprehensive data analysis:

[0291] Server: Based on the user's emotional state, the server suggests a simple and healthy recipe for "Easy Chicken Curry."

[0292] 6. Providing Value-Added Information:

[0293] Server: Sends suggested recipe information to the user device.

[0294] 7. Displaying Information:

[0295] Device: The user opens the app and sees the recipe for "Easy Chicken Curry" along with their digital receipt.

[0296] The present invention allows users to obtain more personalized added-value information, and improves the quality of their daily lives by suggesting optimal recipes that match their mood on that day, for example.

[0297] The processing flow will be explained below.

[0298] Step 1:

[0299] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code or other data using a user device such as a smartphone or tablet, and the payment screen is displayed. At this time, the camera and microphone are activated to recognize the user's emotions.

[0300] Step 2:

[0301] Terminal: The user's device communicates with the store's payment system and acquires transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction. At the same time, the camera and microphone are used to acquire facial expression and voice data from the user.

[0302] Step 3:

[0303] Terminal: Once the payment is completed, the terminal encrypts the transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") and emotion data and sends them to the server along with the user's unique account ID.

[0304] Step 4:

[0305] Server: The server checks the integrity of the transaction data and emotion data it receives, verifies that the data is accurate, and stores the transaction data and emotion data in the database after confirming that there is no fraud or tampering.

[0306] Step 5:

[0307] Server: When storing transaction data and emotion data in the database, information such as user ID, purchased item, price, transaction date and time, and emotional state is associated and recorded. The database is secured to a certain extent.

[0308] Step 6:

[0309] Server: The emotion engine starts working based on the stored transaction data and emotion data. The emotion engine analyzes the user's facial and voice data to identify the user's current emotional state. For example, it identifies emotions such as "happy" from facial expressions and "busy" from voice tone.

[0310] Step 7:

[0311] Server: The generation module analyzes the emotional state analyzed by the emotion engine and combines it with transaction data. This analysis generates optimal value-added information tailored to the user's emotions. For example, it suggests easy-to-make healthy recipes for an emotional state such as "busy but happy."

[0312] Step 8:

[0313] Server: Sends the generated value-added information (e.g., a simple curry recipe using "chicken, onion, and potato") to the user device. The information is converted into an appropriate format so that the user can easily understand it.

[0314] Step 9:

[0315] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[0316] Step 10:

[0317] User: The user opens the application and checks the latest transaction information (electronic receipt) and suggested value-added information (e.g. curry recipe). The user receives the best suggestions tailored to their mood, improving the quality of their daily life.

[0318] This allows the system to provide added-value information that takes user emotions into account, significantly improving the convenience of electronic receipts. Furthermore, users can enjoy a more satisfying shopping experience by receiving suggestions that perfectly match their emotions.

[0319] Example 2

[0320] 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."

[0321] In modern electronic commerce, it is important to understand not only a user's purchasing data but also their emotional state at the time to provide more personalized, value-added information. However, conventional systems are limited to collecting and analyzing transaction data and are unable to generate value-added information that takes user emotions into account. This makes it difficult to provide information that meets individual user needs, limiting improvements to the user experience. Furthermore, providing personalized information through emotion analysis is difficult to achieve due to the increased complexity of the analysis.

[0322] 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.

[0323] In this invention, the server includes a means for receiving transaction data and emotion data acquired by a user device, a means for storing the transaction data and emotion data in a database, and a generation module for analyzing the transaction data and emotion data to generate value-added information. This makes it possible to generate and provide more personalized value-added information based on the user's transaction data and associated emotion data. Specifically, the generation module comprehensively analyzes the transaction data and emotion data and provides optimal recipe information, promotion information, and the like based on the user's emotional state, thereby improving the user experience.

[0324] A "user device" is a hardware device used by a user to conduct a transaction, and includes devices such as smartphones and tablets.

[0325] "Transaction data" refers to information relating to transactions such as purchases made by users, and includes details such as product name, price, and transaction date and time.

[0326] "Emotion data" is data that expresses the user's emotional state, and includes facial expression data and voice data from camera images.

[0327] "Database" refers to a data storage system for systematically storing transaction data and emotion data, including relational database management systems (RDBMS).

[0328] The "generation module" is a software module that analyzes the received transaction data and emotion data and generates optimal value-added information for the user.

[0329] The "emotion engine" is an analysis engine that has the function of analyzing emotion data sent from a user device and recognizing the user's emotional state.

[0330] "Value-added information" refers to additional suggestions and information for users that is generated based on the analysis of transaction data and emotion data, and includes recipes, promotional information, and the like.

[0331] This invention relates to a system that uses transaction data and emotion data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information. To implement this system, the following main components are required:

[0332] System configuration

[0333] The system includes the following major components:

[0334] 1. User Device:

[0335] These devices, which include smartphones, tablets, etc., are used by users to conduct transactions. These devices have an electronic payment application installed and are equipped with the functionality to capture user transaction data and emotional data using sensors such as cameras and microphones.

[0336] 2. Server:

[0337] It has the function of receiving transaction data and emotion data and storing them in a database. It also includes analysis functions such as an emotion engine and generation module.

[0338] 3. Database:

[0339] This is data storage for saving transaction data and generated value-added information. Specifically, a relational database such as MySQL (registered trademark) is used.

[0340] 4. Emotion Engine:

[0341] It has the ability to analyze facial expression and voice data sent from the user's device using a deep learning algorithm and recognize the user's emotions.

[0342] 5. Generation module:

[0343] A software module for analyzing transaction data and recognized user emotion data and generating value-added information based on the analysis.

[0344] Program processing flow

[0345] When a user makes a transaction, the user device acquires transaction data and emotion data. The acquired data is encrypted using encryption technology such as AES and sent to the server via HTTPS protocol. The server checks the integrity of the data, stores it in a database, and then analyzes the emotion data using an emotion engine. The analysis results are integrated with the transaction data, and a generation module generates optimal value-added information (e.g., recipes or promotion information). This information is sent to the user device and displayed within the electronic payment application.

[0346] Specific examples

[0347] Consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. At this time, the camera on the user device captures the user's facial expression and the microphone records the user's voice when making the payment. The transaction data obtained is "Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" and emotion data "Smile, Happiness."

[0348] The encrypted data is then sent to the server along with the user's account ID. The server verifies the data's integrity and stores it in a database. The emotion engine detects the "feeling of happiness," and the generation module generates a recipe for "easy chicken curry" based on this information and sends it to the user's device. The user can then review the suggested recipe within the electronic payment application and begin cooking.

[0349] Prompt Sentence Examples

[0350] By inputting the following prompt sentences into the generative AI model, it is expected that the system's processing flow will be explained in detail.

[0351] You have invented a system that uses user transaction data and emotional data to store electronic receipts and generate value-added information. As a concrete example, let's show the process flow when a user buys chicken, onions, and potatoes at a supermarket. At the time of payment, the system analyzes the user's facial expressions and voice and recognizes that the user is busy but happy. Based on this information, the system suggests a simple and healthy recipe for "Easy Chicken Curry." Based on the above, please explain the system's process flow in detail.

[0352] This invention allows users to obtain personalized value-added information and suggests optimal recipes that match their mood on that day, thereby improving the quality of their daily lives.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1: Get your payment information

[0355] Terminal: When a user makes a payment using an electronic payment app, the terminal acquires transaction data (e.g., purchased item, price, transaction date and time, etc.). The terminal captures the user's facial expression with a camera and records the user's voice with a microphone. These data are stored as transaction data and emotion data.

[0356] Input: User transactions, camera images, audio

[0357] Output: Transaction data, sentiment data

[0358] What it does: It runs a facial recognition algorithm on the smartphone camera to extract facial expression data, and uses a speech recognition algorithm to analyze emotions from recorded speech.

[0359] Step 2: Send receipt information

[0360] Terminal: The acquired transaction data and emotion data are encrypted using AES (Advanced Encryption Standard) encryption technology and sent to the server along with the user's unique account ID. The transmission method uses the HTTPS protocol to ensure secure data transfer.

[0361] Input: Transaction data, emotion data, account ID

[0362] Output: Encrypted transaction data and sentiment data

[0363] Specific operation: The data encryption module encrypts the transaction data and emotion data using the AES method and sends them to the server using the HTTPS protocol.

[0364] Step 3: Save your receipt

[0365] Server: The server checks the consistency of the received transaction data and emotion data. A hash function is used to check consistency and ensure the data has not been tampered with. After checking, the data is stored in a database. A relational database such as MySQL is used as the database.

[0366] Input: Encrypted transaction data and sentiment data

[0367] Output: Integrity-checked transaction data and sentiment data

[0368] Specific operation: The server checks the integrity of the data using a hash function such as SHA-256, and then stores the data in the MySQL database.

[0369] Step 4: Analyze the sentiment data

[0370] Server: The server uses an emotion engine to analyze the user's emotional data. Specifically, it analyzes image data acquired from the camera using a deep learning model to identify emotions from facial expressions. At the same time, it analyzes audio data using voice recognition technology to determine emotions from the tone and intonation of the voice.

[0371] Input: Emotion data with integrity check

[0372] Output: Parsed emotional state

[0373] How it works: Facial expression data is analyzed using a deep learning model (e.g., a convolutional neural network) to identify emotions. Similarly, voice data is analyzed using a recurrent neural network.

[0374] Step 5: Comprehensive data analysis

[0375] Server: The server passes transaction data and recognized emotion data to the generation module. The generation module uses machine learning algorithms to generate value-added information based on the user's emotional state. For example, if the server recognizes that the user is in a happy state, it will suggest simple recipes that will increase happiness.

[0376] Input: Transaction data, analyzed emotional state

[0377] Output: Generated value-added information

[0378] Specific operation: The generation module uses a machine learning algorithm (e.g., Random Forest or Gradient Boosting Machine) to analyze the input data and generate value-added information.

[0379] Step 6: Provide value-added information

[0380] Server: Sends the generated value-added information to the user device. This transmission also uses the HTTPS protocol to ensure data security. The information includes recipes, promotional information, etc.

[0381] Input: Generated value-added information

[0382] Output: Value-added information sent to the user device

[0383] Specific operation: The generated information is sent to the user device using the HTTPS protocol.

[0384] Step 7: Viewing information

[0385] Terminal: The user device displays the received value-added information in the electronic payment application. For example, a suggested recipe is displayed along with an electronic receipt. The user can open the app and check the detailed recipe.

[0386] Input: Value-added information sent to the user device

[0387] Output: Value-added information displayed within the application

[0388] Specific behavior: The application displays the received information in an appropriate format so that the user can view it.

[0389] Through the above processing steps, the system generates personalized value-added information based on the user's transaction data and emotion data, improving the user experience.

[0390] (Application example 2)

[0391] 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."

[0392] While modern electronic payment services exist that provide value-added information based on transaction data acquired by users, they are unable to provide personalized information using user emotional data. As a result, it is difficult to provide optimal advice and promotions that correspond to the user's specific situation and emotions, and this has prevented them from improving user satisfaction and strengthening loyalty.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving transaction data and emotion data acquired by a user terminal, means for storing the transaction data and emotion data in a database, a generation engine for analyzing the transaction data and emotion data to generate value-added information, means for transmitting the value-added information to the user terminal, and means for displaying the value-added information on the user terminal. This makes it possible to provide personalized promotions and information that correspond to the user's emotional state.

[0394] "User terminal" refers to a device used by a user to conduct transactions, and specifically includes smartphones, tablets, etc.

[0395] "Transaction data" refers to information about transactions conducted by users, including product information, price information, transaction date and time, etc.

[0396] "Emotion data" refers to data that indicates the user's emotional state, and mainly includes facial expression data captured by a camera and voice data captured by a microphone.

[0397] "Database" refers to a storage device for storing acquired transaction data and emotion data.

[0398] "Generation engine" refers to a module that analyzes transaction data and emotion data and generates value-added information based on the analysis.

[0399] "Value-added information" refers to information generated based on transaction data and emotion data, and specifically includes promotion information and recipe information.

[0400] This invention is a system that uses transaction data acquired by a user terminal to store electronic receipts, analyzes the data, and generates value-added information. It also combines an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[0401] System configuration

[0402] 1. User Device:

[0403] A terminal such as a smartphone or tablet on which a user makes a transaction.

[0404] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[0405] 2. Server:

[0406] It has the function of receiving transaction data and emotion data and storing it in a database.

[0407] Includes analytical functions such as an emotion engine and a generation engine.

[0408] 3. Database:

[0409] This is a storage device for storing transaction data and emotion data.

[0410] 4. Emotion Engine:

[0411] It has the ability to analyze facial expression and voice data sent from the user's device and recognize the user's emotions.

[0412] 5. Generation engine:

[0413] This module analyzes transaction data and recognized emotion data and generates value-added information based on the analysis.

[0414] Explanation of program processing

[0415] Obtaining payment information

[0416] When a user makes a payment using an electronic payment application, the user terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also uses a camera and microphone to simultaneously acquire the user's emotional data (e.g., facial expressions and voice).

[0417] Sending receipt information

[0418] The user device encrypts the acquired transaction data and emotion data and transmits them to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[0419] Saving receipts

[0420] The server checks the integrity of the received transaction data and sentiment data and stores it in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL) to ensure consistency and durability.

[0421] Emotional Data Analysis

[0422] The emotion engine in the server analyzes the transmitted facial expression and voice data to recognize the user's emotional state. For example, it can identify emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[0423] Comprehensive analysis of data

[0424] The generation engine in the server integrates and analyzes the transaction data and the recognized emotion data, thereby generating optimal value-added information (e.g., promotional information) tailored to the user's emotions.

[0425] Providing added-value information

[0426] The generated value-added information is again transmitted to the user terminal and displayed within the application.

[0427] Adding specific examples

[0428] For example, consider the case where a user purchases a coffee at a cafe using electronic payment. At the time of payment, the emotion engine uses the camera and microphone to recognize that the user is feeling stressed. In this case, the following occurs:

[0429] 1. Obtaining payment information:

[0430] The user device acquires the transaction data "Product: Coffee, Price: 500 yen, Date and Time: 2023-10-20" and the emotion data "Stress."

[0431] 2. Sending receipt information:

[0432] The user device encrypts transaction data and emotion data and sends them to the server.

[0433] 3. Save your receipt:

[0434] The server stores the data in a database.

[0435] 4. Emotional Data Analysis:

[0436] The server analyzes the emotional state as "stress."

[0437] 5. Comprehensive data analysis:

[0438] The server generates promotions for relaxation products based on the user's emotional state, offering coupons for "aromatherapy oils for stress relief."

[0439] 6. Providing Value-Added Information:

[0440] The proposed promotion information is sent to the user terminal.

[0441] 7. Displaying Information:

[0442] The user opens the app and sees the "aromatherapy oil" coupon along with the electronic receipt.

[0443] Example prompt sentence:

[0444] Prompt for the generative AI model: "When a user is in a cafe and buying coffee while feeling stressed, please generate promotional information for relaxation products to help reduce stress."

[0445] This prompt allows the model to generate appropriate value-added information based on the user's emotional state and transaction data.

[0446] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0447] Step 1: Get your payment information

[0448] Description: When a user makes a payment using an electronic payment application, the terminal captures transaction data (item purchased, price, transaction date and time, etc.). In parallel, the emotion engine on the terminal uses the camera and microphone to capture the user's emotion data (facial expressions and voice). Specifically, the camera captures the user's facial expressions, and the microphone records the tone and volume of the voice.

[0449] Input: User purchase operation

[0450] Output: Transaction data, sentiment data

[0451] Step 2: Send receipt information

[0452] Description: The terminal encrypts the acquired transaction data and emotion data with AES encryption and sends it to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[0453] Input: Transaction data, sentiment data

[0454] Output: Encrypted data packet

[0455] Step 3: Save your receipt

[0456] Description: The server decrypts the received transaction data and sentiment data and checks the integrity and consistency of the data. It then stores this data in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL).

[0457] Input: Encrypted data packet

[0458] Output: Transaction data and sentiment data in the database

[0459] Step 4: Analyze the sentiment data

[0460] Description: The emotion engine in the server analyzes facial expression and voice data stored in the database to identify the user's emotional state. For example, it uses machine learning models (e.g., CNN or RNN) to determine whether the user is feeling "stressed" or "happy" based on facial expressions captured by a camera and tone of voice recorded by a microphone.

[0461] Input: Emotion data in the database

[0462] Output: User's emotional state

[0463] Step 5: Comprehensive data analysis

[0464] Description: The generation engine on the server integrates and analyzes transaction data and recognized emotional data. Specifically, it combines transaction data (e.g., type and price of purchased product) with emotional data (e.g., stress level) to generate value-added information (promotion information, recipe information) that is optimal for the user. In this process, a generative AI model is used to generate value-added information that meets the user's needs.

[0465] Input: Transaction data, user emotional state

[0466] Output: Value-added information

[0467] Step 6: Provide value-added information

[0468] Description: The generated value-added information is re-encrypted and sent to the terminal along with the user's unique account ID. The transmission process again uses a secure communication protocol (e.g., HTTPS).

[0469] Input: Value-added information

[0470] Output: Encrypted value-added information data packet

[0471] Step 7: Viewing information

[0472] Description: The terminal decodes the received value-added information and displays it within the electronic payment application. Specifically, promotional information and recipe information are displayed along with the electronic receipt. For example, if a user purchases coffee at a cafe and the emotion engine recognizes stress, a coupon for "aromatherapy oil for stress reduction" is displayed.

[0473] Input: Encrypted value-added information data packet

[0474] Output: Value-added information displayed to the user

[0475] 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.

[0476] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0477] 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.

[0478] [Second embodiment]

[0479] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0480] 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.

[0481] 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).

[0482] 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.

[0483] 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.

[0484] 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).

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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."

[0491] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. Specific embodiments of this system and the program processing are described below in natural language.

[0492] System configuration

[0493] This system mainly consists of the following three components:

[0494] 1. User Device:

[0495] A device used by a user to make electronic payments, typically a smartphone or tablet, has an electronic payment application (e.g., an electronic money app) installed on it.

[0496] 2. Server:

[0497] This is a server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[0498] 3. Database:

[0499] A data storage for storing transaction data and generated value-added information.

[0500] Program processing flow

[0501] 1. Obtaining payment information

[0502] Device:

[0503] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment (e.g., the product purchased, the price, and the date and time of the transaction).

[0504] 2. Send receipt information

[0505] Device:

[0506] Once the payment is complete, the terminal encrypts the transaction data it receives and sends it to the server along with the user's unique account ID.

[0507] 3. Save your receipt

[0508] server:

[0509] The server checks the integrity of the received transaction data and stores it in a database, including the user ID, purchased item, price, transaction date and time, etc.

[0510] 4. Data analysis with generative AI

[0511] server:

[0512] The server passes the stored transaction data to a generation AI, which analyzes the data and generates value-added information (such as recipe suggestions) based on the user's purchasing patterns and product information.

[0513] 5. Providing value-added information

[0514] server:

[0515] The generated value-added information is associated with the user's account and transmitted to the user device.

[0516] 6. Display of Information

[0517] Device:

[0518] The user device displays the value-added information received from the server, and when the user opens the app, they can see the proposed value-added information along with the electronic receipt.

[0519] Specific examples

[0520] For example, suppose a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes).

[0521] 1. Obtaining payment information

[0522] Terminal: Once payment is completed, the transaction data "Items: Chicken, Onion, Potato, Price: 2,000 yen, Date and Time: 2023-10-01" is obtained.

[0523] 2. Send receipt information

[0524] Terminal: The transaction data is encrypted and sent to the server along with the user ID.

[0525] 3. Save your receipt

[0526] Server: Save "User ID: 12345, Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" in the database.

[0527] 4. Data analysis with generative AI

[0528] Server: The generative AI analyzes the transaction data and generates a curry recipe using "chicken, onion, and potato."

[0529] 5. Providing value-added information

[0530] Server: Sends the generated curry recipe information to the user device.

[0531] 6. Display of Information

[0532] Device: When the user opens the app, they can see the "Chicken Curry Recipe" along with "Purchased Items: Chicken, Onion, Potato, Price: 2,000 yen, Date: 2023-10-01."

[0533] This invention saves users the trouble of managing paper receipts and allows them to receive useful information based on transaction data. This improves the convenience of electronic receipts and is expected to encourage more users to use electronic receipts.

[0534] The processing flow will be explained below.

[0535] Step 1:

[0536] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code or other data using a user device such as a smartphone or tablet, and the payment screen is displayed.

[0537] Step 2:

[0538] Terminal: The user device communicates with the store's payment system to obtain transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction.

[0539] Step 3:

[0540] Terminal: After the payment is completed, the obtained transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") is encrypted and sent to the server along with the user's unique account ID.

[0541] Step 4:

[0542] Server: The server checks the integrity of the transaction data it receives, verifies that the data is accurate, and stores the transaction data in the database after verifying that there is no fraud or tampering.

[0543] Step 5:

[0544] Server: When transaction data is saved in a database, it is recorded in association with information such as the user ID, purchased item, price, transaction date and time, etc. The database is secured to a certain extent.

[0545] Step 6:

[0546] Server: The generation AI starts working based on the stored transaction data. The generation AI analyzes the data and identifies the user's purchasing patterns and characteristics.

[0547] Step 7:

[0548] Server: The generation AI analyzes the transaction data and generates value-added information (e.g., recipes, promotional information). Specifically, it can suggest a "curry recipe" using "chicken, onions, and potatoes."

[0549] Step 8:

[0550] Server: Sends the generated value-added information to the user device, where it is converted into an appropriate format so that it can be easily understood by the user.

[0551] Step 9:

[0552] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[0553] Step 10:

[0554] User: The user opens the application and sees the latest transaction information (e-receipt) and suggested value-added information (e.g. curry recipe), which improves the user's life.

[0555] Through the above steps, the system of the present invention can acquire, store, and analyze electronic receipts, and provide added-value information in a single flow.

[0556] Example 1

[0557] 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."

[0558] With the spread of modern electronic payments, users are increasingly burdened with managing paper receipts. While systems exist that provide value-added information based on purchase data, there is a need for an automated, secure method for acquiring and providing this information that does not require user interaction. Therefore, a system is needed that integrates the encrypted transmission of purchase data, secure storage in a database, and the automatic generation and provision of value-added information using a generative AI model.

[0559] 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.

[0560] In this invention, the server includes means for receiving transaction data acquired by a user device, means for encrypting and transmitting the transaction data, means for storing the transaction data in a database, means for verifying the integrity of the stored transaction data, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information generated by the generation module to the user device, and means for displaying the value-added information on the user device. This eliminates the need for users to manage paper receipts, and further enables the system to automatically and safely acquire and store transaction data and provide useful value-added information based on it.

[0561] A "user device" is a device used by a user to make electronic payments, and typically includes a smartphone or tablet.

[0562] "Transaction data" is information generated when a user makes an electronic payment, and includes the name of the purchased product, its price, the date and time of the transaction, and the like.

[0563] "Encryption" is the process of converting transaction data using a specific algorithm to protect it from unauthorized access by third parties, ensuring that only those with the key can access the original data.

[0564] "Database" means data storage for the secure long-term preservation of transaction data and generated value-added information.

[0565] "Integrity verification" is the process of checking received transaction data for errors or unauthorized changes to ensure the authenticity of the data.

[0566] The term "generation module" refers to a program and its execution environment for analyzing transaction data and automatically generating value-added information useful to users.

[0567] "Added-value information" is information that is generated based on transaction data and that the user finds useful, and includes, for example, recipe information and product recommendation information.

[0568] A "generative AI model" is a machine learning model that has the ability to generate text data based on specific input data, and is used to generate recipe information and value-added information based on user transaction data.

[0569] "Transmission means" refers to the communication protocol and its execution environment used to safely and reliably send specific data to another device or server.

[0570] "Displaying means" refers to software and its interface for visually presenting information on the screen of a user device.

[0571] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. This system is mainly composed of a user device, a server, and a database.

[0572] System configuration

[0573] 1. User Device:

[0574] A device used by a user to make electronic payments. This device includes smartphones and tablets. An electronic payment application (e.g., an e-money app) is installed on the user device. When a user makes a purchase, payment is made using this application.

[0575] 2. Server:

[0576] The server receives transaction data and stores it in a database. It also analyzes the stored data and generates value-added information. A generative AI model is used for this analysis. The server checks the integrity of the data and ensures that the transaction data is accurate.

[0577] 3. Database:

[0578] The database is a data storage for saving transaction data and generated value-added information, allowing users' transaction history and generated value-added information to be maintained for a long period of time.

[0579] Program processing

[0580] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. This data includes the name of the purchased item, its price, the date and time of the transaction, etc. This transaction data is stored in a temporary file. Once the transaction is complete, the terminal encrypts this data and sends it to the server along with the user's unique account ID. The transmitted data is protected using encryption algorithms such as AES encryption and is sent via the HTTPS protocol.

[0581] The server verifies the integrity of the received transaction data. Specifically, it performs a data integrity check (e.g., checksum verification) to ensure that no unauthorized changes have been made. Once the integrity of the data is confirmed, information such as the user ID, purchased item, price, and transaction date and time is saved in a database. The saved transaction data is passed to a generative AI model. This generative AI model analyzes the transaction data and generates value-added information useful to the user (e.g., recipe information and recommended products).

[0582] The generated value-added information is converted back to JSON format, linked to the user ID, and sent to the user device via HTTPS. The user device displays the received value-added information within the application. When the user opens the app, they can see the proposed value-added information along with the products they purchased.

[0583] Specific examples

[0584] For example, if a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes), an example of transaction data would be as follows:

[0585] "Item: Chicken, onion, potato, Price: 2000 yen, Date: 2023-10-01"

[0586] This data is sent to a server and analyzed by a generative AI model, which then suggests a curry recipe using "chicken, onions, and potatoes." An example prompt is as follows:

[0587] Based on the purchase data of "chicken, onion, potato," the user is prompted to "suggest a recipe using these ingredients."

[0588] In this way, users can easily obtain useful information based on their purchase history, eliminating the need to manage paper receipts and allowing them to receive useful information based on transaction data.

[0589] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0590] System configuration

[0591] This system mainly consists of the following three components:

[0592] 1. User device: A device used by a user to make electronic payments, typically a smartphone or tablet, on which an electronic payment application (e.g., an e-money app) is installed.

[0593] 2. Server: This server receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[0594] 3. Database: Data storage for storing transaction data and generated value-added information.

[0595] Program processing flow

[0596] Step 1: Get your payment information

[0597] Terminal: When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. Specifically, the application records transaction data such as the name of the purchased item, its price, and the transaction date and time. This data is saved in a temporary storage folder. The input is the item purchased by the user and its details, and the output is temporary storage of transaction data.

[0598] Step 2: Send receipt information

[0599] Terminal: Once the payment is completed, the terminal encrypts and transmits the acquired transaction data. Specifically, it encrypts the transaction data using the AES encryption algorithm and sends the encrypted data and the user's account ID to the server. The transmission uses the HTTPS protocol. The input is the temporarily stored transaction data, and the output is the encrypted data and the account ID sent to the server.

[0600] Step 3: Save your receipt

[0601] Server: The server checks the integrity of the received transaction data. Specifically, it verifies the integrity of the data using a checksum. Once the integrity is confirmed, the data is stored in a database. The stored data includes the user ID, purchased item, price, transaction date and time, etc. The input is the encrypted transaction data and account ID, and the output is the data stored in the database after integrity is confirmed.

[0602] Step 4: Data analysis with generative AI

[0603] Server: The server passes the stored transaction data to the generative AI model and analyzes the data. Specifically, the transaction data is converted into JSON format and input into the generative AI model. The generative AI model generates value-added information (e.g., recipe suggestions) based on the user's purchasing patterns and product information. The input is the transaction data stored in the database, and the output is the generated value-added information.

[0604] Step 5: Provide added value information

[0605] Server: Associates the generated value-added information with the user's account and sends it to the user device. Specifically, the generated information is converted back to JSON format, linked to the user ID, and sent to the user device via the HTTPS protocol. The input is the generated value-added information, and the output is the transmission to the user device.

[0606] Step 6: Viewing information

[0607] Terminal: The user device displays the value-added information received from the server. Specifically, the electronic payment app parses the received data and displays it on the user interface. When the user opens the app, they can see the proposed value-added information along with the electronic receipt. The input is the value-added information received from the server, and the output is the information displayed on the user interface.

[0608] (Application example 1)

[0609] 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."

[0610] With the spread of electronic payments, there is a need for efficient management of transaction data acquired daily by users and for utilizing that data to provide users with useful information. However, current systems are limited to simple recording of transaction data and do not adequately provide value-added information based on users' spending patterns. Furthermore, there is a need for systems that not only manage electronic receipts but also manage income and expenditures and provide relevant campaign information. The present invention aims to solve these problems and provide a system that is more convenient and useful for users.

[0611] 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.

[0612] In this invention, the server includes means for receiving transaction data acquired by a user device, means for storing the transaction data in a database, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information to the user device, means for displaying the value-added information on the user device, and means for providing campaign information based on the user's spending patterns and purchase details as the value-added information. This allows the user to not only manage transaction data but also efficiently obtain useful information based on their daily spending patterns.

[0613] definition statement

[0614] A "user device" is a communication device used by a user to make electronic payments and whose primary use includes capturing and storing transaction data and displaying value-added information.

[0615] "Transaction data" refers to information generated when an electronic payment is made, and includes primarily product name, price, purchase date and time, etc.

[0616] "Database" means a digital storage system for storing transaction data and generated value-added information.

[0617] A "generation module" is a software component that includes artificial intelligence for analyzing transaction data and generating value-added information.

[0618] "Value-added information" is information generated based on transaction data, and includes useful suggestions, advice, and special benefit information for the user.

[0619] "Campaign information" is promotional information such as benefits and discounts that are provided to users under certain conditions, and is intended to support users' purchasing activities.

[0620] A "generative AI model" is a machine learning model used to analyze transaction data and generate value-added information based on users' spending patterns and purchasing tendencies.

[0621] "Spending patterns" are data that indicate specific tendencies or behaviors based on a user's past purchasing history.

[0622] "Special offer information" refers to information including discounts, points, or other benefits offered to users to encourage them to make purchases.

[0623] MODE FOR CARRYING OUT THE INVENTION

[0624] The system of the present invention collects, stores, and analyzes electronic payment data of users, and provides value-added information based on the collected data. A specific embodiment of this system will be described below.

[0625] System configuration

[0626] This system mainly consists of the following three components:

[0627] 1. User Device

[0628] A communication device used by a user to make electronic payments, typically a smartphone, has an electronic payment application installed on it.

[0629] 2. Server

[0630] A server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[0631] 3. Database

[0632] A digital storage system for storing transaction data and generated value-added information.

[0633] Hardware and software used

[0634] Examples of specific hardware and software used in this system include:

[0635] Hardware: Smartphone

[0636] Software: Application frameworks (e.g., React Native), servers (e.g., AWS, Firebase), databases (e.g., MongoDB, Firebase Realtime Database), generative AI models (e.g., OpenAI's GPT-4)

[0637] Explanation of program processing

[0638] 1. Obtaining payment information

[0639] The electronic payment application on the user device automatically acquires transaction data (e.g., product name, price, purchase date and time) when payment is completed.

[0640] 2. Send receipt information

[0641] The acquired transaction data is encrypted and sent to a dedicated cloud server along with the user's unique ID.

[0642] 3. Save your receipt

[0643] The server verifies the integrity of the received transaction data and stores it in a database.

[0644] 4. Data analysis with generative AI

[0645] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information (e.g., recipe information, campaign information, and bonus information).

[0646] 5. Providing value-added information

[0647] The generated value-added information is associated with the user's account and transmitted to the user device.

[0648] 6. Display of Information

[0649] The user device displays the value-added information received from the server, and the user can open the app to view the proposed value-added information along with the electronic receipt.

[0650] Specific examples

[0651] For example, if a user uses an electronic payment app at a convenience store to purchase 1,000 yen worth of drinks and snacks, the following process occurs:

[0652] 1. Obtaining payment information

[0653] When payment is completed, the user device acquires the transaction data "Product: Beverage, Snack, Price: 1,000 yen, Date and Time: 2023-10-01."

[0654] 2. Send receipt information

[0655] This transaction data is encrypted and sent to the cloud server along with the user ID.

[0656] 3. Save your receipt

[0657] The server saves the data in the database as "User ID: 67890, Product: Drink, Snack, Price: 1,000 yen, Date and Time: 2023-10-01".

[0658] 4. Data analysis with generative AI

[0659] The generative AI model analyzes transaction data and generates campaign information for users, such as "a snack set perfect for watching a movie on the weekend."

[0660] 5. Providing value-added information

[0661] Campaign information "We recommend this snack for watching movies on the weekend! Enjoy it with a popular movie title" will be sent to users' smartphones.

[0662] 6. Display of Information

[0663] When a user opens the app, "Purchased items: drinks, snacks, price: 1,000 yen, date and time: 2023-10-01" along with "Recommended movie viewing information" will be displayed.

[0664] Prompt Sentence Examples

[0665] An example of a prompt is:

[0666] The user has purchased drinks and snacks. Please provide recommendations for how to enjoy the weekend.

[0667] In this way, the system according to the present invention effectively utilizes the user's expenditure data and provides useful information for daily life.

[0668] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0669] Program processing

[0670] Explain the process step by step

[0671] Step 1:

[0672] A user makes a payment using an electronic payment app. At this moment, the user device automatically obtains transaction data (e.g., product name, price, purchase date and time). Specifically, the app detects the payment completion event and calls an API to obtain payment information.

[0673] (Input): Electronic payment completion information

[0674] (Output): Transaction data related to the payment (e.g. product name, price, purchase date and time)

[0675] Step 2:

[0676] The acquired transaction data is encrypted and sent to a cloud server along with the user's unique ID. The user device then uses an encryption algorithm to secure the data before sending it to the server via the internet.

[0677] (Input): Payment transaction data, user ID

[0678] (Output): Encrypted transaction data, user ID

[0679] Step 3:

[0680] The server verifies the integrity of the received transaction data and stores it in the database. The server first decrypts the data, then performs an integrity check, and then calls an API to store it in the database.

[0681] (Input): Encrypted transaction data, user ID

[0682] (Output): Transaction data stored in the database

[0683] Step 4:

[0684] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information. The server first retrieves the transaction data from the database, then generates appropriate prompts for the generative AI model and performs the analysis.

[0685] (Input): Transaction data in the database

[0686] (Output): Added-value information generated by the generative AI model (e.g., recipe information, campaign information, special offer information)

[0687] Step 5:

[0688] The generated value-added information is associated with the user's account and transmitted to the user device. The server then links the value-added information to the user ID and transmits the data to the user device using a notification function.

[0689] (Input): Generated value-added information, user ID

[0690] (Output): Value-added information sent to the user device

[0691] Step 6:

[0692] The user device displays the value-added information received from the server. When the user opens the electronic payment app, they can view the value-added information along with the latest transaction data. Specifically, the app processes the received data and displays it on the user interface.

[0693] (Input): Value-added information received from the server

[0694] (Output): Value-added information displayed within the app (e.g., recipes, campaign information)

[0695] Through the above steps, a system is realized that allows users to effectively use electronic payment data and obtain useful added-value information.

[0696] 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.

[0697] The present invention relates to a system that uses transaction data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information, and further combines this with an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[0698] System configuration

[0699] The system includes the following major components:

[0700] 1. User Device:

[0701] A device such as a smartphone or tablet on which users make transactions.

[0702] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[0703] 2. Server:

[0704] It has the function of receiving transaction data and storing it in a database.

[0705] It includes analytical functions such as emotion engines and generation modules.

[0706] 3. Database:

[0707] Data storage for storing transaction data and generated value-added information.

[0708] 4. Emotion Engine:

[0709] It has the ability to analyze facial expression data and voice data sent from the user device and recognize the user's emotions.

[0710] 5. Generation module:

[0711] Transaction data and perceived user sentiment are analyzed and value-added information is generated based thereon.

[0712] Program processing flow

[0713] 1. Obtaining payment information

[0714] Device:

[0715] When a user makes a payment using an electronic payment app, the terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also acquires the user's emotional data (facial expressions and voice).

[0716] 2. Send receipt information

[0717] Device:

[0718] Transaction data and emotion data are encrypted and sent to the server along with the user's unique account ID.

[0719] 3. Save your receipt

[0720] server:

[0721] The consistency of the transaction data and sentiment data is checked and stored in the database.

[0722] 4. Emotion Data Analysis

[0723] server:

[0724] The emotion engine analyzes the user's emotional data and recognizes their current emotional state, for example, by identifying emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[0725] 5. Comprehensive data analysis

[0726] server:

[0727] The generation module analyzes the transaction data and the recognized emotion data, thereby generating the optimal value-added information that matches the user's emotions.

[0728] 6. Providing value-added information

[0729] server:

[0730] The generated value-added information (e.g., recipes, promotional information) is transmitted to the user device.

[0731] 7. Display of Information

[0732] Device:

[0733] The user device displays the received value-added information within the application.

[0734] Specific examples

[0735] For example, consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. When paying, the emotion engine uses a camera to detect the user's facial expressions and analyzes their voice to determine that the user is busy but happy.

[0736] 1. Obtaining payment information:

[0737] Terminal: Obtain transaction data "Product: Chicken, onion, potato, Price: 2000 yen, Date and time: 2023-10-01" and emotion data.

[0738] 2. Sending receipt information:

[0739] Terminal: Transaction data and emotional data are encrypted and sent to the server.

[0740] 3. Save your receipt:

[0741] Server: Stored in the database.

[0742] 4. Emotional Data Analysis:

[0743] Server: Emotional state analyzed as "busy but happy."

[0744] 5. Comprehensive data analysis:

[0745] Server: Based on the user's emotional state, the server suggests a simple and healthy recipe for "Easy Chicken Curry."

[0746] 6. Providing Value-Added Information:

[0747] Server: Sends suggested recipe information to the user device.

[0748] 7. Displaying Information:

[0749] Device: The user opens the app and sees the recipe for "Easy Chicken Curry" along with their digital receipt.

[0750] The present invention allows users to obtain more personalized added-value information, and improves the quality of their daily lives by suggesting optimal recipes that match their mood on that day, for example.

[0751] The processing flow will be explained below.

[0752] Step 1:

[0753] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code or other data using a user device such as a smartphone or tablet, and the payment screen is displayed. At this time, the camera and microphone are activated to recognize the user's emotions.

[0754] Step 2:

[0755] Terminal: The user's device communicates with the store's payment system and acquires transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction. At the same time, the camera and microphone are used to acquire facial expression and voice data from the user.

[0756] Step 3:

[0757] Terminal: Once the payment is completed, the terminal encrypts the transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") and emotion data and sends them to the server along with the user's unique account ID.

[0758] Step 4:

[0759] Server: The server checks the integrity of the transaction data and emotion data it receives, verifies that the data is accurate, and stores the transaction data and emotion data in the database after confirming that there is no fraud or tampering.

[0760] Step 5:

[0761] Server: When storing transaction data and emotion data in the database, information such as user ID, purchased item, price, transaction date and time, and emotional state is associated and recorded. The database is secured to a certain extent.

[0762] Step 6:

[0763] Server: The emotion engine starts working based on the stored transaction data and emotion data. The emotion engine analyzes the user's facial and voice data to identify the user's current emotional state. For example, it identifies emotions such as "happy" from facial expressions and "busy" from voice tone.

[0764] Step 7:

[0765] Server: The generation module analyzes the emotional state analyzed by the emotion engine and combines it with transaction data. This analysis generates optimal value-added information tailored to the user's emotions. For example, it suggests easy-to-make healthy recipes for an emotional state such as "busy but happy."

[0766] Step 8:

[0767] Server: Sends the generated value-added information (e.g., a simple curry recipe using "chicken, onion, and potato") to the user device. The information is converted into an appropriate format so that the user can easily understand it.

[0768] Step 9:

[0769] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[0770] Step 10:

[0771] User: The user opens the application and checks the latest transaction information (electronic receipt) and suggested value-added information (e.g. curry recipe). The user receives the best suggestions tailored to their mood, improving the quality of their daily life.

[0772] This allows the system to provide added-value information that takes user emotions into account, significantly improving the convenience of electronic receipts. Furthermore, users can enjoy a more satisfying shopping experience by receiving suggestions that perfectly match their emotions.

[0773] Example 2

[0774] 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."

[0775] In modern electronic commerce, it is important to understand not only a user's purchasing data but also their emotional state at the time to provide more personalized, value-added information. However, conventional systems are limited to collecting and analyzing transaction data and are unable to generate value-added information that takes user emotions into account. This makes it difficult to provide information that meets individual user needs, limiting improvements to the user experience. Furthermore, providing personalized information through emotion analysis is difficult to achieve due to the increased complexity of the analysis.

[0776] 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.

[0777] In this invention, the server includes a means for receiving transaction data and emotion data acquired by a user device, a means for storing the transaction data and emotion data in a database, and a generation module for analyzing the transaction data and emotion data to generate value-added information. This makes it possible to generate and provide more personalized value-added information based on the user's transaction data and associated emotion data. Specifically, the generation module comprehensively analyzes the transaction data and emotion data and provides optimal recipe information, promotion information, and the like based on the user's emotional state, thereby improving the user experience.

[0778] A "user device" is a hardware device used by a user to conduct a transaction, and includes devices such as smartphones and tablets.

[0779] "Transaction data" refers to information relating to transactions such as purchases made by users, and includes details such as product name, price, and transaction date and time.

[0780] "Emotion data" is data that expresses the user's emotional state, and includes facial expression data and voice data from camera images.

[0781] "Database" refers to a data storage system for systematically storing transaction data and emotion data, including relational database management systems (RDBMS).

[0782] The "generation module" is a software module that analyzes the received transaction data and emotion data and generates optimal value-added information for the user.

[0783] The "emotion engine" is an analysis engine that has the function of analyzing emotion data sent from a user device and recognizing the user's emotional state.

[0784] "Value-added information" refers to additional suggestions and information for users that is generated based on the analysis of transaction data and emotion data, and includes recipes, promotional information, and the like.

[0785] This invention relates to a system that uses transaction data and emotion data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information. To implement this system, the following main components are required:

[0786] System configuration

[0787] The system includes the following major components:

[0788] 1. User Device:

[0789] These devices, which include smartphones, tablets, etc., are used by users to conduct transactions. These devices have an electronic payment application installed and are equipped with the functionality to capture user transaction data and emotional data using sensors such as cameras and microphones.

[0790] 2. Server:

[0791] It has the function of receiving transaction data and emotion data and storing them in a database. It also includes analysis functions such as an emotion engine and generation module.

[0792] 3. Database:

[0793] Data storage for storing transaction data and generated value-added information, specifically using a relational database such as MySQL.

[0794] 4. Emotion Engine:

[0795] It has the ability to analyze facial expression and voice data sent from the user's device using a deep learning algorithm and recognize the user's emotions.

[0796] 5. Generation module:

[0797] A software module for analyzing transaction data and recognized user emotion data and generating value-added information based on the analysis.

[0798] Program processing flow

[0799] When a user makes a transaction, the user device acquires transaction data and emotion data. The acquired data is encrypted using encryption technology such as AES and sent to the server via HTTPS protocol. The server checks the integrity of the data, stores it in a database, and then analyzes the emotion data using an emotion engine. The analysis results are integrated with the transaction data, and a generation module generates optimal value-added information (e.g., recipes or promotion information). This information is sent to the user device and displayed within the electronic payment application.

[0800] Specific examples

[0801] Consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. At this time, the camera on the user device captures the user's facial expression and the microphone records the user's voice when making the payment. The transaction data obtained is "Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" and emotion data "Smile, Happiness."

[0802] The encrypted data is then sent to the server along with the user's account ID. The server verifies the data's integrity and stores it in a database. The emotion engine detects the "feeling of happiness," and the generation module generates a recipe for "easy chicken curry" based on this information and sends it to the user's device. The user can then review the suggested recipe within the electronic payment application and begin cooking.

[0803] Prompt Sentence Examples

[0804] By inputting the following prompt sentences into the generative AI model, it is expected that the system's processing flow will be explained in detail.

[0805] You have invented a system that uses user transaction data and emotional data to store electronic receipts and generate value-added information. As a concrete example, let's show the process flow when a user buys chicken, onions, and potatoes at a supermarket. At the time of payment, the system analyzes the user's facial expressions and voice and recognizes that the user is busy but happy. Based on this information, the system suggests a simple and healthy recipe for "Easy Chicken Curry." Based on the above, please explain the system's process flow in detail.

[0806] This invention allows users to obtain personalized value-added information and suggests optimal recipes that match their mood on that day, thereby improving the quality of their daily lives.

[0807] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0808] Step 1: Get your payment information

[0809] Terminal: When a user makes a payment using an electronic payment app, the terminal acquires transaction data (e.g., purchased item, price, transaction date and time, etc.). The terminal captures the user's facial expression with a camera and records the user's voice with a microphone. These data are stored as transaction data and emotion data.

[0810] Input: User transactions, camera images, audio

[0811] Output: Transaction data, sentiment data

[0812] What it does: It runs a facial recognition algorithm on the smartphone camera to extract facial expression data, and uses a speech recognition algorithm to analyze emotions from recorded speech.

[0813] Step 2: Send receipt information

[0814] Terminal: The acquired transaction data and emotion data are encrypted using AES (Advanced Encryption Standard) encryption technology and sent to the server along with the user's unique account ID. The transmission method uses the HTTPS protocol to ensure secure data transfer.

[0815] Input: Transaction data, emotion data, account ID

[0816] Output: Encrypted transaction data and sentiment data

[0817] Specific operation: The data encryption module encrypts the transaction data and emotion data using the AES method and sends them to the server using the HTTPS protocol.

[0818] Step 3: Save your receipt

[0819] Server: The server checks the consistency of the received transaction data and emotion data. A hash function is used to check consistency and ensure the data has not been tampered with. After checking, the data is stored in a database. A relational database such as MySQL is used as the database.

[0820] Input: Encrypted transaction data and sentiment data

[0821] Output: Integrity-checked transaction data and sentiment data

[0822] Specific operation: The server checks the integrity of the data using a hash function such as SHA-256, and then stores the data in the MySQL database.

[0823] Step 4: Analyze the sentiment data

[0824] Server: The server uses an emotion engine to analyze the user's emotional data. Specifically, it analyzes image data acquired from the camera using a deep learning model to identify emotions from facial expressions. At the same time, it analyzes audio data using voice recognition technology to determine emotions from the tone and intonation of the voice.

[0825] Input: Emotion data with integrity check

[0826] Output: Parsed emotional state

[0827] How it works: Facial expression data is analyzed using a deep learning model (e.g., a convolutional neural network) to identify emotions. Similarly, voice data is analyzed using a recurrent neural network.

[0828] Step 5: Comprehensive data analysis

[0829] Server: The server passes transaction data and recognized emotion data to the generation module. The generation module uses machine learning algorithms to generate value-added information based on the user's emotional state. For example, if the server recognizes that the user is in a happy state, it will suggest simple recipes that will increase happiness.

[0830] Input: Transaction data, analyzed emotional state

[0831] Output: Generated value-added information

[0832] Specific operation: The generation module uses a machine learning algorithm (e.g., Random Forest or Gradient Boosting Machine) to analyze the input data and generate value-added information.

[0833] Step 6: Provide value-added information

[0834] Server: Sends the generated value-added information to the user device. This transmission also uses the HTTPS protocol to ensure data security. The information includes recipes, promotional information, etc.

[0835] Input: Generated value-added information

[0836] Output: Value-added information sent to the user device

[0837] Specific operation: The generated information is sent to the user device using the HTTPS protocol.

[0838] Step 7: Viewing information

[0839] Terminal: The user device displays the received value-added information in the electronic payment application. For example, a suggested recipe is displayed along with an electronic receipt. The user can open the app and check the detailed recipe.

[0840] Input: Value-added information sent to the user device

[0841] Output: Value-added information displayed within the application

[0842] Specific behavior: The application displays the received information in an appropriate format so that the user can view it.

[0843] Through the above processing steps, the system generates personalized value-added information based on the user's transaction data and emotion data, improving the user experience.

[0844] (Application example 2)

[0845] 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."

[0846] While modern electronic payment services exist that provide value-added information based on transaction data acquired by users, they are unable to provide personalized information using user emotional data. As a result, it is difficult to provide optimal advice and promotions that correspond to the user's specific situation and emotions, and this has prevented them from improving user satisfaction and strengthening loyalty.

[0847] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving transaction data and emotion data acquired by a user terminal, means for storing the transaction data and emotion data in a database, a generation engine for analyzing the transaction data and emotion data to generate value-added information, means for transmitting the value-added information to the user terminal, and means for displaying the value-added information on the user terminal. This makes it possible to provide personalized promotions and information that correspond to the user's emotional state.

[0848] "User terminal" refers to a device used by a user to conduct transactions, and specifically includes smartphones, tablets, etc.

[0849] "Transaction data" refers to information about transactions conducted by users, including product information, price information, transaction date and time, etc.

[0850] "Emotion data" refers to data that indicates the user's emotional state, and mainly includes facial expression data captured by a camera and voice data captured by a microphone.

[0851] "Database" refers to a storage device for storing acquired transaction data and emotion data.

[0852] "Generation engine" refers to a module that analyzes transaction data and emotion data and generates value-added information based on the analysis.

[0853] "Value-added information" refers to information generated based on transaction data and emotion data, and specifically includes promotion information and recipe information.

[0854] This invention is a system that uses transaction data acquired by a user terminal to store electronic receipts, analyzes the data, and generates value-added information. It also combines an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[0855] System configuration

[0856] 1. User Device:

[0857] A terminal such as a smartphone or tablet on which a user makes a transaction.

[0858] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[0859] 2. Server:

[0860] It has the function of receiving transaction data and emotion data and storing it in a database.

[0861] Includes analytical functions such as an emotion engine and a generation engine.

[0862] 3. Database:

[0863] This is a storage device for storing transaction data and emotion data.

[0864] 4. Emotion Engine:

[0865] It has the ability to analyze facial expression and voice data sent from the user's device and recognize the user's emotions.

[0866] 5. Generation engine:

[0867] This module analyzes transaction data and recognized emotion data and generates value-added information based on the analysis.

[0868] Explanation of program processing

[0869] Obtaining payment information

[0870] When a user makes a payment using an electronic payment application, the user terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also uses a camera and microphone to simultaneously acquire the user's emotional data (e.g., facial expressions and voice).

[0871] Sending receipt information

[0872] The user device encrypts the acquired transaction data and emotion data and transmits them to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[0873] Saving receipts

[0874] The server checks the integrity of the received transaction data and sentiment data and stores it in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL) to ensure consistency and durability.

[0875] Emotional Data Analysis

[0876] The emotion engine in the server analyzes the transmitted facial expression and voice data to recognize the user's emotional state. For example, it can identify emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[0877] Comprehensive analysis of data

[0878] The generation engine in the server integrates and analyzes the transaction data and the recognized emotion data, thereby generating optimal value-added information (e.g., promotional information) tailored to the user's emotions.

[0879] Providing added-value information

[0880] The generated value-added information is again transmitted to the user terminal and displayed within the application.

[0881] Adding specific examples

[0882] For example, consider the case where a user purchases a coffee at a cafe using electronic payment. At the time of payment, the emotion engine uses the camera and microphone to recognize that the user is feeling stressed. In this case, the following occurs:

[0883] 1. Obtaining payment information:

[0884] The user device acquires the transaction data "Product: Coffee, Price: 500 yen, Date and Time: 2023-10-20" and the emotion data "Stress."

[0885] 2. Sending receipt information:

[0886] The user device encrypts transaction data and emotion data and sends them to the server.

[0887] 3. Save your receipt:

[0888] The server stores the data in a database.

[0889] 4. Emotional Data Analysis:

[0890] The server analyzes the emotional state as "stress."

[0891] 5. Comprehensive data analysis:

[0892] The server generates promotions for relaxation products based on the user's emotional state, offering coupons for "aromatherapy oils for stress relief."

[0893] 6. Providing Value-Added Information:

[0894] The proposed promotion information is sent to the user terminal.

[0895] 7. Displaying Information:

[0896] The user opens the app and sees the "aromatherapy oil" coupon along with the electronic receipt.

[0897] Example prompt sentence:

[0898] Prompt for the generative AI model: "When a user is in a cafe and buying coffee while feeling stressed, please generate promotional information for relaxation products to help reduce stress."

[0899] This prompt allows the model to generate appropriate value-added information based on the user's emotional state and transaction data.

[0900] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0901] Step 1: Get your payment information

[0902] Description: When a user makes a payment using an electronic payment application, the terminal captures transaction data (item purchased, price, transaction date and time, etc.). In parallel, the emotion engine on the terminal uses the camera and microphone to capture the user's emotion data (facial expressions and voice). Specifically, the camera captures the user's facial expressions, and the microphone records the tone and volume of the voice.

[0903] Input: User purchase operation

[0904] Output: Transaction data, sentiment data

[0905] Step 2: Send receipt information

[0906] Description: The terminal encrypts the acquired transaction data and emotion data with AES encryption and sends it to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[0907] Input: Transaction data, sentiment data

[0908] Output: Encrypted data packet

[0909] Step 3: Save your receipt

[0910] Description: The server decrypts the received transaction data and sentiment data and checks the integrity and consistency of the data. It then stores this data in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL).

[0911] Input: Encrypted data packet

[0912] Output: Transaction data and sentiment data in the database

[0913] Step 4: Analyze the sentiment data

[0914] Description: The emotion engine in the server analyzes facial expression and voice data stored in the database to identify the user's emotional state. For example, it uses machine learning models (e.g., CNN or RNN) to determine whether the user is feeling "stressed" or "happy" based on facial expressions captured by a camera and tone of voice recorded by a microphone.

[0915] Input: Emotion data in the database

[0916] Output: User's emotional state

[0917] Step 5: Comprehensive data analysis

[0918] Description: The generation engine on the server integrates and analyzes transaction data and recognized emotional data. Specifically, it combines transaction data (e.g., type and price of purchased product) with emotional data (e.g., stress level) to generate value-added information (promotion information, recipe information) that is optimal for the user. In this process, a generative AI model is used to generate value-added information that meets the user's needs.

[0919] Input: Transaction data, user emotional state

[0920] Output: Value-added information

[0921] Step 6: Provide value-added information

[0922] Description: The generated value-added information is re-encrypted and sent to the terminal along with the user's unique account ID. The transmission process again uses a secure communication protocol (e.g., HTTPS).

[0923] Input: Value-added information

[0924] Output: Encrypted value-added information data packet

[0925] Step 7: Viewing information

[0926] Description: The terminal decodes the received value-added information and displays it within the electronic payment application. Specifically, promotional information and recipe information are displayed along with the electronic receipt. For example, if a user purchases coffee at a cafe and the emotion engine recognizes stress, a coupon for "aromatherapy oil for stress reduction" is displayed.

[0927] Input: Encrypted value-added information data packet

[0928] Output: Value-added information displayed to the user

[0929] 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.

[0930] 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.

[0931] 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.

[0932] [Third embodiment]

[0933] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0934] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0935] 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).

[0936] 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.

[0937] 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.

[0938] 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).

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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."

[0945] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. Specific embodiments of this system and the program processing are described below in natural language.

[0946] System configuration

[0947] This system mainly consists of the following three components:

[0948] 1. User Device:

[0949] A device used by a user to make electronic payments, typically a smartphone or tablet, has an electronic payment application (e.g., an electronic money app) installed on it.

[0950] 2. Server:

[0951] This is a server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[0952] 3. Database:

[0953] A data storage for storing transaction data and generated value-added information.

[0954] Program processing flow

[0955] 1. Obtaining payment information

[0956] Device:

[0957] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment (e.g., the product purchased, the price, and the date and time of the transaction).

[0958] 2. Send receipt information

[0959] Device:

[0960] Once the payment is complete, the terminal encrypts the transaction data it receives and sends it to the server along with the user's unique account ID.

[0961] 3. Save your receipt

[0962] server:

[0963] The server checks the integrity of the received transaction data and stores it in a database, including the user ID, purchased item, price, transaction date and time, etc.

[0964] 4. Data analysis with generative AI

[0965] server:

[0966] The server passes the stored transaction data to a generation AI, which analyzes the data and generates value-added information (such as recipe suggestions) based on the user's purchasing patterns and product information.

[0967] 5. Providing value-added information

[0968] server:

[0969] The generated value-added information is associated with the user's account and transmitted to the user device.

[0970] 6. Display of Information

[0971] Device:

[0972] The user device displays the value-added information received from the server, and when the user opens the app, they can see the proposed value-added information along with the electronic receipt.

[0973] Specific examples

[0974] For example, suppose a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes).

[0975] 1. Obtaining payment information

[0976] Terminal: Once payment is completed, the transaction data "Items: Chicken, Onion, Potato, Price: 2,000 yen, Date and Time: 2023-10-01" is obtained.

[0977] 2. Send receipt information

[0978] Terminal: The transaction data is encrypted and sent to the server along with the user ID.

[0979] 3. Save your receipt

[0980] Server: Save "User ID: 12345, Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" in the database.

[0981] 4. Data analysis with generative AI

[0982] Server: The generative AI analyzes the transaction data and generates a curry recipe using "chicken, onion, and potato."

[0983] 5. Providing value-added information

[0984] Server: Sends the generated curry recipe information to the user device.

[0985] 6. Display of Information

[0986] Device: When the user opens the app, they can see the "Chicken Curry Recipe" along with "Purchased Items: Chicken, Onion, Potato, Price: 2,000 yen, Date: 2023-10-01."

[0987] This invention saves users the trouble of managing paper receipts and allows them to receive useful information based on transaction data. This improves the convenience of electronic receipts and is expected to encourage more users to use electronic receipts.

[0988] The processing flow will be explained below.

[0989] Step 1:

[0990] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code or other data using a user device such as a smartphone or tablet, and the payment screen is displayed.

[0991] Step 2:

[0992] Terminal: The user device communicates with the store's payment system to obtain transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction.

[0993] Step 3:

[0994] Terminal: After the payment is completed, the obtained transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") is encrypted and sent to the server along with the user's unique account ID.

[0995] Step 4:

[0996] Server: The server checks the integrity of the transaction data it receives, verifies that the data is accurate, and stores the transaction data in the database after verifying that there is no fraud or tampering.

[0997] Step 5:

[0998] Server: When transaction data is saved in a database, it is recorded in association with information such as the user ID, purchased item, price, transaction date and time, etc. The database is secured to a certain extent.

[0999] Step 6:

[1000] Server: The generation AI starts working based on the stored transaction data. The generation AI analyzes the data and identifies the user's purchasing patterns and characteristics.

[1001] Step 7:

[1002] Server: The generation AI analyzes the transaction data and generates value-added information (e.g., recipes, promotional information). Specifically, it can suggest a "curry recipe" using "chicken, onions, and potatoes."

[1003] Step 8:

[1004] Server: Sends the generated value-added information to the user device, where it is converted into an appropriate format so that it can be easily understood by the user.

[1005] Step 9:

[1006] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[1007] Step 10:

[1008] User: The user opens the application and sees the latest transaction information (e-receipt) and suggested value-added information (e.g. curry recipe), which improves the user's life.

[1009] Through the above steps, the system of the present invention can acquire, store, and analyze electronic receipts, and provide added-value information in a single flow.

[1010] Example 1

[1011] 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."

[1012] With the spread of modern electronic payments, users are increasingly burdened with managing paper receipts. While systems exist that provide value-added information based on purchase data, there is a need for an automated, secure method for acquiring and providing this information that does not require user interaction. Therefore, a system is needed that integrates the encrypted transmission of purchase data, secure storage in a database, and the automatic generation and provision of value-added information using a generative AI model.

[1013] 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.

[1014] In this invention, the server includes means for receiving transaction data acquired by a user device, means for encrypting and transmitting the transaction data, means for storing the transaction data in a database, means for verifying the integrity of the stored transaction data, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information generated by the generation module to the user device, and means for displaying the value-added information on the user device. This eliminates the need for users to manage paper receipts, and further enables the system to automatically and safely acquire and store transaction data and provide useful value-added information based on it.

[1015] A "user device" is a device used by a user to make electronic payments, and typically includes a smartphone or tablet.

[1016] "Transaction data" is information generated when a user makes an electronic payment, and includes the name of the purchased product, its price, the date and time of the transaction, and the like.

[1017] "Encryption" is the process of converting transaction data using a specific algorithm to protect it from unauthorized access by third parties, ensuring that only those with the key can access the original data.

[1018] "Database" means data storage for the secure long-term preservation of transaction data and generated value-added information.

[1019] "Integrity verification" is the process of checking received transaction data for errors or unauthorized changes to ensure the authenticity of the data.

[1020] The term "generation module" refers to a program and its execution environment for analyzing transaction data and automatically generating value-added information useful to users.

[1021] "Added-value information" is information that is generated based on transaction data and that the user finds useful, and includes, for example, recipe information and product recommendation information.

[1022] A "generative AI model" is a machine learning model that has the ability to generate text data based on specific input data, and is used to generate recipe information and value-added information based on user transaction data.

[1023] "Transmission means" refers to the communication protocol and its execution environment used to safely and reliably send specific data to another device or server.

[1024] "Displaying means" refers to software and its interface for visually presenting information on the screen of a user device.

[1025] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. This system is mainly composed of a user device, a server, and a database.

[1026] System configuration

[1027] 1. User Device:

[1028] A device used by a user to make electronic payments. This device includes smartphones and tablets. An electronic payment application (e.g., an e-money app) is installed on the user device. When a user makes a purchase, payment is made using this application.

[1029] 2. Server:

[1030] The server receives transaction data and stores it in a database. It also analyzes the stored data and generates value-added information. A generative AI model is used for this analysis. The server checks the integrity of the data and ensures that the transaction data is accurate.

[1031] 3. Database:

[1032] The database is a data storage for saving transaction data and generated value-added information, allowing users' transaction history and generated value-added information to be maintained for a long period of time.

[1033] Program processing

[1034] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. This data includes the name of the purchased item, its price, the date and time of the transaction, etc. This transaction data is stored in a temporary file. Once the transaction is complete, the terminal encrypts this data and sends it to the server along with the user's unique account ID. The transmitted data is protected using encryption algorithms such as AES encryption and is sent via the HTTPS protocol.

[1035] The server verifies the integrity of the received transaction data. Specifically, it performs a data integrity check (e.g., checksum verification) to ensure that no unauthorized changes have been made. Once the integrity of the data is confirmed, information such as the user ID, purchased item, price, and transaction date and time is saved in a database. The saved transaction data is passed to a generative AI model. This generative AI model analyzes the transaction data and generates value-added information useful to the user (e.g., recipe information and recommended products).

[1036] The generated value-added information is converted back to JSON format, linked to the user ID, and sent to the user device via HTTPS. The user device displays the received value-added information within the application. When the user opens the app, they can see the proposed value-added information along with the products they purchased.

[1037] Specific examples

[1038] For example, if a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes), an example of transaction data would be as follows:

[1039] "Item: Chicken, onion, potato, Price: 2000 yen, Date: 2023-10-01"

[1040] This data is sent to a server and analyzed by a generative AI model, which then suggests a curry recipe using "chicken, onions, and potatoes." An example prompt is as follows:

[1041] Based on the purchase data of "chicken, onion, potato," the user is prompted to "suggest a recipe using these ingredients."

[1042] In this way, users can easily obtain useful information based on their purchase history, eliminating the need to manage paper receipts and allowing them to receive useful information based on transaction data.

[1043] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1044] System configuration

[1045] This system mainly consists of the following three components:

[1046] 1. User device: A device used by a user to make electronic payments, typically a smartphone or tablet, on which an electronic payment application (e.g., an e-money app) is installed.

[1047] 2. Server: This server receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[1048] 3. Database: Data storage for storing transaction data and generated value-added information.

[1049] Program processing flow

[1050] Step 1: Get your payment information

[1051] Terminal: When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. Specifically, the application records transaction data such as the name of the purchased item, its price, and the transaction date and time. This data is saved in a temporary storage folder. The input is the item purchased by the user and its details, and the output is temporary storage of transaction data.

[1052] Step 2: Send receipt information

[1053] Terminal: Once the payment is completed, the terminal encrypts and transmits the acquired transaction data. Specifically, it encrypts the transaction data using the AES encryption algorithm and sends the encrypted data and the user's account ID to the server. The transmission uses the HTTPS protocol. The input is the temporarily stored transaction data, and the output is the encrypted data and the account ID sent to the server.

[1054] Step 3: Save your receipt

[1055] Server: The server checks the integrity of the received transaction data. Specifically, it verifies the integrity of the data using a checksum. Once the integrity is confirmed, the data is stored in a database. The stored data includes the user ID, purchased item, price, transaction date and time, etc. The input is the encrypted transaction data and account ID, and the output is the data stored in the database after integrity is confirmed.

[1056] Step 4: Data analysis with generative AI

[1057] Server: The server passes the stored transaction data to the generative AI model and analyzes the data. Specifically, the transaction data is converted into JSON format and input into the generative AI model. The generative AI model generates value-added information (e.g., recipe suggestions) based on the user's purchasing patterns and product information. The input is the transaction data stored in the database, and the output is the generated value-added information.

[1058] Step 5: Provide added value information

[1059] Server: Associates the generated value-added information with the user's account and sends it to the user device. Specifically, the generated information is converted back to JSON format, linked to the user ID, and sent to the user device via the HTTPS protocol. The input is the generated value-added information, and the output is the transmission to the user device.

[1060] Step 6: Viewing information

[1061] Terminal: The user device displays the value-added information received from the server. Specifically, the electronic payment app parses the received data and displays it on the user interface. When the user opens the app, they can see the proposed value-added information along with the electronic receipt. The input is the value-added information received from the server, and the output is the information displayed on the user interface.

[1062] (Application example 1)

[1063] 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."

[1064] With the spread of electronic payments, there is a need for efficient management of transaction data acquired daily by users and for utilizing that data to provide users with useful information. However, current systems are limited to simple recording of transaction data and do not adequately provide value-added information based on users' spending patterns. Furthermore, there is a need for systems that not only manage electronic receipts but also manage income and expenditures and provide relevant campaign information. The present invention aims to solve these problems and provide a system that is more convenient and useful for users.

[1065] 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.

[1066] In this invention, the server includes means for receiving transaction data acquired by a user device, means for storing the transaction data in a database, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information to the user device, means for displaying the value-added information on the user device, and means for providing campaign information based on the user's spending patterns and purchase details as the value-added information. This allows the user to not only manage transaction data but also efficiently obtain useful information based on their daily spending patterns.

[1067] definition statement

[1068] A "user device" is a communication device used by a user to make electronic payments and whose primary use includes capturing and storing transaction data and displaying value-added information.

[1069] "Transaction data" refers to information generated when an electronic payment is made, and includes primarily product name, price, purchase date and time, etc.

[1070] "Database" means a digital storage system for storing transaction data and generated value-added information.

[1071] A "generation module" is a software component that includes artificial intelligence for analyzing transaction data and generating value-added information.

[1072] "Value-added information" is information generated based on transaction data, and includes useful suggestions, advice, and special benefit information for the user.

[1073] "Campaign information" is promotional information such as benefits and discounts that are provided to users under certain conditions, and is intended to support users' purchasing activities.

[1074] A "generative AI model" is a machine learning model used to analyze transaction data and generate value-added information based on users' spending patterns and purchasing tendencies.

[1075] "Spending patterns" are data that indicate specific tendencies or behaviors based on a user's past purchasing history.

[1076] "Special offer information" refers to information including discounts, points, or other benefits offered to users to encourage them to make purchases.

[1077] MODE FOR CARRYING OUT THE INVENTION

[1078] The system of the present invention collects, stores, and analyzes electronic payment data of users, and provides value-added information based on the collected data. A specific embodiment of this system will be described below.

[1079] System configuration

[1080] This system mainly consists of the following three components:

[1081] 1. User Device

[1082] A communication device used by a user to make electronic payments, typically a smartphone, has an electronic payment application installed on it.

[1083] 2. Server

[1084] A server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[1085] 3. Database

[1086] A digital storage system for storing transaction data and generated value-added information.

[1087] Hardware and software used

[1088] Examples of specific hardware and software used in this system include:

[1089] Hardware: Smartphone

[1090] Software: Application frameworks (e.g., React Native), servers (e.g., AWS, Firebase), databases (e.g., MongoDB, Firebase Realtime Database), generative AI models (e.g., OpenAI's GPT-4)

[1091] Explanation of program processing

[1092] 1. Obtaining payment information

[1093] The electronic payment application on the user device automatically acquires transaction data (e.g., product name, price, purchase date and time) when payment is completed.

[1094] 2. Send receipt information

[1095] The acquired transaction data is encrypted and sent to a dedicated cloud server along with the user's unique ID.

[1096] 3. Save your receipt

[1097] The server verifies the integrity of the received transaction data and stores it in a database.

[1098] 4. Data analysis with generative AI

[1099] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information (e.g., recipe information, campaign information, and bonus information).

[1100] 5. Providing value-added information

[1101] The generated value-added information is associated with the user's account and transmitted to the user device.

[1102] 6. Display of Information

[1103] The user device displays the value-added information received from the server, and the user can open the app to view the proposed value-added information along with the electronic receipt.

[1104] Specific examples

[1105] For example, if a user uses an electronic payment app at a convenience store to purchase 1,000 yen worth of drinks and snacks, the following process occurs:

[1106] 1. Obtaining payment information

[1107] When payment is completed, the user device acquires the transaction data "Product: Beverage, Snack, Price: 1,000 yen, Date and Time: 2023-10-01."

[1108] 2. Send receipt information

[1109] This transaction data is encrypted and sent to the cloud server along with the user ID.

[1110] 3. Save your receipt

[1111] The server saves the data in the database as "User ID: 67890, Product: Drink, Snack, Price: 1,000 yen, Date and Time: 2023-10-01".

[1112] 4. Data analysis with generative AI

[1113] The generative AI model analyzes transaction data and generates campaign information for users, such as "a snack set perfect for watching a movie on the weekend."

[1114] 5. Providing value-added information

[1115] Campaign information "We recommend this snack for watching movies on the weekend! Enjoy it with a popular movie title" will be sent to users' smartphones.

[1116] 6. Display of Information

[1117] When a user opens the app, "Purchased items: drinks, snacks, price: 1,000 yen, date and time: 2023-10-01" along with "Recommended movie viewing information" will be displayed.

[1118] Prompt Sentence Examples

[1119] An example of a prompt is:

[1120] The user has purchased drinks and snacks. Please provide recommendations for how to enjoy the weekend.

[1121] In this way, the system according to the present invention effectively utilizes the user's expenditure data and provides useful information for daily life.

[1122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1123] Program processing

[1124] Explain the process step by step

[1125] Step 1:

[1126] A user makes a payment using an electronic payment app. At this moment, the user device automatically obtains transaction data (e.g., product name, price, purchase date and time). Specifically, the app detects the payment completion event and calls an API to obtain payment information.

[1127] (Input): Electronic payment completion information

[1128] (Output): Transaction data related to the payment (e.g. product name, price, purchase date and time)

[1129] Step 2:

[1130] The acquired transaction data is encrypted and sent to a cloud server along with the user's unique ID. The user device then uses an encryption algorithm to secure the data before sending it to the server via the internet.

[1131] (Input): Payment transaction data, user ID

[1132] (Output): Encrypted transaction data, user ID

[1133] Step 3:

[1134] The server verifies the integrity of the received transaction data and stores it in the database. The server first decrypts the data, then performs an integrity check, and then calls an API to store it in the database.

[1135] (Input): Encrypted transaction data, user ID

[1136] (Output): Transaction data stored in the database

[1137] Step 4:

[1138] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information. The server first retrieves the transaction data from the database, then generates appropriate prompts for the generative AI model and performs the analysis.

[1139] (Input): Transaction data in the database

[1140] (Output): Added-value information generated by the generative AI model (e.g., recipe information, campaign information, special offer information)

[1141] Step 5:

[1142] The generated value-added information is associated with the user's account and transmitted to the user device. The server then links the value-added information to the user ID and transmits the data to the user device using a notification function.

[1143] (Input): Generated value-added information, user ID

[1144] (Output): Value-added information sent to the user device

[1145] Step 6:

[1146] The user device displays the value-added information received from the server. When the user opens the electronic payment app, they can view the value-added information along with the latest transaction data. Specifically, the app processes the received data and displays it on the user interface.

[1147] (Input): Value-added information received from the server

[1148] (Output): Value-added information displayed within the app (e.g., recipes, campaign information)

[1149] Through the above steps, a system is realized that allows users to effectively use electronic payment data and obtain useful added-value information.

[1150] 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.

[1151] The present invention relates to a system that uses transaction data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information, and further combines this with an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[1152] System configuration

[1153] The system includes the following major components:

[1154] 1. User Device:

[1155] A device such as a smartphone or tablet on which users make transactions.

[1156] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[1157] 2. Server:

[1158] It has the function of receiving transaction data and storing it in a database.

[1159] It includes analytical functions such as emotion engines and generation modules.

[1160] 3. Database:

[1161] Data storage for storing transaction data and generated value-added information.

[1162] 4. Emotion Engine:

[1163] It has the ability to analyze facial expression data and voice data sent from the user device and recognize the user's emotions.

[1164] 5. Generation module:

[1165] Transaction data and perceived user sentiment are analyzed and value-added information is generated based thereon.

[1166] Program processing flow

[1167] 1. Obtaining payment information

[1168] Device:

[1169] When a user makes a payment using an electronic payment app, the terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also acquires the user's emotional data (facial expressions and voice).

[1170] 2. Send receipt information

[1171] Device:

[1172] Transaction data and emotion data are encrypted and sent to the server along with the user's unique account ID.

[1173] 3. Save your receipt

[1174] server:

[1175] The consistency of the transaction data and sentiment data is checked and stored in the database.

[1176] 4. Emotion Data Analysis

[1177] server:

[1178] The emotion engine analyzes the user's emotional data and recognizes their current emotional state, for example, by identifying emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[1179] 5. Comprehensive data analysis

[1180] server:

[1181] The generation module analyzes the transaction data and the recognized emotion data, thereby generating the optimal value-added information that matches the user's emotions.

[1182] 6. Providing value-added information

[1183] server:

[1184] The generated value-added information (e.g., recipes, promotional information) is transmitted to the user device.

[1185] 7. Display of Information

[1186] Device:

[1187] The user device displays the received value-added information within the application.

[1188] Specific examples

[1189] For example, consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. When paying, the emotion engine uses a camera to detect the user's facial expressions and analyzes their voice to determine that the user is busy but happy.

[1190] 1. Obtaining payment information:

[1191] Terminal: Obtain transaction data "Product: Chicken, onion, potato, Price: 2000 yen, Date and time: 2023-10-01" and emotion data.

[1192] 2. Sending receipt information:

[1193] Terminal: Transaction data and emotional data are encrypted and sent to the server.

[1194] 3. Save your receipt:

[1195] Server: Stored in the database.

[1196] 4. Emotional Data Analysis:

[1197] Server: Emotional state analyzed as "busy but happy."

[1198] 5. Comprehensive data analysis:

[1199] Server: Based on the user's emotional state, the server suggests a simple and healthy recipe for "Easy Chicken Curry."

[1200] 6. Providing Value-Added Information:

[1201] Server: Sends suggested recipe information to the user device.

[1202] 7. Displaying Information:

[1203] Device: The user opens the app and sees the recipe for "Easy Chicken Curry" along with their digital receipt.

[1204] The present invention allows users to obtain more personalized added-value information, and improves the quality of their daily lives by suggesting optimal recipes that match their mood on that day, for example.

[1205] The processing flow will be explained below.

[1206] Step 1:

[1207] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code or other data using a user device such as a smartphone or tablet, and the payment screen is displayed. At this time, the camera and microphone are activated to recognize the user's emotions.

[1208] Step 2:

[1209] Terminal: The user's device communicates with the store's payment system and acquires transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction. At the same time, the camera and microphone are used to acquire facial expression and voice data from the user.

[1210] Step 3:

[1211] Terminal: Once the payment is completed, the terminal encrypts the transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") and emotion data and sends them to the server along with the user's unique account ID.

[1212] Step 4:

[1213] Server: The server checks the integrity of the transaction data and emotion data it receives, verifies that the data is accurate, and stores the transaction data and emotion data in the database after confirming that there is no fraud or tampering.

[1214] Step 5:

[1215] Server: When storing transaction data and emotion data in the database, information such as user ID, purchased item, price, transaction date and time, and emotional state is associated and recorded. The database is secured to a certain extent.

[1216] Step 6:

[1217] Server: The emotion engine starts working based on the stored transaction data and emotion data. The emotion engine analyzes the user's facial and voice data to identify the user's current emotional state. For example, it identifies emotions such as "happy" from facial expressions and "busy" from voice tone.

[1218] Step 7:

[1219] Server: The generation module analyzes the emotional state analyzed by the emotion engine and combines it with transaction data. This analysis generates optimal value-added information tailored to the user's emotions. For example, it suggests easy-to-make healthy recipes for an emotional state such as "busy but happy."

[1220] Step 8:

[1221] Server: Sends the generated value-added information (e.g., a simple curry recipe using "chicken, onion, and potato") to the user device. The information is converted into an appropriate format so that the user can easily understand it.

[1222] Step 9:

[1223] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[1224] Step 10:

[1225] User: The user opens the application and checks the latest transaction information (electronic receipt) and suggested value-added information (e.g. curry recipe). The user receives the best suggestions tailored to their mood, improving the quality of their daily life.

[1226] This allows the system to provide added-value information that takes user emotions into account, significantly improving the convenience of electronic receipts. Furthermore, users can enjoy a more satisfying shopping experience by receiving suggestions that perfectly match their emotions.

[1227] Example 2

[1228] 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."

[1229] In modern electronic commerce, it is important to understand not only a user's purchasing data but also their emotional state at the time to provide more personalized, value-added information. However, conventional systems are limited to collecting and analyzing transaction data and are unable to generate value-added information that takes user emotions into account. This makes it difficult to provide information that meets individual user needs, limiting improvements to the user experience. Furthermore, providing personalized information through emotion analysis is difficult to achieve due to the increased complexity of the analysis.

[1230] 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.

[1231] In this invention, the server includes a means for receiving transaction data and emotion data acquired by a user device, a means for storing the transaction data and emotion data in a database, and a generation module for analyzing the transaction data and emotion data to generate value-added information. This makes it possible to generate and provide more personalized value-added information based on the user's transaction data and associated emotion data. Specifically, the generation module comprehensively analyzes the transaction data and emotion data and provides optimal recipe information, promotion information, and the like based on the user's emotional state, thereby improving the user experience.

[1232] A "user device" is a hardware device used by a user to conduct a transaction, and includes devices such as smartphones and tablets.

[1233] "Transaction data" refers to information relating to transactions such as purchases made by users, and includes details such as product name, price, and transaction date and time.

[1234] "Emotion data" is data that expresses the user's emotional state, and includes facial expression data and voice data from camera images.

[1235] "Database" refers to a data storage system for systematically storing transaction data and emotion data, including relational database management systems (RDBMS).

[1236] The "generation module" is a software module that analyzes the received transaction data and emotion data and generates optimal value-added information for the user.

[1237] The "emotion engine" is an analysis engine that has the function of analyzing emotion data sent from a user device and recognizing the user's emotional state.

[1238] "Value-added information" refers to additional suggestions and information for users that is generated based on the analysis of transaction data and emotion data, and includes recipes, promotional information, and the like.

[1239] This invention relates to a system that uses transaction data and emotion data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information. To implement this system, the following main components are required:

[1240] System configuration

[1241] The system includes the following major components:

[1242] 1. User Device:

[1243] These devices, which include smartphones, tablets, etc., are used by users to conduct transactions. These devices have an electronic payment application installed and are equipped with the functionality to capture user transaction data and emotional data using sensors such as cameras and microphones.

[1244] 2. Server:

[1245] It has the function of receiving transaction data and emotion data and storing them in a database. It also includes analysis functions such as an emotion engine and generation module.

[1246] 3. Database:

[1247] Data storage for storing transaction data and generated value-added information, specifically using a relational database such as MySQL.

[1248] 4. Emotion Engine:

[1249] It has the ability to analyze facial expression and voice data sent from the user's device using a deep learning algorithm and recognize the user's emotions.

[1250] 5. Generation module:

[1251] A software module for analyzing transaction data and recognized user emotion data and generating value-added information based on the analysis.

[1252] Program processing flow

[1253] When a user makes a transaction, the user device acquires transaction data and emotion data. The acquired data is encrypted using encryption technology such as AES and sent to the server via HTTPS protocol. The server checks the integrity of the data, stores it in a database, and then analyzes the emotion data using an emotion engine. The analysis results are integrated with the transaction data, and a generation module generates optimal value-added information (e.g., recipes or promotion information). This information is sent to the user device and displayed within the electronic payment application.

[1254] Specific examples

[1255] Consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. At this time, the camera on the user device captures the user's facial expression and the microphone records the user's voice when making the payment. The transaction data obtained is "Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" and emotion data "Smile, Happiness."

[1256] The encrypted data is then sent to the server along with the user's account ID. The server verifies the data's integrity and stores it in a database. The emotion engine detects the "feeling of happiness," and the generation module generates a recipe for "easy chicken curry" based on this information and sends it to the user's device. The user can then review the suggested recipe within the electronic payment application and begin cooking.

[1257] Prompt Sentence Examples

[1258] By inputting the following prompt sentences into the generative AI model, it is expected that the system's processing flow will be explained in detail.

[1259] You have invented a system that uses user transaction data and emotional data to store electronic receipts and generate value-added information. As a concrete example, let's show the process flow when a user buys chicken, onions, and potatoes at a supermarket. At the time of payment, the system analyzes the user's facial expressions and voice and recognizes that the user is busy but happy. Based on this information, the system suggests a simple and healthy recipe for "Easy Chicken Curry." Based on the above, please explain the system's process flow in detail.

[1260] This invention allows users to obtain personalized value-added information and suggests optimal recipes that match their mood on that day, thereby improving the quality of their daily lives.

[1261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1262] Step 1: Get your payment information

[1263] Terminal: When a user makes a payment using an electronic payment app, the terminal acquires transaction data (e.g., purchased item, price, transaction date and time, etc.). The terminal captures the user's facial expression with a camera and records the user's voice with a microphone. These data are stored as transaction data and emotion data.

[1264] Input: User transactions, camera images, audio

[1265] Output: Transaction data, sentiment data

[1266] What it does: It runs a facial recognition algorithm on the smartphone camera to extract facial expression data, and uses a speech recognition algorithm to analyze emotions from recorded speech.

[1267] Step 2: Send receipt information

[1268] Terminal: The acquired transaction data and emotion data are encrypted using AES (Advanced Encryption Standard) encryption technology and sent to the server along with the user's unique account ID. The transmission method uses the HTTPS protocol to ensure secure data transfer.

[1269] Input: Transaction data, emotion data, account ID

[1270] Output: Encrypted transaction data and sentiment data

[1271] Specific operation: The data encryption module encrypts the transaction data and emotion data using the AES method and sends them to the server using the HTTPS protocol.

[1272] Step 3: Save your receipt

[1273] Server: The server checks the consistency of the received transaction data and emotion data. A hash function is used to check consistency and ensure the data has not been tampered with. After checking, the data is stored in a database. A relational database such as MySQL is used as the database.

[1274] Input: Encrypted transaction data and sentiment data

[1275] Output: Integrity-checked transaction data and sentiment data

[1276] Specific operation: The server checks the integrity of the data using a hash function such as SHA-256, and then stores the data in the MySQL database.

[1277] Step 4: Analyze the sentiment data

[1278] Server: The server uses an emotion engine to analyze the user's emotional data. Specifically, it analyzes image data acquired from the camera using a deep learning model to identify emotions from facial expressions. At the same time, it analyzes audio data using voice recognition technology to determine emotions from the tone and intonation of the voice.

[1279] Input: Emotion data with integrity check

[1280] Output: Parsed emotional state

[1281] How it works: Facial expression data is analyzed using a deep learning model (e.g., a convolutional neural network) to identify emotions. Similarly, voice data is analyzed using a recurrent neural network.

[1282] Step 5: Comprehensive data analysis

[1283] Server: The server passes transaction data and recognized emotion data to the generation module. The generation module uses machine learning algorithms to generate value-added information based on the user's emotional state. For example, if the server recognizes that the user is in a happy state, it will suggest simple recipes that will increase happiness.

[1284] Input: Transaction data, analyzed emotional state

[1285] Output: Generated value-added information

[1286] Specific operation: The generation module uses a machine learning algorithm (e.g., Random Forest or Gradient Boosting Machine) to analyze the input data and generate value-added information.

[1287] Step 6: Provide value-added information

[1288] Server: Sends the generated value-added information to the user device. This transmission also uses the HTTPS protocol to ensure data security. The information includes recipes, promotional information, etc.

[1289] Input: Generated value-added information

[1290] Output: Value-added information sent to the user device

[1291] Specific operation: The generated information is sent to the user device using the HTTPS protocol.

[1292] Step 7: Viewing information

[1293] Terminal: The user device displays the received value-added information in the electronic payment application. For example, a suggested recipe is displayed along with an electronic receipt. The user can open the app and check the detailed recipe.

[1294] Input: Value-added information sent to the user device

[1295] Output: Value-added information displayed within the application

[1296] Specific behavior: The application displays the received information in an appropriate format so that the user can view it.

[1297] Through the above processing steps, the system generates personalized value-added information based on the user's transaction data and emotion data, improving the user experience.

[1298] (Application example 2)

[1299] 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."

[1300] While modern electronic payment services exist that provide value-added information based on transaction data acquired by users, they are unable to provide personalized information using user emotional data. As a result, it is difficult to provide optimal advice and promotions that correspond to the user's specific situation and emotions, and this has prevented them from improving user satisfaction and strengthening loyalty.

[1301] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving transaction data and emotion data acquired by a user terminal, means for storing the transaction data and emotion data in a database, a generation engine for analyzing the transaction data and emotion data to generate value-added information, means for transmitting the value-added information to the user terminal, and means for displaying the value-added information on the user terminal. This makes it possible to provide personalized promotions and information that correspond to the user's emotional state.

[1302] "User terminal" refers to a device used by a user to conduct transactions, and specifically includes smartphones, tablets, etc.

[1303] "Transaction data" refers to information about transactions conducted by users, including product information, price information, transaction date and time, etc.

[1304] "Emotion data" refers to data that indicates the user's emotional state, and mainly includes facial expression data captured by a camera and voice data captured by a microphone.

[1305] "Database" refers to a storage device for storing acquired transaction data and emotion data.

[1306] "Generation engine" refers to a module that analyzes transaction data and emotion data and generates value-added information based on the analysis.

[1307] "Value-added information" refers to information generated based on transaction data and emotion data, and specifically includes promotion information and recipe information.

[1308] This invention is a system that uses transaction data acquired by a user terminal to store electronic receipts, analyzes the data, and generates value-added information. It also combines an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[1309] System configuration

[1310] 1. User Device:

[1311] A terminal such as a smartphone or tablet on which a user makes a transaction.

[1312] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[1313] 2. Server:

[1314] It has the function of receiving transaction data and emotion data and storing it in a database.

[1315] Includes analytical functions such as an emotion engine and a generation engine.

[1316] 3. Database:

[1317] This is a storage device for storing transaction data and emotion data.

[1318] 4. Emotion Engine:

[1319] It has the ability to analyze facial expression and voice data sent from the user's device and recognize the user's emotions.

[1320] 5. Generation engine:

[1321] This module analyzes transaction data and recognized emotion data and generates value-added information based on the analysis.

[1322] Explanation of program processing

[1323] Obtaining payment information

[1324] When a user makes a payment using an electronic payment application, the user terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also uses a camera and microphone to simultaneously acquire the user's emotional data (e.g., facial expressions and voice).

[1325] Sending receipt information

[1326] The user device encrypts the acquired transaction data and emotion data and transmits them to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[1327] Saving receipts

[1328] The server checks the integrity of the received transaction data and sentiment data and stores it in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL) to ensure consistency and durability.

[1329] Emotional Data Analysis

[1330] The emotion engine in the server analyzes the transmitted facial expression and voice data to recognize the user's emotional state. For example, it can identify emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[1331] Comprehensive analysis of data

[1332] The generation engine in the server integrates and analyzes the transaction data and the recognized emotion data, thereby generating optimal value-added information (e.g., promotional information) tailored to the user's emotions.

[1333] Providing added-value information

[1334] The generated value-added information is again transmitted to the user terminal and displayed within the application.

[1335] Adding specific examples

[1336] For example, consider the case where a user purchases a coffee at a cafe using electronic payment. At the time of payment, the emotion engine uses the camera and microphone to recognize that the user is feeling stressed. In this case, the following occurs:

[1337] 1. Obtaining payment information:

[1338] The user device acquires the transaction data "Product: Coffee, Price: 500 yen, Date and Time: 2023-10-20" and the emotion data "Stress."

[1339] 2. Sending receipt information:

[1340] The user device encrypts transaction data and emotion data and sends them to the server.

[1341] 3. Save your receipt:

[1342] The server stores the data in a database.

[1343] 4. Emotional Data Analysis:

[1344] The server analyzes the emotional state as "stress."

[1345] 5. Comprehensive data analysis:

[1346] The server generates promotions for relaxation products based on the user's emotional state, offering coupons for "aromatherapy oils for stress relief."

[1347] 6. Providing Value-Added Information:

[1348] The proposed promotion information is sent to the user terminal.

[1349] 7. Displaying Information:

[1350] The user opens the app and sees the "aromatherapy oil" coupon along with the electronic receipt.

[1351] Example prompt sentence:

[1352] Prompt for the generative AI model: "When a user is in a cafe and buying coffee while feeling stressed, please generate promotional information for relaxation products to help reduce stress."

[1353] This prompt allows the model to generate appropriate value-added information based on the user's emotional state and transaction data.

[1354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1355] Step 1: Get your payment information

[1356] Description: When a user makes a payment using an electronic payment application, the terminal captures transaction data (item purchased, price, transaction date and time, etc.). In parallel, the emotion engine on the terminal uses the camera and microphone to capture the user's emotion data (facial expressions and voice). Specifically, the camera captures the user's facial expressions, and the microphone records the tone and volume of the voice.

[1357] Input: User purchase operation

[1358] Output: Transaction data, sentiment data

[1359] Step 2: Send receipt information

[1360] Description: The terminal encrypts the acquired transaction data and emotion data with AES encryption and sends it to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[1361] Input: Transaction data, sentiment data

[1362] Output: Encrypted data packet

[1363] Step 3: Save your receipt

[1364] Description: The server decrypts the received transaction data and sentiment data and checks the integrity and consistency of the data. It then stores this data in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL).

[1365] Input: Encrypted data packet

[1366] Output: Transaction data and sentiment data in the database

[1367] Step 4: Analyze the sentiment data

[1368] Description: The emotion engine in the server analyzes facial expression and voice data stored in the database to identify the user's emotional state. For example, it uses machine learning models (e.g., CNN or RNN) to determine whether the user is feeling "stressed" or "happy" based on facial expressions captured by a camera and tone of voice recorded by a microphone.

[1369] Input: Emotion data in the database

[1370] Output: User's emotional state

[1371] Step 5: Comprehensive data analysis

[1372] Description: The generation engine on the server integrates and analyzes transaction data and recognized emotional data. Specifically, it combines transaction data (e.g., type and price of purchased product) with emotional data (e.g., stress level) to generate value-added information (promotion information, recipe information) that is optimal for the user. In this process, a generative AI model is used to generate value-added information that meets the user's needs.

[1373] Input: Transaction data, user emotional state

[1374] Output: Value-added information

[1375] Step 6: Provide value-added information

[1376] Description: The generated value-added information is re-encrypted and sent to the terminal along with the user's unique account ID. The transmission process again uses a secure communication protocol (e.g., HTTPS).

[1377] Input: Value-added information

[1378] Output: Encrypted value-added information data packet

[1379] Step 7: Viewing information

[1380] Description: The terminal decodes the received value-added information and displays it within the electronic payment application. Specifically, promotional information and recipe information are displayed along with the electronic receipt. For example, if a user purchases coffee at a cafe and the emotion engine recognizes stress, a coupon for "aromatherapy oil for stress reduction" is displayed.

[1381] Input: Encrypted value-added information data packet

[1382] Output: Value-added information displayed to the user

[1383] 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.

[1384] 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.

[1385] 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.

[1386] [Fourth embodiment]

[1387] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1388] 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.

[1389] 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).

[1390] 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.

[1391] 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.

[1392] 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).

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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."

[1400] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. Specific embodiments of this system and the program processing are described below in natural language.

[1401] System configuration

[1402] This system mainly consists of the following three components:

[1403] 1. User Device:

[1404] A device used by a user to make electronic payments, typically a smartphone or tablet, has an electronic payment application (e.g., an electronic money app) installed on it.

[1405] 2. Server:

[1406] This is a server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[1407] 3. Database:

[1408] A data storage for storing transaction data and generated value-added information.

[1409] Program processing flow

[1410] 1. Obtaining payment information

[1411] Device:

[1412] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment (e.g., the product purchased, the price, and the date and time of the transaction).

[1413] 2. Send receipt information

[1414] Device:

[1415] Once the payment is complete, the terminal encrypts the transaction data it receives and sends it to the server along with the user's unique account ID.

[1416] 3. Save your receipt

[1417] server:

[1418] The server checks the integrity of the received transaction data and stores it in a database, including the user ID, purchased item, price, transaction date and time, etc.

[1419] 4. Data analysis with generative AI

[1420] server:

[1421] The server passes the stored transaction data to a generation AI, which analyzes the data and generates value-added information (such as recipe suggestions) based on the user's purchasing patterns and product information.

[1422] 5. Providing value-added information

[1423] server:

[1424] The generated value-added information is associated with the user's account and transmitted to the user device.

[1425] 6. Display of Information

[1426] Device:

[1427] The user device displays the value-added information received from the server, and when the user opens the app, they can see the proposed value-added information along with the electronic receipt.

[1428] Specific examples

[1429] For example, suppose a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes).

[1430] 1. Obtaining payment information

[1431] Terminal: Once payment is completed, the transaction data "Items: Chicken, Onion, Potato, Price: 2,000 yen, Date and Time: 2023-10-01" is obtained.

[1432] 2. Send receipt information

[1433] Terminal: The transaction data is encrypted and sent to the server along with the user ID.

[1434] 3. Save your receipt

[1435] Server: Save "User ID: 12345, Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" in the database.

[1436] 4. Data analysis with generative AI

[1437] Server: The generative AI analyzes the transaction data and generates a curry recipe using "chicken, onion, and potato."

[1438] 5. Providing value-added information

[1439] Server: Sends the generated curry recipe information to the user device.

[1440] 6. Display of Information

[1441] Device: When the user opens the app, they can see the "Chicken Curry Recipe" along with "Purchased Items: Chicken, Onion, Potato, Price: 2,000 yen, Date: 2023-10-01."

[1442] This invention saves users the trouble of managing paper receipts and allows them to receive useful information based on transaction data. This improves the convenience of electronic receipts and is expected to encourage more users to use electronic receipts.

[1443] The processing flow will be explained below.

[1444] Step 1:

[1445] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code or other data using a user device such as a smartphone or tablet, and the payment screen is displayed.

[1446] Step 2:

[1447] Terminal: The user device communicates with the store's payment system to obtain transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction.

[1448] Step 3:

[1449] Terminal: After the payment is completed, the obtained transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") is encrypted and sent to the server along with the user's unique account ID.

[1450] Step 4:

[1451] Server: The server checks the integrity of the transaction data it receives, verifies that the data is accurate, and stores the transaction data in the database after verifying that there is no fraud or tampering.

[1452] Step 5:

[1453] Server: When transaction data is saved in a database, it is recorded in association with information such as the user ID, purchased item, price, transaction date and time, etc. The database is secured to a certain extent.

[1454] Step 6:

[1455] Server: The generation AI starts working based on the stored transaction data. The generation AI analyzes the data and identifies the user's purchasing patterns and characteristics.

[1456] Step 7:

[1457] Server: The generation AI analyzes the transaction data and generates value-added information (e.g., recipes, promotional information). Specifically, it can suggest a "curry recipe" using "chicken, onions, and potatoes."

[1458] Step 8:

[1459] Server: Sends the generated value-added information to the user device, where it is converted into an appropriate format so that it can be easily understood by the user.

[1460] Step 9:

[1461] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[1462] Step 10:

[1463] User: The user opens the application and sees the latest transaction information (e-receipt) and suggested value-added information (e.g. curry recipe), which improves the user's life.

[1464] Through the above steps, the system of the present invention can acquire, store, and analyze electronic receipts, and provide added-value information in a single flow.

[1465] Example 1

[1466] 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."

[1467] With the spread of modern electronic payments, users are increasingly burdened with managing paper receipts. While systems exist that provide value-added information based on purchase data, there is a need for an automated, secure method for acquiring and providing this information that does not require user interaction. Therefore, a system is needed that integrates the encrypted transmission of purchase data, secure storage in a database, and the automatic generation and provision of value-added information using a generative AI model.

[1468] 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.

[1469] In this invention, the server includes means for receiving transaction data acquired by a user device, means for encrypting and transmitting the transaction data, means for storing the transaction data in a database, means for verifying the integrity of the stored transaction data, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information generated by the generation module to the user device, and means for displaying the value-added information on the user device. This eliminates the need for users to manage paper receipts, and further enables the system to automatically and safely acquire and store transaction data and provide useful value-added information based on it.

[1470] A "user device" is a device used by a user to make electronic payments, and typically includes a smartphone or tablet.

[1471] "Transaction data" is information generated when a user makes an electronic payment, and includes the name of the purchased product, its price, the date and time of the transaction, and the like.

[1472] "Encryption" is the process of converting transaction data using a specific algorithm to protect it from unauthorized access by third parties, ensuring that only those with the key can access the original data.

[1473] "Database" means data storage for the secure long-term preservation of transaction data and generated value-added information.

[1474] "Integrity verification" is the process of checking received transaction data for errors or unauthorized changes to ensure the authenticity of the data.

[1475] The term "generation module" refers to a program and its execution environment for analyzing transaction data and automatically generating value-added information useful to users.

[1476] "Added-value information" is information that is generated based on transaction data and that the user finds useful, and includes, for example, recipe information and product recommendation information.

[1477] A "generative AI model" is a machine learning model that has the ability to generate text data based on specific input data, and is used to generate recipe information and value-added information based on user transaction data.

[1478] "Transmission means" refers to the communication protocol and its execution environment used to safely and reliably send specific data to another device or server.

[1479] "Displaying means" refers to software and its interface for visually presenting information on the screen of a user device.

[1480] The system of the present invention automatically acquires and stores electronic receipts when a user makes an electronic payment, analyzes the information, and provides value-added information. This system is mainly composed of a user device, a server, and a database.

[1481] System configuration

[1482] 1. User Device:

[1483] A device used by a user to make electronic payments. This device includes smartphones and tablets. An electronic payment application (e.g., an e-money app) is installed on the user device. When a user makes a purchase, payment is made using this application.

[1484] 2. Server:

[1485] The server receives transaction data and stores it in a database. It also analyzes the stored data and generates value-added information. A generative AI model is used for this analysis. The server checks the integrity of the data and ensures that the transaction data is accurate.

[1486] 3. Database:

[1487] The database is a data storage for saving transaction data and generated value-added information, allowing users' transaction history and generated value-added information to be maintained for a long period of time.

[1488] Program processing

[1489] When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. This data includes the name of the purchased item, its price, the date and time of the transaction, etc. This transaction data is stored in a temporary file. Once the transaction is complete, the terminal encrypts this data and sends it to the server along with the user's unique account ID. The transmitted data is protected using encryption algorithms such as AES encryption and is sent via the HTTPS protocol.

[1490] The server verifies the integrity of the received transaction data. Specifically, it performs a data integrity check (e.g., checksum verification) to ensure that no unauthorized changes have been made. Once the integrity of the data is confirmed, information such as the user ID, purchased item, price, and transaction date and time is saved in a database. The saved transaction data is passed to a generative AI model. This generative AI model analyzes the transaction data and generates value-added information useful to the user (e.g., recipe information and recommended products).

[1491] The generated value-added information is converted back to JSON format, linked to the user ID, and sent to the user device via HTTPS. The user device displays the received value-added information within the application. When the user opens the app, they can see the proposed value-added information along with the products they purchased.

[1492] Specific examples

[1493] For example, if a user uses an electronic payment app at a supermarket to purchase 2,000 yen worth of ingredients (chicken, onions, and potatoes), an example of transaction data would be as follows:

[1494] "Item: Chicken, onion, potato, Price: 2000 yen, Date: 2023-10-01"

[1495] This data is sent to a server and analyzed by a generative AI model, which then suggests a curry recipe using "chicken, onions, and potatoes." An example prompt is as follows:

[1496] Based on the purchase data of "chicken, onion, potato," the user is prompted to "suggest a recipe using these ingredients."

[1497] In this way, users can easily obtain useful information based on their purchase history, eliminating the need to manage paper receipts and allowing them to receive useful information based on transaction data.

[1498] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1499] System configuration

[1500] This system mainly consists of the following three components:

[1501] 1. User device: A device used by a user to make electronic payments, typically a smartphone or tablet, on which an electronic payment application (e.g., an e-money app) is installed.

[1502] 2. Server: This server receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[1503] 3. Database: Data storage for storing transaction data and generated value-added information.

[1504] Program processing flow

[1505] Step 1: Get your payment information

[1506] Terminal: When a user makes a payment using an electronic payment app, the terminal obtains transaction data related to the payment. Specifically, the application records transaction data such as the name of the purchased item, its price, and the transaction date and time. This data is saved in a temporary storage folder. The input is the item purchased by the user and its details, and the output is temporary storage of transaction data.

[1507] Step 2: Send receipt information

[1508] Terminal: Once the payment is completed, the terminal encrypts and transmits the acquired transaction data. Specifically, it encrypts the transaction data using the AES encryption algorithm and sends the encrypted data and the user's account ID to the server. The transmission uses the HTTPS protocol. The input is the temporarily stored transaction data, and the output is the encrypted data and the account ID sent to the server.

[1509] Step 3: Save your receipt

[1510] Server: The server checks the integrity of the received transaction data. Specifically, it verifies the integrity of the data using a checksum. Once the integrity is confirmed, the data is stored in a database. The stored data includes the user ID, purchased item, price, transaction date and time, etc. The input is the encrypted transaction data and account ID, and the output is the data stored in the database after integrity is confirmed.

[1511] Step 4: Data analysis with generative AI

[1512] Server: The server passes the stored transaction data to the generative AI model and analyzes the data. Specifically, the transaction data is converted into JSON format and input into the generative AI model. The generative AI model generates value-added information (e.g., recipe suggestions) based on the user's purchasing patterns and product information. The input is the transaction data stored in the database, and the output is the generated value-added information.

[1513] Step 5: Provide added value information

[1514] Server: Associates the generated value-added information with the user's account and sends it to the user device. Specifically, the generated information is converted back to JSON format, linked to the user ID, and sent to the user device via the HTTPS protocol. The input is the generated value-added information, and the output is the transmission to the user device.

[1515] Step 6: Viewing information

[1516] Terminal: The user device displays the value-added information received from the server. Specifically, the electronic payment app parses the received data and displays it on the user interface. When the user opens the app, they can see the proposed value-added information along with the electronic receipt. The input is the value-added information received from the server, and the output is the information displayed on the user interface.

[1517] (Application example 1)

[1518] 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."

[1519] With the spread of electronic payments, there is a need for efficient management of transaction data acquired daily by users and for utilizing that data to provide users with useful information. However, current systems are limited to simple recording of transaction data and do not adequately provide value-added information based on users' spending patterns. Furthermore, there is a need for systems that not only manage electronic receipts but also manage income and expenditures and provide relevant campaign information. The present invention aims to solve these problems and provide a system that is more convenient and useful for users.

[1520] 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.

[1521] In this invention, the server includes means for receiving transaction data acquired by a user device, means for storing the transaction data in a database, a generation module for analyzing the transaction data and generating value-added information, means for transmitting the value-added information to the user device, means for displaying the value-added information on the user device, and means for providing campaign information based on the user's spending patterns and purchase details as the value-added information. This allows the user to not only manage transaction data but also efficiently obtain useful information based on their daily spending patterns.

[1522] definition statement

[1523] A "user device" is a communication device used by a user to make electronic payments and whose primary use includes capturing and storing transaction data and displaying value-added information.

[1524] "Transaction data" refers to information generated when an electronic payment is made, and includes primarily product name, price, purchase date and time, etc.

[1525] "Database" means a digital storage system for storing transaction data and generated value-added information.

[1526] A "generation module" is a software component that includes artificial intelligence for analyzing transaction data and generating value-added information.

[1527] "Value-added information" is information generated based on transaction data, and includes useful suggestions, advice, and special benefit information for the user.

[1528] "Campaign information" is promotional information such as benefits and discounts that are provided to users under certain conditions, and is intended to support users' purchasing activities.

[1529] A "generative AI model" is a machine learning model used to analyze transaction data and generate value-added information based on users' spending patterns and purchasing tendencies.

[1530] "Spending patterns" are data that indicate specific tendencies or behaviors based on a user's past purchasing history.

[1531] "Special offer information" refers to information including discounts, points, or other benefits offered to users to encourage them to make purchases.

[1532] MODE FOR CARRYING OUT THE INVENTION

[1533] The system of the present invention collects, stores, and analyzes electronic payment data of users, and provides value-added information based on the collected data. A specific embodiment of this system will be described below.

[1534] System configuration

[1535] This system mainly consists of the following three components:

[1536] 1. User Device

[1537] A communication device used by a user to make electronic payments, typically a smartphone, has an electronic payment application installed on it.

[1538] 2. Server

[1539] A server that receives transaction data and stores it in a database. It also has the function of analyzing the stored data and generating value-added information.

[1540] 3. Database

[1541] A digital storage system for storing transaction data and generated value-added information.

[1542] Hardware and software used

[1543] Examples of specific hardware and software used in this system include:

[1544] Hardware: Smartphone

[1545] Software: Application frameworks (e.g., React Native), servers (e.g., AWS, Firebase), databases (e.g., MongoDB, Firebase Realtime Database), generative AI models (e.g., OpenAI's GPT-4)

[1546] Explanation of program processing

[1547] 1. Obtaining payment information

[1548] The electronic payment application on the user device automatically acquires transaction data (e.g., product name, price, purchase date and time) when payment is completed.

[1549] 2. Send receipt information

[1550] The acquired transaction data is encrypted and sent to a dedicated cloud server along with the user's unique ID.

[1551] 3. Save your receipt

[1552] The server verifies the integrity of the received transaction data and stores it in a database.

[1553] 4. Data analysis with generative AI

[1554] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information (e.g., recipe information, campaign information, and bonus information).

[1555] 5. Providing value-added information

[1556] The generated value-added information is associated with the user's account and transmitted to the user device.

[1557] 6. Display of Information

[1558] The user device displays the value-added information received from the server, and the user can open the app to view the proposed value-added information along with the electronic receipt.

[1559] Specific examples

[1560] For example, if a user uses an electronic payment app at a convenience store to purchase 1,000 yen worth of drinks and snacks, the following process occurs:

[1561] 1. Obtaining payment information

[1562] When payment is completed, the user device acquires the transaction data "Product: Beverage, Snack, Price: 1,000 yen, Date and Time: 2023-10-01."

[1563] 2. Send receipt information

[1564] This transaction data is encrypted and sent to the cloud server along with the user ID.

[1565] 3. Save your receipt

[1566] The server saves the data in the database as "User ID: 67890, Product: Drink, Snack, Price: 1,000 yen, Date and Time: 2023-10-01".

[1567] 4. Data analysis with generative AI

[1568] The generative AI model analyzes transaction data and generates campaign information for users, such as "a snack set perfect for watching a movie on the weekend."

[1569] 5. Providing value-added information

[1570] Campaign information "We recommend this snack for watching movies on the weekend! Enjoy it with a popular movie title" will be sent to users' smartphones.

[1571] 6. Display of Information

[1572] When a user opens the app, "Purchased items: drinks, snacks, price: 1,000 yen, date and time: 2023-10-01" along with "Recommended movie viewing information" will be displayed.

[1573] Prompt Sentence Examples

[1574] An example of a prompt is:

[1575] The user has purchased drinks and snacks. Please provide recommendations for how to enjoy the weekend.

[1576] In this way, the system according to the present invention effectively utilizes the user's expenditure data and provides useful information for daily life.

[1577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1578] Program processing

[1579] Explain the process step by step

[1580] Step 1:

[1581] A user makes a payment using an electronic payment app. At this moment, the user device automatically obtains transaction data (e.g., product name, price, purchase date and time). Specifically, the app detects the payment completion event and calls an API to obtain payment information.

[1582] (Input): Electronic payment completion information

[1583] (Output): Transaction data related to the payment (e.g. product name, price, purchase date and time)

[1584] Step 2:

[1585] The acquired transaction data is encrypted and sent to a cloud server along with the user's unique ID. The user device then uses an encryption algorithm to secure the data before sending it to the server via the internet.

[1586] (Input): Payment transaction data, user ID

[1587] (Output): Encrypted transaction data, user ID

[1588] Step 3:

[1589] The server verifies the integrity of the received transaction data and stores it in the database. The server first decrypts the data, then performs an integrity check, and then calls an API to store it in the database.

[1590] (Input): Encrypted transaction data, user ID

[1591] (Output): Transaction data stored in the database

[1592] Step 4:

[1593] The server passes the transaction data stored in the database to the generative AI model, which analyzes the data and generates value-added information. The server first retrieves the transaction data from the database, then generates appropriate prompts for the generative AI model and performs the analysis.

[1594] (Input): Transaction data in the database

[1595] (Output): Added-value information generated by the generative AI model (e.g., recipe information, campaign information, special offer information)

[1596] Step 5:

[1597] The generated value-added information is associated with the user's account and transmitted to the user device. The server then links the value-added information to the user ID and transmits the data to the user device using a notification function.

[1598] (Input): Generated value-added information, user ID

[1599] (Output): Value-added information sent to the user device

[1600] Step 6:

[1601] The user device displays the value-added information received from the server. When the user opens the electronic payment app, they can view the value-added information along with the latest transaction data. Specifically, the app processes the received data and displays it on the user interface.

[1602] (Input): Value-added information received from the server

[1603] (Output): Value-added information displayed within the app (e.g., recipes, campaign information)

[1604] Through the above steps, a system is realized that allows users to effectively use electronic payment data and obtain useful added-value information.

[1605] 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.

[1606] The present invention relates to a system that uses transaction data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information, and further combines this with an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[1607] System configuration

[1608] The system includes the following major components:

[1609] 1. User Device:

[1610] A device such as a smartphone or tablet on which users make transactions.

[1611] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[1612] 2. Server:

[1613] It has the function of receiving transaction data and storing it in a database.

[1614] It includes analytical functions such as emotion engines and generation modules.

[1615] 3. Database:

[1616] Data storage for storing transaction data and generated value-added information.

[1617] 4. Emotion Engine:

[1618] It has the ability to analyze facial expression data and voice data sent from the user device and recognize the user's emotions.

[1619] 5. Generation module:

[1620] Transaction data and perceived user sentiment are analyzed and value-added information is generated based thereon.

[1621] Program processing flow

[1622] 1. Obtaining payment information

[1623] Device:

[1624] When a user makes a payment using an electronic payment app, the terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also acquires the user's emotional data (facial expressions and voice).

[1625] 2. Send receipt information

[1626] Device:

[1627] Transaction data and emotion data are encrypted and sent to the server along with the user's unique account ID.

[1628] 3. Save your receipt

[1629] server:

[1630] The consistency of the transaction data and sentiment data is checked and stored in the database.

[1631] 4. Emotion Data Analysis

[1632] server:

[1633] The emotion engine analyzes the user's emotional data and recognizes their current emotional state, for example, by identifying emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[1634] 5. Comprehensive data analysis

[1635] server:

[1636] The generation module analyzes the transaction data and the recognized emotion data, thereby generating the optimal value-added information that matches the user's emotions.

[1637] 6. Providing value-added information

[1638] server:

[1639] The generated value-added information (e.g., recipes, promotional information) is transmitted to the user device.

[1640] 7. Display of Information

[1641] Device:

[1642] The user device displays the received value-added information within the application.

[1643] Specific examples

[1644] For example, consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. When paying, the emotion engine uses a camera to detect the user's facial expressions and analyzes their voice to determine that the user is busy but happy.

[1645] 1. Obtaining payment information:

[1646] Terminal: Obtain transaction data "Product: Chicken, onion, potato, Price: 2000 yen, Date and time: 2023-10-01" and emotion data.

[1647] 2. Sending receipt information:

[1648] Terminal: Transaction data and emotional data are encrypted and sent to the server.

[1649] 3. Save your receipt:

[1650] Server: Stored in the database.

[1651] 4. Emotional Data Analysis:

[1652] Server: Emotional state analyzed as "busy but happy."

[1653] 5. Comprehensive data analysis:

[1654] Server: Based on the user's emotional state, the server suggests a simple and healthy recipe for "Easy Chicken Curry."

[1655] 6. Providing Value-Added Information:

[1656] Server: Sends suggested recipe information to the user device.

[1657] 7. Displaying Information:

[1658] Device: The user opens the app and sees the recipe for "Easy Chicken Curry" along with their digital receipt.

[1659] The present invention allows users to obtain more personalized added-value information, and improves the quality of their daily lives by suggesting optimal recipes that match their mood on that day, for example.

[1660] The processing flow will be explained below.

[1661] Step 1:

[1662] User: The user decides to purchase an item at a store and opens the electronic payment app. The user then proceeds with the purchase, scans a QR code or other data using a user device such as a smartphone or tablet, and the payment screen is displayed. At this time, the camera and microphone are activated to recognize the user's emotions.

[1663] Step 2:

[1664] Terminal: The user's device communicates with the store's payment system and acquires transaction data, including a list of purchased items, the prices of each item, the total amount, and the date and time of the transaction. At the same time, the camera and microphone are used to acquire facial expression and voice data from the user.

[1665] Step 3:

[1666] Terminal: Once the payment is completed, the terminal encrypts the transaction data (e.g., "Products: chicken, onions, potatoes, price: 2,000 yen, date and time: 2023-10-01") and emotion data and sends them to the server along with the user's unique account ID.

[1667] Step 4:

[1668] Server: The server checks the integrity of the transaction data and emotion data it receives, verifies that the data is accurate, and stores the transaction data and emotion data in the database after confirming that there is no fraud or tampering.

[1669] Step 5:

[1670] Server: When storing transaction data and emotion data in the database, information such as user ID, purchased item, price, transaction date and time, and emotional state is associated and recorded. The database is secured to a certain extent.

[1671] Step 6:

[1672] Server: The emotion engine starts working based on the stored transaction data and emotion data. The emotion engine analyzes the user's facial and voice data to identify the user's current emotional state. For example, it identifies emotions such as "happy" from facial expressions and "busy" from voice tone.

[1673] Step 7:

[1674] Server: The generation module analyzes the emotional state analyzed by the emotion engine and combines it with transaction data. This analysis generates optimal value-added information tailored to the user's emotions. For example, it suggests easy-to-make healthy recipes for an emotional state such as "busy but happy."

[1675] Step 8:

[1676] Server: Sends the generated value-added information (e.g., a simple curry recipe using "chicken, onion, and potato") to the user device. The information is converted into an appropriate format so that the user can easily understand it.

[1677] Step 9:

[1678] Terminal: The user device decodes the value-added information received from the server and displays it within the application. Suggested recipes and other information are displayed along with an electronic receipt.

[1679] Step 10:

[1680] User: The user opens the application and checks the latest transaction information (electronic receipt) and suggested value-added information (e.g. curry recipe). The user receives the best suggestions tailored to their mood, improving the quality of their daily life.

[1681] This allows the system to provide added-value information that takes user emotions into account, significantly improving the convenience of electronic receipts. Furthermore, users can enjoy a more satisfying shopping experience by receiving suggestions that perfectly match their emotions.

[1682] Example 2

[1683] 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."

[1684] In modern electronic commerce, it is important to understand not only a user's purchasing data but also their emotional state at the time to provide more personalized, value-added information. However, conventional systems are limited to collecting and analyzing transaction data and are unable to generate value-added information that takes user emotions into account. This makes it difficult to provide information that meets individual user needs, limiting improvements to the user experience. Furthermore, providing personalized information through emotion analysis is difficult to achieve due to the increased complexity of the analysis.

[1685] 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.

[1686] In this invention, the server includes a means for receiving transaction data and emotion data acquired by a user device, a means for storing the transaction data and emotion data in a database, and a generation module for analyzing the transaction data and emotion data to generate value-added information. This makes it possible to generate and provide more personalized value-added information based on the user's transaction data and associated emotion data. Specifically, the generation module comprehensively analyzes the transaction data and emotion data and provides optimal recipe information, promotion information, and the like based on the user's emotional state, thereby improving the user experience.

[1687] A "user device" is a hardware device used by a user to conduct a transaction, and includes devices such as smartphones and tablets.

[1688] "Transaction data" refers to information relating to transactions such as purchases made by users, and includes details such as product name, price, and transaction date and time.

[1689] "Emotion data" is data that expresses the user's emotional state, and includes facial expression data and voice data from camera images.

[1690] "Database" refers to a data storage system for systematically storing transaction data and emotion data, including relational database management systems (RDBMS).

[1691] The "generation module" is a software module that analyzes the received transaction data and emotion data and generates optimal value-added information for the user.

[1692] The "emotion engine" is an analysis engine that has the function of analyzing emotion data sent from a user device and recognizing the user's emotional state.

[1693] "Value-added information" refers to additional suggestions and information for users that is generated based on the analysis of transaction data and emotion data, and includes recipes, promotional information, and the like.

[1694] This invention relates to a system that uses transaction data and emotion data acquired by a user device to store electronic receipts, analyzes the data, and generates value-added information. To implement this system, the following main components are required:

[1695] System configuration

[1696] The system includes the following major components:

[1697] 1. User Device:

[1698] These devices, which include smartphones, tablets, etc., are used by users to conduct transactions. These devices have an electronic payment application installed and are equipped with the functionality to capture user transaction data and emotional data using sensors such as cameras and microphones.

[1699] 2. Server:

[1700] It has the function of receiving transaction data and emotion data and storing them in a database. It also includes analysis functions such as an emotion engine and generation module.

[1701] 3. Database:

[1702] Data storage for storing transaction data and generated value-added information, specifically using a relational database such as MySQL.

[1703] 4. Emotion Engine:

[1704] It has the ability to analyze facial expression and voice data sent from the user's device using a deep learning algorithm and recognize the user's emotions.

[1705] 5. Generation module:

[1706] A software module for analyzing transaction data and recognized user emotion data and generating value-added information based on the analysis.

[1707] Program processing flow

[1708] When a user makes a transaction, the user device acquires transaction data and emotion data. The acquired data is encrypted using encryption technology such as AES and sent to the server via HTTPS protocol. The server checks the integrity of the data, stores it in a database, and then analyzes the emotion data using an emotion engine. The analysis results are integrated with the transaction data, and a generation module generates optimal value-added information (e.g., recipes or promotion information). This information is sent to the user device and displayed within the electronic payment application.

[1709] Specific examples

[1710] Consider a case where a user purchases chicken, onions, and potatoes at a supermarket using electronic payment. At this time, the camera on the user device captures the user's facial expression and the microphone records the user's voice when making the payment. The transaction data obtained is "Product: Chicken, Onion, Potato, Price: 2000 yen, Date and Time: 2023-10-01" and emotion data "Smile, Happiness."

[1711] The encrypted data is then sent to the server along with the user's account ID. The server verifies the data's integrity and stores it in a database. The emotion engine detects the "feeling of happiness," and the generation module generates a recipe for "easy chicken curry" based on this information and sends it to the user's device. The user can then review the suggested recipe within the electronic payment application and begin cooking.

[1712] Prompt Sentence Examples

[1713] By inputting the following prompt sentences into the generative AI model, it is expected that the system's processing flow will be explained in detail.

[1714] You have invented a system that uses user transaction data and emotional data to store electronic receipts and generate value-added information. As a concrete example, let's show the process flow when a user buys chicken, onions, and potatoes at a supermarket. At the time of payment, the system analyzes the user's facial expressions and voice and recognizes that the user is busy but happy. Based on this information, the system suggests a simple and healthy recipe for "Easy Chicken Curry." Based on the above, please explain the system's process flow in detail.

[1715] This invention allows users to obtain personalized value-added information and suggests optimal recipes that match their mood on that day, thereby improving the quality of their daily lives.

[1716] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1717] Step 1: Get your payment information

[1718] Terminal: When a user makes a payment using an electronic payment app, the terminal acquires transaction data (e.g., purchased item, price, transaction date and time, etc.). The terminal captures the user's facial expression with a camera and records the user's voice with a microphone. These data are stored as transaction data and emotion data.

[1719] Input: User transactions, camera images, audio

[1720] Output: Transaction data, sentiment data

[1721] What it does: It runs a facial recognition algorithm on the smartphone camera to extract facial expression data, and uses a speech recognition algorithm to analyze emotions from recorded speech.

[1722] Step 2: Send receipt information

[1723] Terminal: The acquired transaction data and emotion data are encrypted using AES (Advanced Encryption Standard) encryption technology and sent to the server along with the user's unique account ID. The transmission method uses the HTTPS protocol to ensure secure data transfer.

[1724] Input: Transaction data, emotion data, account ID

[1725] Output: Encrypted transaction data and sentiment data

[1726] Specific operation: The data encryption module encrypts the transaction data and emotion data using the AES method and sends them to the server using the HTTPS protocol.

[1727] Step 3: Save your receipt

[1728] Server: The server checks the consistency of the received transaction data and emotion data. A hash function is used to check consistency and ensure the data has not been tampered with. After checking, the data is stored in a database. A relational database such as MySQL is used as the database.

[1729] Input: Encrypted transaction data and sentiment data

[1730] Output: Integrity-checked transaction data and sentiment data

[1731] Specific operation: The server checks the integrity of the data using a hash function such as SHA-256, and then stores the data in the MySQL database.

[1732] Step 4: Analyze the sentiment data

[1733] Server: The server uses an emotion engine to analyze the user's emotional data. Specifically, it analyzes image data acquired from the camera using a deep learning model to identify emotions from facial expressions. At the same time, it analyzes audio data using voice recognition technology to determine emotions from the tone and intonation of the voice.

[1734] Input: Emotion data with integrity check

[1735] Output: Parsed emotional state

[1736] How it works: Facial expression data is analyzed using a deep learning model (e.g., a convolutional neural network) to identify emotions. Similarly, voice data is analyzed using a recurrent neural network.

[1737] Step 5: Comprehensive data analysis

[1738] Server: The server passes transaction data and recognized emotion data to the generation module. The generation module uses machine learning algorithms to generate value-added information based on the user's emotional state. For example, if the server recognizes that the user is in a happy state, it will suggest simple recipes that will increase happiness.

[1739] Input: Transaction data, analyzed emotional state

[1740] Output: Generated value-added information

[1741] Specific operation: The generation module uses a machine learning algorithm (e.g., Random Forest or Gradient Boosting Machine) to analyze the input data and generate value-added information.

[1742] Step 6: Provide value-added information

[1743] Server: Sends the generated value-added information to the user device. This transmission also uses the HTTPS protocol to ensure data security. The information includes recipes, promotional information, etc.

[1744] Input: Generated value-added information

[1745] Output: Value-added information sent to the user device

[1746] Specific operation: The generated information is sent to the user device using the HTTPS protocol.

[1747] Step 7: Viewing information

[1748] Terminal: The user device displays the received value-added information in the electronic payment application. For example, a suggested recipe is displayed along with an electronic receipt. The user can open the app and check the detailed recipe.

[1749] Input: Value-added information sent to the user device

[1750] Output: Value-added information displayed within the application

[1751] Specific behavior: The application displays the received information in an appropriate format so that the user can view it.

[1752] Through the above processing steps, the system generates personalized value-added information based on the user's transaction data and emotion data, improving the user experience.

[1753] (Application example 2)

[1754] 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."

[1755] While modern electronic payment services exist that provide value-added information based on transaction data acquired by users, they are unable to provide personalized information using user emotional data. As a result, it is difficult to provide optimal advice and promotions that correspond to the user's specific situation and emotions, and this has prevented them from improving user satisfaction and strengthening loyalty.

[1756] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving transaction data and emotion data acquired by a user terminal, means for storing the transaction data and emotion data in a database, a generation engine for analyzing the transaction data and emotion data to generate value-added information, means for transmitting the value-added information to the user terminal, and means for displaying the value-added information on the user terminal. This makes it possible to provide personalized promotions and information that correspond to the user's emotional state.

[1757] "User terminal" refers to a device used by a user to conduct transactions, and specifically includes smartphones, tablets, etc.

[1758] "Transaction data" refers to information about transactions conducted by users, including product information, price information, transaction date and time, etc.

[1759] "Emotion data" refers to data that indicates the user's emotional state, and mainly includes facial expression data captured by a camera and voice data captured by a microphone.

[1760] "Database" refers to a storage device for storing acquired transaction data and emotion data.

[1761] "Generation engine" refers to a module that analyzes transaction data and emotion data and generates value-added information based on the analysis.

[1762] "Value-added information" refers to information generated based on transaction data and emotion data, and specifically includes promotion information and recipe information.

[1763] This invention is a system that uses transaction data acquired by a user terminal to store electronic receipts, analyzes the data, and generates value-added information. It also combines an emotion engine that recognizes the user's emotions to provide more advanced value-added information.

[1764] System configuration

[1765] 1. User Device:

[1766] A terminal such as a smartphone or tablet on which a user makes a transaction.

[1767] An electronic payment application is installed, and the device has the ability to acquire user emotional data using sensors such as a camera and microphone.

[1768] 2. Server:

[1769] It has the function of receiving transaction data and emotion data and storing it in a database.

[1770] Includes analytical functions such as an emotion engine and a generation engine.

[1771] 3. Database:

[1772] This is a storage device for storing transaction data and emotion data.

[1773] 4. Emotion Engine:

[1774] It has the ability to analyze facial expression and voice data sent from the user's device and recognize the user's emotions.

[1775] 5. Generation engine:

[1776] This module analyzes transaction data and recognized emotion data and generates value-added information based on the analysis.

[1777] Explanation of program processing

[1778] Obtaining payment information

[1779] When a user makes a payment using an electronic payment application, the user terminal acquires transaction data (purchased item, price, transaction date and time, etc.) and also uses a camera and microphone to simultaneously acquire the user's emotional data (e.g., facial expressions and voice).

[1780] Sending receipt information

[1781] The user device encrypts the acquired transaction data and emotion data and transmits them to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[1782] Saving receipts

[1783] The server checks the integrity of the received transaction data and sentiment data and stores it in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL) to ensure consistency and durability.

[1784] Emotional Data Analysis

[1785] The emotion engine in the server analyzes the transmitted facial expression and voice data to recognize the user's emotional state. For example, it can identify emotions from facial expressions captured by a camera or tone of voice recorded by a microphone.

[1786] Comprehensive analysis of data

[1787] The generation engine in the server integrates and analyzes the transaction data and the recognized emotion data, thereby generating optimal value-added information (e.g., promotional information) tailored to the user's emotions.

[1788] Providing added-value information

[1789] The generated value-added information is again transmitted to the user terminal and displayed within the application.

[1790] Adding specific examples

[1791] For example, consider the case where a user purchases a coffee at a cafe using electronic payment. At the time of payment, the emotion engine uses the camera and microphone to recognize that the user is feeling stressed. In this case, the following occurs:

[1792] 1. Obtaining payment information:

[1793] The user device acquires the transaction data "Product: Coffee, Price: 500 yen, Date and Time: 2023-10-20" and the emotion data "Stress."

[1794] 2. Sending receipt information:

[1795] The user device encrypts transaction data and emotion data and sends them to the server.

[1796] 3. Save your receipt:

[1797] The server stores the data in a database.

[1798] 4. Emotional Data Analysis:

[1799] The server analyzes the emotional state as "stress."

[1800] 5. Comprehensive data analysis:

[1801] The server generates promotions for relaxation products based on the user's emotional state, offering coupons for "aromatherapy oils for stress relief."

[1802] 6. Providing Value-Added Information:

[1803] The proposed promotion information is sent to the user terminal.

[1804] 7. Displaying Information:

[1805] The user opens the app and sees the "aromatherapy oil" coupon along with the electronic receipt.

[1806] Example prompt sentence:

[1807] Prompt for the generative AI model: "When a user is in a cafe and buying coffee while feeling stressed, please generate promotional information for relaxation products to help reduce stress."

[1808] This prompt allows the model to generate appropriate value-added information based on the user's emotional state and transaction data.

[1809] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1810] Step 1: Get your payment information

[1811] Description: When a user makes a payment using an electronic payment application, the terminal captures transaction data (item purchased, price, transaction date and time, etc.). In parallel, the emotion engine on the terminal uses the camera and microphone to capture the user's emotion data (facial expressions and voice). Specifically, the camera captures the user's facial expressions, and the microphone records the tone and volume of the voice.

[1812] Input: User purchase operation

[1813] Output: Transaction data, sentiment data

[1814] Step 2: Send receipt information

[1815] Description: The terminal encrypts the acquired transaction data and emotion data with AES encryption and sends it to the server along with the user's unique account ID. This transmission process uses a secure communication protocol (e.g., HTTPS).

[1816] Input: Transaction data, sentiment data

[1817] Output: Encrypted data packet

[1818] Step 3: Save your receipt

[1819] Description: The server decrypts the received transaction data and sentiment data and checks the integrity and consistency of the data. It then stores this data in a database. This data storage process uses a database management system (e.g., MySQL, PostgreSQL).

[1820] Input: Encrypted data packet

[1821] Output: Transaction data and sentiment data in the database

[1822] Step 4: Analyze the sentiment data

[1823] Description: The emotion engine in the server analyzes facial expression and voice data stored in the database to identify the user's emotional state. For example, it uses machine learning models (e.g., CNN or RNN) to determine whether the user is feeling "stressed" or "happy" based on facial expressions captured by a camera and tone of voice recorded by a microphone.

[1824] Input: Emotion data in the database

[1825] Output: User's emotional state

[1826] Step 5: Comprehensive data analysis

[1827] Description: The generation engine on the server integrates and analyzes transaction data and recognized emotional data. Specifically, it combines transaction data (e.g., type and price of purchased product) with emotional data (e.g., stress level) to generate value-added information (promotion information, recipe information) that is optimal for the user. In this process, a generative AI model is used to generate value-added information that meets the user's needs.

[1828] Input: Transaction data, user emotional state

[1829] Output: Value-added information

[1830] Step 6: Provide value-added information

[1831] Description: The generated value-added information is re-encrypted and sent to the terminal along with the user's unique account ID. The transmission process again uses a secure communication protocol (e.g., HTTPS).

[1832] Input: Value-added information

[1833] Output: Encrypted value-added information data packet

[1834] Step 7: Viewing information

[1835] Description: The terminal decodes the received value-added information and displays it within the electronic payment application. Specifically, promotional information and recipe information are displayed along with the electronic receipt. For example, if a user purchases coffee at a cafe and the emotion engine recognizes stress, a coupon for "aromatherapy oil for stress reduction" is displayed.

[1836] Input: Encrypted value-added information data packet

[1837] Output: Value-added information displayed to the user

[1838] 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.

[1839] 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.

[1840] 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.

[1841] 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.

[1842] 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.

[1843] 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.

[1844] 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).

[1845] 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.

[1846] 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."

[1847] 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.

[1848] 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).

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] The following is further disclosed regarding the above embodiment.

[1860] (Claim 1)

[1861] means for receiving the acquired transaction data at the user device;

[1862] means for storing said transaction data in a database;

[1863] a generation module that analyzes the transaction data and generates value-added information;

[1864] means for transmitting the value-added information to a user device;

[1865] means for displaying the value-added information in the user device;

[1866] A system including:

[1867] (Claim 2)

[1868] 2. The system of claim 1, wherein the transaction data is electronic receipt information including at least product name and price information.

[1869] (Claim 3)

[1870] The system of claim 1 , wherein the generation module generates recipe information using the transaction data.

[1871] "Example 1"

[1872] (Claim 1)

[1873] means for receiving the acquired transaction data at the user device;

[1874] means for encrypting and transmitting the transaction data;

[1875] means for storing said transaction data in a database;

[1876] means for verifying the integrity of the stored transaction data;

[1877] a generation module that analyzes the transaction data and generates value-added information;

[1878] means for transmitting the value-added information generated by the generation module to a user device;

[1879] means for displaying the value-added information in the user device;

[1880] A system including:

[1881] (Claim 2)

[1882] 2. The system of claim 1, wherein the transaction data is electronic receipt information including at least product name and price information.

[1883] (Claim 3)

[1884] 2. The system of claim 1, wherein the generation module includes a generative AI model that uses the transaction data to generate recipe information.

[1885] "Application Example 1"

[1886] Rewrite

[1887] (Claim 1)

[1888] means for receiving the acquired transaction data at the user device;

[1889] means for storing said transaction data in a database;

[1890] a generation module that analyzes the transaction data and generates value-added information;

[1891] means for transmitting the value-added information to a user device;

[1892] means for displaying the value-added information in the user device;

[1893] means for providing campaign information based on the user's spending patterns and purchase contents as the added value information;

[1894] A system including:

[1895] (Claim 2)

[1896] 2. The system of claim 1, wherein the transaction data is electronic receipt information including at least product name and price information.

[1897] (Claim 3)

[1898] The system of claim 1 , wherein the generation module generates recipe information using the transaction data.

[1899] (Claim 4)

[1900] 10. The system of claim 1, wherein the generation module uses a generative AI model that uses the transaction data to provide reward information based on a user's spending patterns.

[1901] "Example 2: Combining Emotion Engines"

[1902] (Claim 1)

[1903] means for receiving transaction data and emotion data acquired at a user device;

[1904] means for storing the transaction data and emotion data in a database;

[1905] a generation module that analyzes the transaction data and emotion data to generate value-added information;

[1906] means for transmitting the value-added information to a user device;

[1907] means for displaying the value-added information in the user device;

[1908] A system including:

[1909] (Claim 2)

[1910] The system of claim 1 further comprising an emotion engine that generates the value-added information based on the transaction data and emotion data.

[1911] (Claim 3)

[1912] The system of claim 1 , wherein the generation module uses the transaction data and the emotion data to generate recipe information based on the user's emotional state.

[1913] "Application example 2 when combining emotion engines"

[1914] (Claim 1)

[1915] means for receiving the acquired transaction data at the user terminal;

[1916] means for storing the transaction data and user emotion data in a database;

[1917] a generation engine that analyzes the transaction data and emotion data to generate value-added information;

[1918] means for transmitting the value-added information to a user terminal;

[1919] means for displaying the added-value information in the user terminal;

[1920] A system including:

[1921] (Claim 2)

[1922] 2. The system according to claim 1, wherein the transaction data is electronic receipt information including at least product information and price information, and the emotion data is data indicating an emotional state of a user.

[1923] (Claim 3)

[1924] The system of claim 1 , wherein the generation engine generates promotional information using the transaction data and emotion data. [Explanation of symbols]

[1925] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving the acquired transaction data at the user device; means for storing said transaction data in a database; a generation module that analyzes the transaction data and generates value-added information; means for transmitting the value-added information to a user device; means for displaying the value-added information in the user device; A system including:

2. The system of claim 1 , wherein the transaction data is electronic receipt information including at least product name and price information.

3. The system of claim 1 , wherein the generation module generates recipe information using the transaction data.

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