System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting transaction history data of a user; means for analyzing the transaction history data and generating a preference profile of the user; means for proposing a plurality of return items suitable for the user; means for performing a donation application procedure for a return item selected by the user from the proposed return items; and means for collecting feedback on the return item from the user and improving accuracy of a next proposal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The hometown tax payment procedure is complicated for many users, and selecting a gift in return takes time and effort. It is also often difficult for users to select the gift that best suits their preferences. It is also generally difficult to accurately calculate the maximum donation amount for hometown tax payments. To solve these problems, a system is needed that allows users to easily complete the hometown tax payment procedure and efficiently select the most suitable gift in return. [Means for solving the problem]

[0005] The present invention provides a means for collecting a user's transaction history data and analyzing this data to generate a user preference profile. Next, a means for proposing multiple suitable return gifts to the user based on this profile is provided. Furthermore, a means for smoothly completing the donation application process for the return gift selected by the user is provided, thereby completing the application process. Furthermore, a means for collecting feedback from the user on the return gift and improving the accuracy of the next proposal is provided, thereby increasing user satisfaction. Furthermore, a means for collecting the user's location data and calculating the user's hometown tax donation limit based on this data and transaction history data is provided, thereby simplifying the calculation of the donation limit. These means provide a system that allows users to make hometown tax donations easily and efficiently and obtain the most suitable return gift.

[0006] "User data" refers to data relating to a user's transaction history, residential location information, and preferences.

[0007] "Transaction history data" refers to data relating to purchases and payments made by a user in the past.

[0008] A "preference profile" is data about a user's preferences that is obtained by analyzing the user's transaction history data and interests.

[0009] A "return gift plan" refers to multiple options offered as return gifts for hometown tax donations.

[0010] The "donation application procedure" is the hometown tax payment procedure carried out based on the return gift plan selected by the user.

[0011] "Feedback" refers to the evaluation or opinion a user gives about the gift they received.

[0012] "Location data" refers to data including information about the user's residence and current location.

[0013] The "donation limit" refers to the maximum amount that a user can donate through hometown tax donations.

[0014] The "generative AI model" is an artificial intelligence model that analyzes users' transaction history data and preferences to generate personalized reward gift plans. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. The system includes a server, a user terminal, and a generative AI model.

[0037] Program processing

[0038] 1. Collection and analysis of user data

[0039] Subject: Server

[0040] The server collects the user's transaction history data. Specifically, the server captures the user's payment history, residential location information, and in-app behavior history, and stores this information in a database. The server then analyzes this data to generate a user preference profile. A generative AI model is used to identify personalized preferences based on the user's purchase history, product categories of interest, and other factors.

[0041] Examples:

[0042] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients.

[0043] 2. Generate personalized reward plans

[0044] Subject: Server

[0045] The server generates multiple gift plans based on the preference profile output by the generative AI model. This includes logic for selecting gifts that match the user's donation limit and preferences. To maximize user satisfaction, the server also takes past feedback data into account and suggests the most likely options for the next time.

[0046] Examples:

[0047] The server generates a gift plan for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen."

[0048] 3. Present a gift plan

[0049] Subject: Terminal

[0050] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the screen.

[0051] Examples:

[0052] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," including "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo."

[0053] 4. Select a gift plan and apply

[0054] Subject: User

[0055] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[0056] Examples:

[0057] User B selects "Niigata Prefecture Junmai Sake," enters the donation amount and shipping information within the app, and completes the application process with just a few clicks.

[0058] 5. Gather feedback and improve your next plan

[0059] Subject: Server

[0060] The server collects feedback about the gifts received by the user, including ratings and comments made by the user about the gifts, and uses this feedback when generating the next plan to help provide a plan that better matches the user's preferences.

[0061] Examples:

[0062] If user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the server will reflect this feedback in the next plan generation and suggest a more accurate sake plan to the user.

[0063] With this configuration, the present invention can realize a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[0064] The processing flow will be explained below.

[0065] Step 1: Collect user data

[0066] Subject: Server

[0067] The server accesses a database to collect user transaction history data, including the user's past payment history, residential location information, and in-app activity history. The server periodically checks this information to obtain the latest data.

[0068] Specific behavior:

[0069] The server executes an SQL query to retrieve the most recent payment history from the transaction history table.

[0070] Get registered address and GPS data from the user profile table.

[0071] Analyze log data of page visits and product views within the app.

[0072] Step 2: Generate a preference profile

[0073] Subject: Server

[0074] The server inputs the collected data into a generative AI model to generate a user preference profile, which involves analyzing patterns of the user's past purchases and pages viewed.

[0075] Specific behavior:

[0076] The server preprocesses the collected data to match the input format of the generative AI model.

[0077] The preprocessed data is fed into an AI model to generate a preference profile.

[0078] The generated preference profile is linked to the user information in the database and saved.

[0079] Step 3: Create a reward plan

[0080] Subject: Server

[0081] The server generates a reward plan suitable for the user based on the preference profile and the donation limit, which includes a process of suggesting multiple reward options.

[0082] Specific behavior:

[0083] The server filters the reward information in the database based on the preference profile data.

[0084] A gift is selected from the search results so that it falls within the user's donation limit.

[0085] A number of plans are generated and a list is created to be proposed to the user.

[0086] Step 4: Present your gift plan

[0087] Subject: Terminal

[0088] The terminal displays the gift plan sent from the server on the user interface, and the user can view the details of the proposed plan on the screen.

[0089] Specific behavior:

[0090] The terminal parses the received gift plan data.

[0091] Bind the parsed data to a UI component to display the details.

[0092] Build an interface for users to view plan details.

[0093] Step 5: Select a reward plan and apply

[0094] Subject: User

[0095] Users can select the desired plan from the multiple gift plans presented, and then complete the donation application process within the app.

[0096] Specific behavior:

[0097] The user selects a desired plan from the displayed plans.

[0098] Follow the instructions on the device to enter your donation amount and shipping information.

[0099] Tap the Apply button, confirm all the information, and then complete the process.

[0100] Step 6: Gather feedback

[0101] Subject: User

[0102] Users can enter their ratings and comments on the gifts they receive, and this feedback will be used to generate the next plan.

[0103] Specific behavior:

[0104] Users can enter feedback about the gift through a rating form.

[0105] The terminal transmits the input feedback to the server.

[0106] Step 7: Analyze feedback and improve your next plan

[0107] Subject: Server

[0108] The server analyzes the received feedback and reflects it in the generative AI model, thereby improving the accuracy of the next plan generation.

[0109] Specific behavior:

[0110] The server stores the feedback data in a database.

[0111] The saved data is input into a generative AI model and used as training data for the model.

[0112] The improved model is used to generate the next reward plan.

[0113] Example 1

[0114] 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."

[0115] The problem with hometown tax donations is that the process of users selecting a donation recipient and gift in return is complicated and requires a great deal of time and effort. It can also be difficult to find a gift that matches a user's preferences, which can result in lower satisfaction. Additionally, the criteria for selecting the best gift from the wide variety of gifts available are unclear, making it difficult for users to make an appropriate choice.

[0116] 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.

[0117] In this invention, the server includes a means for collecting user transaction history, a means for analyzing the transaction history to generate a user preference profile, and a means for generating multiple return gift plans based on the preference profile. This allows users to easily select the optimal return gift plan based on their preferences, simplifies the donation application process, and further allows for feedback to be collected to improve the accuracy of future proposals.

[0118] "Means for collecting user transaction history" refers to a device or program for acquiring and recording data such as a user's past purchase history, payment history, and residential information.

[0119] The "means for generating a user preference profile" is a device or program for analyzing collected user transaction history data and identifying the user's purchasing tendencies and preferences.

[0120] The "means for generating multiple return gift plans" is a device or program for proposing multiple return gifts that are optimal for a user based on the user's preference profile.

[0121] "Means for displaying the generated gift plan on the user's terminal" refers to a device or program for transmitting the gift plan generated by the server to the user's terminal and displaying it on the screen.

[0122] The "means for carrying out donation application procedures" refers to a device or program for carrying out the necessary donation application for the return gift plan selected by the user.

[0123] The "means for collecting feedback" is a device or program for obtaining and collecting feedback such as ratings and comments on the return gift from users.

[0124] The "means for improving the accuracy of the next proposal" is a device or program that analyzes the collected feedback and reflects it when generating the next return gift plan, thereby making proposals that are more in line with the user's preferences.

[0125] The "means for collecting user location data" refers to a device or program for acquiring and recording location information such as the user's current location and past movement history.

[0126] The "means for calculating the maximum donation amount" is a device or program for calculating the maximum amount that a user can donate based on the collected location data and transaction history data.

[0127] The "means for generating documents required for donation applications" refers to a device or program that automatically generates the documents and forms required when a user applies for a donation.

[0128] "Means for updating the progress of a donation request" refers to a device or program for managing and updating the progress of a donation request (e.g., application in progress, completed) based on the gift plan selected by the user.

[0129] The present invention is a system that collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. This system includes a server, a user terminal, and a generative AI model.

[0130] The server collects the user's transaction history data. Specifically, the server captures information such as the user's payment history, location information, and in-app behavior history, and stores this data in a database. The server then analyzes this data to generate a user preference profile. A generative AI model (e.g., OpenAI's GPT-4) is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[0131] Examples:

[0132] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in local specialties and luxury ingredients. An example of a prompt sentence is input to the generative AI model: "Analyze the user's transaction history data and generate a food preference profile."

[0133] The server then generates multiple gift plans based on the preference profile output by the generative AI model. The server then executes logic to select gifts that fit the user's donation limit and preferences, and takes past feedback data into account to suggest the most likely options for the next time, thereby maximizing user satisfaction.

[0134] Examples:

[0135] The server generates gift plans for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen." As an example of a prompt, the server inputs the instruction "Generate an appropriate gift plan based on User A's preference profile" into the generation AI model.

[0136] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the device, allowing the user to select one.

[0137] Examples:

[0138] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," such as "Junmai sake from Niigata Prefecture" and "Daiginjo from Yamagata Prefecture." As an example of a prompt, the instruction "Display detailed information about the proposed return gift plan" is input to the generative AI model.

[0139] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[0140] Examples:

[0141] User B selects "Junmai sake from Niigata Prefecture," enters the donation amount and shipping information in the app, and completes the application process with just a few clicks. An example of a prompt sentence is input to the generative AI model: "Complete the donation application process according to the gift plan you selected."

[0142] Finally, the server collects feedback about the gifts received by the user, including ratings and comments the user makes about the gifts, and uses this feedback to generate the next plan to help provide a plan that better matches the user's preferences.

[0143] Examples:

[0144] If User C gives a high rating to the "Niigata Prefecture Junmai Sake" that he received, the server will reflect this feedback in the next plan generation and propose a more accurate sake plan to the user. An example of a prompt sentence is to input the instruction "Collect feedback about the gift received and use it to generate the next plan" into the generative AI model.

[0145] With this configuration, the present invention enables a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] The server collects the user's transaction history. First, it calls an API to obtain the user's past purchase history data from the payment system and stores the results in a database. Next, it obtains the user's residential location information and stores it in the database as well. It also analyzes the user's behavioral history within the app and saves it in the database as transaction history data.

[0149] Input: User purchase history, payment history, residential location information, in-app behavior history

[0150] Output: Collected transaction history data stored in a database

[0151] Specific operation: The server calls the API to obtain PayPal and credit card payment history and stores it in a database within the system.

[0152] Step 2:

[0153] The server analyzes the collected transaction history data to generate a user preference profile. Using a generative AI model (e.g., OpenAI's GPT-4), it analyzes the transaction history retrieved from the database to identify the user's purchasing trends and product categories that indicate their interests.

[0154] Input: Transaction history data

[0155] Output: User preference profile

[0156] Specific operation: The server inputs the transaction history data into the generation AI model and generates a profile using the prompt sentence, "Analyze the user's transaction history data and generate a food preference profile."

[0157] Step 3:

[0158] The server generates multiple gift plans based on the generated preference profile, and uses a generative AI model to create the optimal gift plan, taking into account the user's donation limit and past feedback data.

[0159] Inputs: Preference profile, donation limit, feedback data

[0160] Output: Personalized gift plan

[0161] Specific operation: The server inputs a prompt statement such as "Generate an appropriate gift plan based on user A's preference profile" into the generative AI model and combines gift plans.

[0162] Step 4:

[0163] The user's device receives the gift plan data sent from the server and displays it within the app, allowing the user to check detailed information about the gift plan.

[0164] Input: Gift plan data

[0165] Output: Display of reward plan on the app screen

[0166] Specific operation: The device parses the data sent from the server in JSON format and displays detailed information such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture" on the screen.

[0167] Step 5:

[0168] The user selects the desired gift plan from the displayed options and completes the donation application process. The user enters the donation amount and shipping address information and follows the application procedure guide to complete the application.

[0169] Input: User selection, donation amount, shipping information

[0170] Output: Notification of application completion and corresponding data update

[0171] Specific operation: User B selects "Niigata Prefecture Junmai Sake," enters the donation amount of 50,000 yen and shipping information, and clicks the "Application Complete" button.

[0172] Step 6:

[0173] The server collects feedback from users, including ratings and comments on the gifts they received, and stores them in a database as reference information for generating the next gift plan.

[0174] Input: User feedback (ratings, comments)

[0175] Output: Feedback data stored in a database and the next plan generated based on the feedback

[0176] Specific operation: The server obtains the user's rating (e.g., "I gave this Junmai sake from Niigata Prefecture five stars") from the feedback section within the app and reflects it in the next prompt sentence for the generative AI model.

[0177] (Application example 1)

[0178] 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."

[0179] Conventional systems did not adequately personalize products and gifts to match users' preferences, making it difficult to improve the user experience. Additionally, the accuracy of suggestions was low, making it difficult to increase user satisfaction. This made it difficult for users to find the best product for them from the many options available, requiring cumbersome search tasks. Furthermore, there was no system for incorporating feedback into the next suggestions, making it difficult to ensure continued user satisfaction.

[0180] 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.

[0181] In this invention, the server includes means for collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for suggesting multiple gift items or products suitable for the user, means for the user to purchase a gift item or product selected from the suggested gift items or products, and means for collecting feedback from the user on the gift items or products and improving the accuracy of subsequent suggestions. This allows users to easily find products that suit their preferences, improving their purchasing experience. Furthermore, the use of a generative AI model improves the accuracy of data analysis, enabling more personalized suggestions. Furthermore, by incorporating user feedback into subsequent suggestions, continued improvement in user satisfaction can be expected.

[0182] "User transaction history data" refers to information about a user's past purchasing behavior and payments, including details such as product name, purchase date and time, payment amount, and payment method.

[0183] A "preference profile" is a collection of information that indicates a user's preferences and interests, generated based on the user's past behavioral data. This profile includes information such as the user's preferred categories, brands, and price ranges.

[0184] A "return gift" is a product or service that a user can receive when making a donation to a specific service.

[0185] "Product" is a general term for products and services that users can purchase, including physical goods, digital content, and services.

[0186] "Donation Request Process" means the formal process by which a User may make a donation to a particular service or cause, including entering a donation amount and submitting any required documentation.

[0187] "Feedback" refers to the evaluation or comments that users make on the products or gifts they receive, which can help improve the accuracy of future suggestions.

[0188] "Location data" refers to information about a user's current location and past visit history, collected through GPS, Wi-Fi, and base station data.

[0189] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and predicts user preferences and patterns. This model uses machine learning and deep learning techniques.

[0190] A "prompt" is text data that is input to a particular generative AI model and is an instruction that the model uses to generate the appropriate output.

[0191] The system of the present invention collects and analyzes users' transaction history data and location data to propose personalized products and return gifts suitable for the user, simplifying the purchasing process and donation application process. This system includes a server, a user terminal, and a generative AI model. Specific embodiments are described in detail below.

[0192] Collection and analysis of user data

[0193] The server collects data on users' transaction history, location, and purchasing behavior, including payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. The data is then analyzed using Pandas and Scikit-Learn to generate a user preference profile.

[0194] Examples:

[0195] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients. A generative AI model is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[0196] Generate personalized suggestions

[0197] The server generates multiple rewards or product plans based on the preference profile output by the generative AI model. This generative AI model uses machine learning and deep learning technologies to analyze users' purchasing preferences and behavioral patterns.

[0198] Examples:

[0199] Based on the information that "User A is interested in a particular brand and has a budget of 50,000 yen," the server generates a shopping plan that includes information on popular products and special sales from that brand.

[0200] Presenting the proposal

[0201] The user device receives the personalized gift or product plan sent from the server and presents it to the user within the app, where detailed information, ratings, prices, discount information, etc. for each gift or product are displayed.

[0202] Examples:

[0203] The device displays multiple product plans in a section called "Recommended Shopping Plans," such as "luxury bags from specific brands" and "latest smartphone models."

[0204] Purchase or donation process

[0205] Users select the desired gift or product plan from the presented options, and then follow the in-app guide to complete the purchase or donation application process, simplifying the complicated process.

[0206] Examples:

[0207] User B selects "luxury bag from a specific brand," enters the purchase amount and shipping information within the app, and completes the purchase process with just a few clicks.

[0208] Feedback and suggestions for next improvements

[0209] The server collects feedback about the gifts or products received by the user, including ratings and comments the user makes about the products or gifts, and uses this feedback when generating subsequent suggestions to help provide suggestions that better match the user's preferences.

[0210] Examples:

[0211] If user C gives a high rating to a "luxury bag from a specific brand" that he / she purchased, the server will reflect this feedback in the next proposal generation and propose a more accurate plan to the user.

[0212] Prompt Sentence Examples

[0213] The following prompt sentences could be input to the generative AI model:

[0214] "Cluster the patterns in the following data and generate the most suitable product list for a specific profile. Data: purchase history, amount, category. Desired result: product list for a specific profile."

[0215] In this way, the present invention enables personalized offers based on user preferences, improving the purchasing experience and ongoing user satisfaction.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The server collects the user's transaction history data and location data. This input data includes the user's payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. This allows the user's past activity data to be managed centrally.

[0219] Step 2:

[0220] The server analyzes the collected transaction history data and location data to generate a user preference profile. This analysis uses Pandas to process the data and Scikit-Learn to apply machine learning models such as clustering. Based on the input data, the server identifies the user's past preferred categories, brands, and price ranges and outputs them as a preference profile.

[0221] Step 3:

[0222] The server uses a generative AI model to generate multiple rewards or product plans based on the user's preference profile. The prompt is "Please generate a product list that matches a specific preference profile." The generative AI model outputs a list of products or rewards that best fit the user's preference profile.

[0223] Step 4:

[0224] The server sends the generated reward or product plan to the user's device, which receives it and presents it to the user within the app. Input includes a product list, ratings, prices, discount information, etc., which are displayed on the screen for easy understanding by the user.

[0225] Step 5:

[0226] The user selects the desired gift or product plan from the presented gift plans. The user terminal sends information about the selected product or gift to the server. The input data is the product information selected by the user, and the server receives this information and proceeds with the purchase procedure or donation application procedure.

[0227] Step 6:

[0228] The server generates the documents required for the purchase process and sends them to the user. The user can then check the documents sent within the app and complete the process with a few simple operations. The input data includes purchase information and shipping address information, and the documents are generated based on this information.

[0229] Step 7:

[0230] After receiving a product or gift, the user provides feedback within the app. The user's device sends this feedback to the server. The input data is the user's rating and comments, and the server analyzes them and reflects them in the next proposal.

[0231] Step 8:

[0232] The server adjusts the prompts for the generative AI model based on the collected feedback to improve the accuracy of the next recommendation. Specifically, it uses prompts such as, "Please consider the user's feedback and generate a product list that will improve the accuracy of the next recommendation." The generative AI model outputs further suggestions based on the new profile and feedback information.

[0233] Through this series of steps, the system will suggest personalized products and gifts based on the user's preferences, improving the user experience.

[0234] 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.

[0235] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[0236] Program processing

[0237] 1. Collection and analysis of user data

[0238] Subject: Server

[0239] The server collects the user's transaction history data and location data and stores them in a database, including the user's payment history, residential location information, behavioral history, etc. The collected data is then analyzed to generate a user preference profile.

[0240] Examples:

[0241] The server collects data such as luxury ingredients and local specialties purchased by the user in the past, and analyzes whether the user is interested in a particular category.

[0242] 2. Emotional state recognition using an emotion engine

[0243] Subject: Server

[0244] The emotion engine analyzes the user's facial expression data, voice data, and text input data to recognize the user's current emotional state. This recognition result, along with the user's preference profile, is used to generate a reward plan.

[0245] Examples:

[0246] The server recognizes facial expressions while the user is operating the app and estimates emotions from voice input. For example, if the user is smiling, it determines that the user is in a positive emotional state.

[0247] 3. Generate personalized reward plans

[0248] Subject: Server

[0249] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the preference profile and the recognition results of the emotion engine. This involves selecting gifts that match the user's preferences within the user's donation limit and adjusting the plan according to the user's emotional state.

[0250] Examples:

[0251] Based on the fact that "User A is interested in sake and his / her current emotional state is positive," the server generates a gift plan for sake offered by local governments across the country and makes suggestions according to the user's emotional state.

[0252] 4. Present a gift plan

[0253] Subject: Terminal

[0254] The user device receives the gift plans sent from the server and presents them to the user within the app. Details of each gift plan are displayed on the screen in a format that corresponds to the user's emotional state.

[0255] Examples:

[0256] The device displays "recommended hometown tax donation plans" such as "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo" with a UI designed to make users feel emotionally satisfied.

[0257] 5. Select a gift plan and apply

[0258] Subject: User

[0259] Users select the desired gift plan from the presented options and complete the donation application process within the app.

[0260] Examples:

[0261] User B selects "Niigata Prefecture Junmai Sake" and enters the donation amount and shipping address information with simple operations to complete the application process.

[0262] 6. Gathering Feedback

[0263] Subject: User

[0264] The user inputs a rating and comment on the gift they received.

[0265] Examples:

[0266] When user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the feedback data is sent to the server.

[0267] 7. Analyze feedback and improve your next plan

[0268] Subject: Server

[0269] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine, thereby improving the accuracy of the next plan generation.

[0270] Examples:

[0271] Based on the positive feedback, the server will improve the model so that it can propose more accurate sake plans the next time it generates a gift plan.

[0272] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[0273] The processing flow will be explained below.

[0274] Step 1: Collect user data

[0275] Subject: Server

[0276] The server collects user transaction history data and location data, including user payment history, residential location information, and in-app activity history.

[0277] Specific behavior:

[0278] The server runs SQL queries against the transaction history database to retrieve past payment records.

[0279] Registered address and GPS data are retrieved from the user profile database.

[0280] Collect user behavior logs within the app (page visits, purchase history).

[0281] Step 2: Generate a preference profile

[0282] Subject: Server

[0283] The server inputs the collected data into a generative AI model to generate a user preference profile.

[0284] Specific behavior:

[0285] The server preprocesses the collected data and converts it into an input format for the AI ​​model.

[0286] The preprocessed data is input into a generative AI model to analyze user preference patterns.

[0287] The generated preference profile is stored in a database.

[0288] Step 3: Recognizing emotional states using the emotion engine

[0289] Subject: Terminal

[0290] The terminal collects the user's facial expression data, voice data, and character input data and sends them to the emotion engine, which recognizes the user's current emotional state.

[0291] Specific behavior:

[0292] The device uses the front camera to capture the user's facial expressions.

[0293] A microphone is used to record the user's voice and the voice data is sent to the emotion engine.

[0294] Character data is collected from touch and keyboard input and sent to the emotion engine.

[0295] The emotion engine analyzes the received data and recognizes the user's emotional state.

[0296] Step 4: Create a personalized reward plan

[0297] Subject: Server

[0298] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the user's preference profile and the recognition results of the emotion engine.

[0299] Specific behavior:

[0300] The server inputs preference profile data and emotion recognition results into the AI ​​model.

[0301] The AI ​​model searches for rewards that match the user's donation limit and generates a list.

[0302] Create a list of suggested gift plans and send it to the user.

[0303] Step 5: Present your gift plan

[0304] Subject: Terminal

[0305] The terminal receives the return gift plan sent from the server and displays it on the user interface.

[0306] Specific behavior:

[0307] The terminal parses the received gift plan data.

[0308] Bind the parsed data to a UI component to display the details.

[0309] The display format is presented to the user according to the emotional state.

[0310] Step 6: Select a reward plan and apply

[0311] Subject: User

[0312] The user selects the desired plan from the presented return gift plans and completes the donation application procedure.

[0313] Specific behavior:

[0314] The user taps the selection button on the screen to select the desired gift plan.

[0315] Enter your donation amount and shipping information.

[0316] Click the Apply button to complete the process.

[0317] Step 7: Gather feedback

[0318] Subject: User

[0319] The user inputs a rating and comment on the gift they received.

[0320] Specific behavior:

[0321] Users can access the in-app rating form and provide feedback about the gift.

[0322] The terminal transmits the input feedback data to the server.

[0323] Step 8: Analyze feedback and improve your next plan

[0324] Subject: Server

[0325] The server analyzes the collected feedback and applies it to the generative AI model and emotion engine, thereby improving the accuracy of the next reward plan.

[0326] Specific behavior:

[0327] The server stores the feedback data in a database.

[0328] The feedback data is input into a generative AI model and used as training data for the model.

[0329] The improved AI model will be used to generate the next reward plan.

[0330] Example 2

[0331] 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."

[0332] Current hometown tax donation systems do not adequately provide personalized suggestions that take into account users' preferences and emotional state. This makes it difficult for users to find the perfect gift from the many options available, resulting in a lack of improvement in the user experience. Furthermore, the lack of effective feedback collection and improvement of the next proposal makes it difficult to continuously improve user satisfaction.

[0333] 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.

[0334] In this invention, the server includes means for collecting user transaction history data and location data, means for analyzing the user transaction history data and location data to generate a user preference profile, means for analyzing the user's facial expression data, voice data, and character input data to recognize the user's emotional state, means for proposing multiple gift plans generated based on the preference profile and emotional state, means for presenting the gift plans to the user terminal and allowing the user to apply for a donation for the gift selected by the user, and means for collecting feedback on the gift from the user to improve the accuracy of the next proposal. This not only makes it possible to propose the most suitable gift plan for the user, but also makes it possible to continuously improve user satisfaction.

[0335] "Transaction history data" refers to data that includes detailed information about purchases and payments made by a user in the past.

[0336] "Location data" is data that includes information about a user's geographic location, such as where the user lives or where the user visits.

[0337] A "preference profile" is information that indicates a user's preferences, interests, and concerns, and is generated by analyzing the user's transaction history data and location data.

[0338] "Facial expression data" is data that includes information about the user's facial expression.

[0339] "Voice data" is data that includes information about the voice uttered by the user.

[0340] "Character input data" refers to data that includes character information input by a user via a keyboard or touch screen.

[0341] "Emotional state" is information indicating the emotional state of the user that is recognized by analyzing facial expression data, voice data, and character input data.

[0342] A "return gift plan" is a specific option for a return gift for hometown tax donations that is proposed to the user.

[0343] "Feedback" is information indicating opinions and impressions, such as ratings and comments, regarding the gift received by the user.

[0344] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate the optimal reward plan for each user.

[0345] A "prompt" is input text that is fed into a generative AI model to instruct it on a specific task.

[0346] The present invention is a system that collects and analyzes a user's transaction history data and location data to provide a personalized gift plan suited to the user, and further proposes a gift that takes into account the user's emotional state using an emotion engine. This system includes a server, a user terminal, a generative AI model, and an emotion engine. Specific embodiments are described below.

[0347] The server first collects the user's transaction history data and location data through the API. The transaction history data includes payment history, purchase history, etc., and this data is stored in a database such as MySQL or PostgreSQL. For example, the server collects and analyzes data such as the user's past purchases of luxury ingredients and local specialties.

[0348] The server then analyzes the collected transaction history data and location data to generate a user preference profile. This analysis is performed using data analysis tools such as the Python pandas library and SQL queries. The analysis results are saved in JSON format and stored on the server.

[0349] The emotion engine acquires facial expression data, voice data, and text input data from the user's device. This data is collected through hardware such as a camera and microphone. The server analyzes this data using an emotion analysis library such as Microsoft Azure's Cognitive Services to recognize the user's emotional state. For example, it recognizes facial expressions while the user is operating the app and infers emotions from voice input.

[0350] Next, the server uses a generative AI model to generate a gift plan suited to the user based on the preference profile and the recognition results of the emotion engine. The generative AI model uses machine learning algorithms and cloud-based AI services. The AI ​​model operates based on the prompt text, and generates a gift plan for sake based on the fact that "User A is interested in sake and his current emotional state is positive."

[0351] The user's device receives the gift plans sent from the server and presents them to the user within the app. Each gift plan is displayed in detail in a format that reflects the user's emotional state. For example, the device might display plans such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture."

[0352] Users select the desired gift plan from the presented plans and complete the donation application process within the app. This includes entering the donation amount, shipping information, and confirmation. The user's device will then complete a set procedure and send the information to the server, completing the donation application.

[0353] In addition, users can enter ratings and comments about the gifts they receive and send the feedback to the server. The server analyzes this feedback and uses it to improve the accuracy of the next gift plan generation. For example, if the gift is highly rated, the server can use that information to adjust the algorithm of the AI ​​model for generation.

[0354] As a result, the system of the present invention can realize personalized hometown tax return gift suggestions that take into account the user's emotional state, thereby continuously improving user satisfaction.

[0355] Specific prompt examples:

[0356] 1. "Please generate a recommended gift plan for user A based on their donation history."

[0357] 2. "Analyze User A's latest emotional state and provide personalized suggestions based on it."

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Program processing flow

[0360] Step 1:

[0361] User Data Collection

[0362] Subject: Server

[0363] The server collects user transaction history data and location data through the API. This data includes payment history, purchase history, and location information. This data is stored in a database such as MySQL or PostgreSQL. Specifically, the server uses the REST API to obtain data on products the user has purchased in the past and saves it in the database as entries including date, time, category, and location information.

[0364] Input: Raw transaction history and location data obtained via API

[0365] Output: Structured data stored in a database

[0366] Step 2:

[0367] Generating a preference profile

[0368] Subject: Server

[0369] The server analyzes the collected transaction history data and location data to generate a user preference profile. The analysis uses Python's pandas library to calculate the purchase frequency for each category. The results are saved in JSON format. Specifically, the number of purchases and purchase amounts for each category are tallied to generate each user's preference profile.

[0370] Input: Transaction history data and location data stored in a database

[0371] Output: User preference profile in JSON format

[0372] Step 3:

[0373] Recognition of emotional states

[0374] Subject: Server

[0375] The server uses an emotion engine to analyze facial expression data, voice data, and text input data sent from the user's device. It uses Microsoft Azure's Cognitive Services to determine the user's emotional state from their facial expression. Specifically, it analyzes captured image and voice data and generates emotion labels such as positive, negative, and neutral.

[0376] Input: Facial expression data, voice data, and text input data sent from the user device

[0377] Output: Emotional state data in JSON format

[0378] Step 4:

[0379] Generate a gift plan

[0380] Subject: Server

[0381] The server uses a generative AI model to generate a gift plan suited to the user based on their preference profile and emotional state data. During this process, a machine learning algorithm is used to analyze the data and generate multiple gift plans. Specifically, based on the prompt text, the AI ​​model creates a plan that recommends gifts that best suit the user's preferences and current emotional state.

[0382] Input: Preference profile, emotional state data

[0383] Output: Multiple gift plans generated by the generative AI model

[0384] Step 5:

[0385] Presentation of return gift plan

[0386] Subject: Terminal

[0387] The user's device receives the gift plans sent from the server and presents them to the user within the app. The plans are displayed in detail in a format that corresponds to the user's emotional state. Specifically, a UI framework is used to display an image and description of each gift plan on the screen.

[0388] Input: Gift plan received from the server

[0389] Output: Gift plan displayed on the user's device screen

[0390] Step 6:

[0391] Selecting a reward plan and applying

[0392] Subject: User

[0393] Users select the desired gift plan from the presented options and complete the donation application process within the app. They enter the necessary information and confirm it on the confirmation screen to complete the application. Specifically, they enter the donation amount and shipping address information, and then confirm it.

[0394] Input: The gift plan presented, the donation amount and shipping information entered by the user

[0395] Output: Donation request data sent to the server

[0396] Step 7:

[0397] Collecting feedback

[0398] Subject: User

[0399] Users can enter their ratings and comments about the gifts they receive in the app. Specifically, they enter their opinions and thoughts in the rating form and press the submit button.

[0400] Input: Ratings and comments entered by users

[0401] Output: Feedback data sent to the server

[0402] Step 8:

[0403] Analyze feedback and improve your next plan

[0404] Subject: Server

[0405] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine. This improves the accuracy of generating the next reward plan. Specifically, it analyzes the evaluation content using natural language processing and adjusts the model parameters.

[0406] Input: Feedback data

[0407] Output: Next reward plan with improved generative AI model and emotion engine

[0408] The above processing steps make it possible to propose personalized gift plans based on the user's preferences and emotional state, thereby improving user satisfaction.

[0409] (Application example 2)

[0410] 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."

[0411] Conventional hometown tax donation systems often offer a uniform gift proposal without considering the user's preferences or emotional state, resulting in low user satisfaction. Furthermore, proposals based solely on transaction history and location data are unable to provide detailed proposals that reflect the user's emotional state. Therefore, there is a need for a system that offers personalized gift proposals and application procedures that take the user's emotional state into account.

[0412] The identification process by the identification 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 collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for recognizing the user's emotional state, and means for proposing multiple personalized return gifts to the user based on the emotional state and preference profile. This enables detailed suggestions that take the user's emotional state into consideration.

[0413] "Transaction history data" refers to data that records the purchases and payments that a user has made in the past.

[0414] A "preference profile" is a collection of information indicating a user's preferences and interests, obtained by analyzing the user's transaction history data.

[0415] The "emotional state" is a state that indicates the user's current emotions and mood, and is information acquired through facial expression recognition and voice analysis.

[0416] "Personalized offers" refer to personalized offers of rewards or services that are tailored to a user based on that user's preference profile and emotional state.

[0417] "Feedback" refers to evaluations and comments on the gift provided by the user, and is information used to improve the accuracy of suggestions next time.

[0418] "Location data" is data that indicates the user's current location and past travel routes.

[0419] The "donation application procedure" is a procedure for officially making a donation for the return gift selected by the user.

[0420] The "donation limit" refers to the upper limit of donations set when a user receives tax benefits.

[0421] "Documents" refers to official documents, consent forms, etc. required for donation applications.

[0422] "Progress" is information that indicates the current status or stage of the donation application process.

[0423] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[0424] Program processing explanation

[0425] Hardware and Software

[0426] 1. Server:

[0427] Collected Data: Collects user transaction history data and location data.

[0428] Analytical Data: Collected data is analyzed to generate a profile of your preferences.

[0429] Emotion engine: Analyzes the user's facial expression and voice data to recognize their emotional state.

[0430] Generative AI model: Generates personalized reward offers based on the user's preference profile and emotional state.

[0431] 2. User Device:

[0432] Received plan: Receives the reward plan sent from the server and presents it to the user within the app.

[0433] Selection procedure: Select the desired reward plan from the presented plans.

[0434] Application process: Complete the donation application process within the app.

[0435] Feedback: Enter your feedback on the proposed reward.

[0436] Data processing and calculation

[0437] 1. User Data Collection and Analysis:

[0438] The server collects the user's transaction history and location data through API calls, and uses the collected data to create a user preference profile. For example, it analyzes data such as the user's past purchases of luxury ingredients or local specialties to identify the user's interests.

[0439] 2. Recognition of emotional states:

[0440] The server uses an emotion engine to analyze the user's facial expression and voice data. It recognizes the user's current emotional state and uses it, along with their preference profile, to generate a reward plan. For example, if the user is smiling while using the app, it is determined to be in a positive emotional state.

[0441] 3. Generate personalized reward plans:

[0442] The server uses the generative AI model to generate multiple gift plans suitable for the user based on their preference profile and emotional state. For example, if the user is interested in sake and their current emotional state is positive, the server generates gift plans for sake offered by local governments across the country.

[0443] Examples of concrete examples and prompts

[0444] Examples:

[0445] Usage: Proposing a cashback campaign to improve the hometown tax donation experience on smartphone apps.

[0446] Input: transaction history, location, facial expression images, audio clips.

[0447] Output: A reward plan that matches the user's preferences and emotional state.

[0448] Example prompts to be input to the generative AI model

[0449] text

[0450] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[0451] User data: {user_data}

[0452] Emotional state: {emotion}

[0453] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Step 1:

[0456] The server collects user transaction history data and location data through API calls. This includes the user's past purchases and payments, as well as current and past location information. The collected data is stored in a database. The input is the user ID, and the output is the transaction history data and location data.

[0457] Step 2:

[0458] The server analyzes the collected transaction history data and generates a user preference profile. For example, it analyzes whether the user likes a particular category of products or which services they frequently use. The input is the transaction history data, and the output is the user preference profile.

[0459] Step 3:

[0460] The user terminal collects the user's facial expression and voice data through a camera and microphone. These data are transmitted to the server. The input is the user's facial expression image and voice clip, and the output is the transmitted data.

[0461] Step 4:

[0462] The server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This determines whether the emotion is positive or negative based on factors such as smiles and tone of voice. The input is facial expression images and voice data, and the output is the recognized emotional state.

[0463] Step 5:

[0464] The server uses a generative AI model to generate personalized gift plans based on the user's preference profile and emotional state. For example, if a user is interested in sake and has a positive emotional state, multiple gift plans for sake are created. The input is the user's preference profile and emotional state, and the output is the gift plans.

[0465] Step 6:

[0466] The user device receives the gift plan sent from the server and presents it to the user within the app. The details of the gift plan are displayed on the screen in a design that corresponds to the user's emotional state. The input is the gift plan, and the output is the information presented to the user.

[0467] Step 7:

[0468] The user selects the desired plan from the presented gift plans and completes the donation application process within the app. Based on the selected plan, the user enters the required information. The input is the selected gift plan and application information, and the output is application completion information.

[0469] Step 8:

[0470] The server collects feedback from users. The feedback includes ratings and comments about the rewards. The collected feedback data is used to improve the accuracy of the next proposal. The input is feedback information, and the output is improvement data for the next proposal.

[0471] Step 9:

[0472] The server analyzes the collected feedback data and reflects it in the generative AI model and emotion engine. This improves the accuracy and quality of the next gift plan generation. The input is the feedback data, and the output is improved model and engine parameters.

[0473] (Example of a prompt)

[0474] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[0475] User data: {user_data}

[0476] Emotional state: {emotion}

[0477] 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.

[0478] 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.

[0479] 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.

[0480] [Second embodiment]

[0481] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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."

[0493] The system of the present invention collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. The system includes a server, a user terminal, and a generative AI model.

[0494] Program processing

[0495] 1. Collection and analysis of user data

[0496] Subject: Server

[0497] The server collects the user's transaction history data. Specifically, the server captures the user's payment history, residential location information, and in-app behavior history, and stores this information in a database. The server then analyzes this data to generate a user preference profile. A generative AI model is used to identify personalized preferences based on the user's purchase history, product categories of interest, and other factors.

[0498] Examples:

[0499] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients.

[0500] 2. Generate personalized reward plans

[0501] Subject: Server

[0502] The server generates multiple gift plans based on the preference profile output by the generative AI model. This includes logic for selecting gifts that match the user's donation limit and preferences. To maximize user satisfaction, the server also takes past feedback data into account and suggests the most likely options for the next time.

[0503] Examples:

[0504] The server generates a gift plan for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen."

[0505] 3. Present a gift plan

[0506] Subject: Terminal

[0507] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the screen.

[0508] Examples:

[0509] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," including "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo."

[0510] 4. Select a gift plan and apply

[0511] Subject: User

[0512] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[0513] Examples:

[0514] User B selects "Niigata Prefecture Junmai Sake," enters the donation amount and shipping information within the app, and completes the application process with just a few clicks.

[0515] 5. Gather feedback and improve your next plan

[0516] Subject: Server

[0517] The server collects feedback about the gifts received by the user, including ratings and comments made by the user about the gifts, and uses this feedback when generating the next plan to help provide a plan that better matches the user's preferences.

[0518] Examples:

[0519] If user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the server will reflect this feedback in the next plan generation and suggest a more accurate sake plan to the user.

[0520] With this configuration, the present invention can realize a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[0521] The processing flow will be explained below.

[0522] Step 1: Collect user data

[0523] Subject: Server

[0524] The server accesses a database to collect user transaction history data, including the user's past payment history, residential location information, and in-app activity history. The server periodically checks this information to obtain the latest data.

[0525] Specific behavior:

[0526] The server executes an SQL query to retrieve the most recent payment history from the transaction history table.

[0527] Get registered address and GPS data from the user profile table.

[0528] Analyze log data of page visits and product views within the app.

[0529] Step 2: Generate a preference profile

[0530] Subject: Server

[0531] The server inputs the collected data into a generative AI model to generate a user preference profile, which involves analyzing patterns of the user's past purchases and pages viewed.

[0532] Specific behavior:

[0533] The server preprocesses the collected data to match the input format of the generative AI model.

[0534] The preprocessed data is fed into an AI model to generate a preference profile.

[0535] The generated preference profile is linked to the user information in the database and saved.

[0536] Step 3: Create a reward plan

[0537] Subject: Server

[0538] The server generates a reward plan suitable for the user based on the preference profile and the donation limit, which includes a process of suggesting multiple reward options.

[0539] Specific behavior:

[0540] The server filters the reward information in the database based on the preference profile data.

[0541] A gift is selected from the search results so that it falls within the user's donation limit.

[0542] A number of plans are generated and a list is created to be proposed to the user.

[0543] Step 4: Present your gift plan

[0544] Subject: Terminal

[0545] The terminal displays the gift plan sent from the server on the user interface, and the user can view the details of the proposed plan on the screen.

[0546] Specific behavior:

[0547] The terminal parses the received gift plan data.

[0548] Bind the parsed data to a UI component to display the details.

[0549] Build an interface for users to view plan details.

[0550] Step 5: Select a reward plan and apply

[0551] Subject: User

[0552] Users can select the desired plan from the multiple gift plans presented, and then complete the donation application process within the app.

[0553] Specific behavior:

[0554] The user selects a desired plan from the displayed plans.

[0555] Follow the instructions on the device to enter your donation amount and shipping information.

[0556] Tap the Apply button, confirm all the information, and then complete the process.

[0557] Step 6: Gather feedback

[0558] Subject: User

[0559] Users can enter their ratings and comments on the gifts they receive, and this feedback will be used to generate the next plan.

[0560] Specific behavior:

[0561] Users can enter feedback about the gift through a rating form.

[0562] The terminal transmits the input feedback to the server.

[0563] Step 7: Analyze feedback and improve your next plan

[0564] Subject: Server

[0565] The server analyzes the received feedback and reflects it in the generative AI model, thereby improving the accuracy of the next plan generation.

[0566] Specific behavior:

[0567] The server stores the feedback data in a database.

[0568] The saved data is input into a generative AI model and used as training data for the model.

[0569] The improved model is used to generate the next reward plan.

[0570] Example 1

[0571] 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."

[0572] The problem with hometown tax donations is that the process of users selecting a donation recipient and gift in return is complicated and requires a great deal of time and effort. It can also be difficult to find a gift that matches a user's preferences, which can result in lower satisfaction. Additionally, the criteria for selecting the best gift from the wide variety of gifts available are unclear, making it difficult for users to make an appropriate choice.

[0573] 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.

[0574] In this invention, the server includes a means for collecting user transaction history, a means for analyzing the transaction history to generate a user preference profile, and a means for generating multiple return gift plans based on the preference profile. This allows users to easily select the optimal return gift plan based on their preferences, simplifies the donation application process, and further allows for feedback to be collected to improve the accuracy of future proposals.

[0575] "Means for collecting user transaction history" refers to a device or program for acquiring and recording data such as a user's past purchase history, payment history, and residential information.

[0576] The "means for generating a user preference profile" is a device or program for analyzing collected user transaction history data and identifying the user's purchasing tendencies and preferences.

[0577] The "means for generating multiple return gift plans" is a device or program for proposing multiple return gifts that are optimal for a user based on the user's preference profile.

[0578] "Means for displaying the generated gift plan on the user's terminal" refers to a device or program for transmitting the gift plan generated by the server to the user's terminal and displaying it on the screen.

[0579] The "means for carrying out donation application procedures" refers to a device or program for carrying out the necessary donation application for the return gift plan selected by the user.

[0580] The "means for collecting feedback" is a device or program for obtaining and collecting feedback such as ratings and comments on the return gift from users.

[0581] The "means for improving the accuracy of the next proposal" is a device or program that analyzes the collected feedback and reflects it when generating the next return gift plan, thereby making proposals that are more in line with the user's preferences.

[0582] The "means for collecting user location data" refers to a device or program for acquiring and recording location information such as the user's current location and past movement history.

[0583] The "means for calculating the maximum donation amount" is a device or program for calculating the maximum amount that a user can donate based on the collected location data and transaction history data.

[0584] The "means for generating documents required for donation applications" refers to a device or program that automatically generates the documents and forms required when a user applies for a donation.

[0585] "Means for updating the progress of a donation request" refers to a device or program for managing and updating the progress of a donation request (e.g., application in progress, completed) based on the gift plan selected by the user.

[0586] The present invention is a system that collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. This system includes a server, a user terminal, and a generative AI model.

[0587] The server collects the user's transaction history data. Specifically, the server captures information such as the user's payment history, location information, and in-app behavior history, and stores this data in a database. The server then analyzes this data to generate a user preference profile. A generative AI model (e.g., OpenAI's GPT-4) is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[0588] Examples:

[0589] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in local specialties and luxury ingredients. An example of a prompt sentence is input to the generative AI model: "Analyze the user's transaction history data and generate a food preference profile."

[0590] The server then generates multiple gift plans based on the preference profile output by the generative AI model. The server then executes logic to select gifts that fit the user's donation limit and preferences, and takes past feedback data into account to suggest the most likely options for the next time, thereby maximizing user satisfaction.

[0591] Examples:

[0592] The server generates gift plans for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen." As an example of a prompt, the server inputs the instruction "Generate an appropriate gift plan based on User A's preference profile" into the generation AI model.

[0593] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the device, allowing the user to select one.

[0594] Examples:

[0595] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," such as "Junmai sake from Niigata Prefecture" and "Daiginjo from Yamagata Prefecture." As an example of a prompt, the instruction "Display detailed information about the proposed return gift plan" is input to the generative AI model.

[0596] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[0597] Examples:

[0598] User B selects "Junmai sake from Niigata Prefecture," enters the donation amount and shipping information in the app, and completes the application process with just a few clicks. An example of a prompt sentence is input to the generative AI model: "Complete the donation application process according to the gift plan you selected."

[0599] Finally, the server collects feedback about the gifts received by the user, including ratings and comments the user makes about the gifts, and uses this feedback to generate the next plan to help provide a plan that better matches the user's preferences.

[0600] Examples:

[0601] If User C gives a high rating to the "Niigata Prefecture Junmai Sake" that he received, the server will reflect this feedback in the next plan generation and propose a more accurate sake plan to the user. An example of a prompt sentence is to input the instruction "Collect feedback about the gift received and use it to generate the next plan" into the generative AI model.

[0602] With this configuration, the present invention enables a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[0603] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0604] Step 1:

[0605] The server collects the user's transaction history. First, it calls an API to obtain the user's past purchase history data from the payment system and stores the results in a database. Next, it obtains the user's residential location information and stores it in the database as well. It also analyzes the user's behavioral history within the app and saves it in the database as transaction history data.

[0606] Input: User purchase history, payment history, residential location information, in-app behavior history

[0607] Output: Collected transaction history data stored in a database

[0608] Specific operation: The server calls the API to obtain PayPal and credit card payment history and stores it in a database within the system.

[0609] Step 2:

[0610] The server analyzes the collected transaction history data to generate a user preference profile. Using a generative AI model (e.g., OpenAI's GPT-4), it analyzes the transaction history retrieved from the database to identify the user's purchasing trends and product categories that indicate their interests.

[0611] Input: Transaction history data

[0612] Output: User preference profile

[0613] Specific operation: The server inputs the transaction history data into the generation AI model and generates a profile using the prompt sentence, "Analyze the user's transaction history data and generate a food preference profile."

[0614] Step 3:

[0615] The server generates multiple gift plans based on the generated preference profile, and uses a generative AI model to create the optimal gift plan, taking into account the user's donation limit and past feedback data.

[0616] Inputs: Preference profile, donation limit, feedback data

[0617] Output: Personalized gift plan

[0618] Specific operation: The server inputs a prompt statement such as "Generate an appropriate gift plan based on user A's preference profile" into the generative AI model and combines gift plans.

[0619] Step 4:

[0620] The user's device receives the gift plan data sent from the server and displays it within the app, allowing the user to check detailed information about the gift plan.

[0621] Input: Gift plan data

[0622] Output: Display of reward plan on the app screen

[0623] Specific operation: The device parses the data sent from the server in JSON format and displays detailed information such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture" on the screen.

[0624] Step 5:

[0625] The user selects the desired gift plan from the displayed options and completes the donation application process. The user enters the donation amount and shipping address information and follows the application procedure guide to complete the application.

[0626] Input: User selection, donation amount, shipping information

[0627] Output: Notification of application completion and corresponding data update

[0628] Specific operation: User B selects "Niigata Prefecture Junmai Sake," enters the donation amount of 50,000 yen and shipping information, and clicks the "Application Complete" button.

[0629] Step 6:

[0630] The server collects feedback from users, including ratings and comments on the gifts they received, and stores them in a database as reference information for generating the next gift plan.

[0631] Input: User feedback (ratings, comments)

[0632] Output: Feedback data stored in a database and the next plan generated based on the feedback

[0633] Specific operation: The server obtains the user's rating (e.g., "I gave this Junmai sake from Niigata Prefecture five stars") from the feedback section within the app and reflects it in the next prompt sentence for the generative AI model.

[0634] (Application example 1)

[0635] 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."

[0636] Conventional systems did not adequately personalize products and gifts to match users' preferences, making it difficult to improve the user experience. Additionally, the accuracy of suggestions was low, making it difficult to increase user satisfaction. This made it difficult for users to find the best product for them from the many options available, requiring cumbersome search tasks. Furthermore, there was no system for incorporating feedback into the next suggestions, making it difficult to ensure continued user satisfaction.

[0637] 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.

[0638] In this invention, the server includes means for collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for suggesting multiple gift items or products suitable for the user, means for the user to purchase a gift item or product selected from the suggested gift items or products, and means for collecting feedback from the user on the gift items or products and improving the accuracy of subsequent suggestions. This allows users to easily find products that suit their preferences, improving their purchasing experience. Furthermore, the use of a generative AI model improves the accuracy of data analysis, enabling more personalized suggestions. Furthermore, by incorporating user feedback into subsequent suggestions, continued improvement in user satisfaction can be expected.

[0639] "User transaction history data" refers to information about a user's past purchasing behavior and payments, including details such as product name, purchase date and time, payment amount, and payment method.

[0640] A "preference profile" is a collection of information that indicates a user's preferences and interests, generated based on the user's past behavioral data. This profile includes information such as the user's preferred categories, brands, and price ranges.

[0641] A "return gift" is a product or service that a user can receive when making a donation to a specific service.

[0642] "Product" is a general term for products and services that users can purchase, including physical goods, digital content, and services.

[0643] "Donation Request Process" means the formal process by which a User may make a donation to a particular service or cause, including entering a donation amount and submitting any required documentation.

[0644] "Feedback" refers to the evaluation or comments that users make on the products or gifts they receive, which can help improve the accuracy of future suggestions.

[0645] "Location data" refers to information about a user's current location and past visit history, collected through GPS, Wi-Fi, and base station data.

[0646] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and predicts user preferences and patterns. This model uses machine learning and deep learning techniques.

[0647] A "prompt" is text data that is input to a particular generative AI model and is an instruction that the model uses to generate the appropriate output.

[0648] The system of the present invention collects and analyzes users' transaction history data and location data to propose personalized products and return gifts suitable for the user, simplifying the purchasing process and donation application process. This system includes a server, a user terminal, and a generative AI model. Specific embodiments are described in detail below.

[0649] Collection and analysis of user data

[0650] The server collects data on users' transaction history, location, and purchasing behavior, including payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. The data is then analyzed using Pandas and Scikit-Learn to generate a user preference profile.

[0651] Examples:

[0652] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients. A generative AI model is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[0653] Generate personalized suggestions

[0654] The server generates multiple rewards or product plans based on the preference profile output by the generative AI model. This generative AI model uses machine learning and deep learning technologies to analyze users' purchasing preferences and behavioral patterns.

[0655] Examples:

[0656] Based on the information that "User A is interested in a particular brand and has a budget of 50,000 yen," the server generates a shopping plan that includes information on popular products and special sales from that brand.

[0657] Presenting the proposal

[0658] The user device receives the personalized gift or product plan sent from the server and presents it to the user within the app, where detailed information, ratings, prices, discount information, etc. for each gift or product are displayed.

[0659] Examples:

[0660] The device displays multiple product plans in a section called "Recommended Shopping Plans," such as "luxury bags from specific brands" and "latest smartphone models."

[0661] Purchase or donation process

[0662] Users select the desired gift or product plan from the presented options, and then follow the in-app guide to complete the purchase or donation application process, simplifying the complicated process.

[0663] Examples:

[0664] User B selects "luxury bag from a specific brand," enters the purchase amount and shipping information within the app, and completes the purchase process with just a few clicks.

[0665] Feedback and suggestions for next improvements

[0666] The server collects feedback about the gifts or products received by the user, including ratings and comments the user makes about the products or gifts, and uses this feedback when generating subsequent suggestions to help provide suggestions that better match the user's preferences.

[0667] Examples:

[0668] If user C gives a high rating to a "luxury bag from a specific brand" that he / she purchased, the server will reflect this feedback in the next proposal generation and propose a more accurate plan to the user.

[0669] Prompt Sentence Examples

[0670] The following prompt sentences could be input to the generative AI model:

[0671] "Cluster the patterns in the following data and generate the most suitable product list for a specific profile. Data: purchase history, amount, category. Desired result: product list for a specific profile."

[0672] In this way, the present invention enables personalized offers based on user preferences, improving the purchasing experience and ongoing user satisfaction.

[0673] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0674] Step 1:

[0675] The server collects the user's transaction history data and location data. This input data includes the user's payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. This allows the user's past activity data to be managed centrally.

[0676] Step 2:

[0677] The server analyzes the collected transaction history data and location data to generate a user preference profile. This analysis uses Pandas to process the data and Scikit-Learn to apply machine learning models such as clustering. Based on the input data, the server identifies the user's past preferred categories, brands, and price ranges and outputs them as a preference profile.

[0678] Step 3:

[0679] The server uses a generative AI model to generate multiple rewards or product plans based on the user's preference profile. The prompt is "Please generate a product list that matches a specific preference profile." The generative AI model outputs a list of products or rewards that best fit the user's preference profile.

[0680] Step 4:

[0681] The server sends the generated reward or product plan to the user's device, which receives it and presents it to the user within the app. Input includes a product list, ratings, prices, discount information, etc., which are displayed on the screen for easy understanding by the user.

[0682] Step 5:

[0683] The user selects the desired gift or product plan from the presented gift plans. The user terminal sends information about the selected product or gift to the server. The input data is the product information selected by the user, and the server receives this information and proceeds with the purchase procedure or donation application procedure.

[0684] Step 6:

[0685] The server generates the documents required for the purchase process and sends them to the user. The user can then check the documents sent within the app and complete the process with a few simple operations. The input data includes purchase information and shipping address information, and the documents are generated based on this information.

[0686] Step 7:

[0687] After receiving a product or gift, the user provides feedback within the app. The user's device sends this feedback to the server. The input data is the user's rating and comments, and the server analyzes them and reflects them in the next proposal.

[0688] Step 8:

[0689] The server adjusts the prompts for the generative AI model based on the collected feedback to improve the accuracy of the next recommendation. Specifically, it uses prompts such as, "Please consider the user's feedback and generate a product list that will improve the accuracy of the next recommendation." The generative AI model outputs further suggestions based on the new profile and feedback information.

[0690] Through this series of steps, the system will suggest personalized products and gifts based on the user's preferences, improving the user experience.

[0691] 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.

[0692] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[0693] Program processing

[0694] 1. Collection and analysis of user data

[0695] Subject: Server

[0696] The server collects the user's transaction history data and location data and stores them in a database, including the user's payment history, residential location information, behavioral history, etc. The collected data is then analyzed to generate a user preference profile.

[0697] Examples:

[0698] The server collects data such as luxury ingredients and local specialties purchased by the user in the past, and analyzes whether the user is interested in a particular category.

[0699] 2. Emotional state recognition using an emotion engine

[0700] Subject: Server

[0701] The emotion engine analyzes the user's facial expression data, voice data, and text input data to recognize the user's current emotional state. This recognition result, along with the user's preference profile, is used to generate a reward plan.

[0702] Examples:

[0703] The server recognizes facial expressions while the user is operating the app and estimates emotions from voice input. For example, if the user is smiling, it determines that the user is in a positive emotional state.

[0704] 3. Generate personalized reward plans

[0705] Subject: Server

[0706] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the preference profile and the recognition results of the emotion engine. This involves selecting gifts that match the user's preferences within the user's donation limit and adjusting the plan according to the user's emotional state.

[0707] Examples:

[0708] Based on the fact that "User A is interested in sake and his / her current emotional state is positive," the server generates a gift plan for sake offered by local governments across the country and makes suggestions according to the user's emotional state.

[0709] 4. Present a gift plan

[0710] Subject: Terminal

[0711] The user device receives the gift plans sent from the server and presents them to the user within the app. Details of each gift plan are displayed on the screen in a format that corresponds to the user's emotional state.

[0712] Examples:

[0713] The device displays "recommended hometown tax donation plans" such as "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo" with a UI designed to make users feel emotionally satisfied.

[0714] 5. Select a gift plan and apply

[0715] Subject: User

[0716] Users select the desired gift plan from the presented options and complete the donation application process within the app.

[0717] Examples:

[0718] User B selects "Niigata Prefecture Junmai Sake" and enters the donation amount and shipping address information with simple operations to complete the application process.

[0719] 6. Gathering Feedback

[0720] Subject: User

[0721] The user inputs a rating and comment on the gift they received.

[0722] Examples:

[0723] When user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the feedback data is sent to the server.

[0724] 7. Analyze feedback and improve your next plan

[0725] Subject: Server

[0726] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine, thereby improving the accuracy of the next plan generation.

[0727] Examples:

[0728] Based on the positive feedback, the server will improve the model so that it can propose more accurate sake plans the next time it generates a gift plan.

[0729] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[0730] The processing flow will be explained below.

[0731] Step 1: Collect user data

[0732] Subject: Server

[0733] The server collects user transaction history data and location data, including user payment history, residential location information, and in-app activity history.

[0734] Specific behavior:

[0735] The server runs SQL queries against the transaction history database to retrieve past payment records.

[0736] Registered address and GPS data are retrieved from the user profile database.

[0737] Collect user behavior logs within the app (page visits, purchase history).

[0738] Step 2: Generate a preference profile

[0739] Subject: Server

[0740] The server inputs the collected data into a generative AI model to generate a user preference profile.

[0741] Specific behavior:

[0742] The server preprocesses the collected data and converts it into an input format for the AI ​​model.

[0743] The preprocessed data is input into a generative AI model to analyze user preference patterns.

[0744] The generated preference profile is stored in a database.

[0745] Step 3: Recognizing emotional states using the emotion engine

[0746] Subject: Terminal

[0747] The terminal collects the user's facial expression data, voice data, and character input data and sends them to the emotion engine, which recognizes the user's current emotional state.

[0748] Specific behavior:

[0749] The device uses the front camera to capture the user's facial expressions.

[0750] A microphone is used to record the user's voice and the voice data is sent to the emotion engine.

[0751] Character data is collected from touch and keyboard input and sent to the emotion engine.

[0752] The emotion engine analyzes the received data and recognizes the user's emotional state.

[0753] Step 4: Create a personalized reward plan

[0754] Subject: Server

[0755] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the user's preference profile and the recognition results of the emotion engine.

[0756] Specific behavior:

[0757] The server inputs preference profile data and emotion recognition results into the AI ​​model.

[0758] The AI ​​model searches for rewards that match the user's donation limit and generates a list.

[0759] Create a list of suggested gift plans and send it to the user.

[0760] Step 5: Present your gift plan

[0761] Subject: Terminal

[0762] The terminal receives the return gift plan sent from the server and displays it on the user interface.

[0763] Specific behavior:

[0764] The terminal parses the received gift plan data.

[0765] Bind the parsed data to a UI component to display the details.

[0766] The display format is presented to the user according to the emotional state.

[0767] Step 6: Select a reward plan and apply

[0768] Subject: User

[0769] The user selects the desired plan from the presented return gift plans and completes the donation application procedure.

[0770] Specific behavior:

[0771] The user taps the selection button on the screen to select the desired gift plan.

[0772] Enter your donation amount and shipping information.

[0773] Click the Apply button to complete the process.

[0774] Step 7: Gather feedback

[0775] Subject: User

[0776] The user inputs a rating and comment on the gift they received.

[0777] Specific behavior:

[0778] Users can access the in-app rating form and provide feedback about the gift.

[0779] The terminal transmits the input feedback data to the server.

[0780] Step 8: Analyze feedback and improve your next plan

[0781] Subject: Server

[0782] The server analyzes the collected feedback and applies it to the generative AI model and emotion engine, thereby improving the accuracy of the next reward plan.

[0783] Specific behavior:

[0784] The server stores the feedback data in a database.

[0785] The feedback data is input into a generative AI model and used as training data for the model.

[0786] The improved AI model will be used to generate the next reward plan.

[0787] Example 2

[0788] 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."

[0789] Current hometown tax donation systems do not adequately provide personalized suggestions that take into account users' preferences and emotional state. This makes it difficult for users to find the perfect gift from the many options available, resulting in a lack of improvement in the user experience. Furthermore, the lack of effective feedback collection and improvement of the next proposal makes it difficult to continuously improve user satisfaction.

[0790] 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.

[0791] In this invention, the server includes means for collecting user transaction history data and location data, means for analyzing the user transaction history data and location data to generate a user preference profile, means for analyzing the user's facial expression data, voice data, and character input data to recognize the user's emotional state, means for proposing multiple gift plans generated based on the preference profile and emotional state, means for presenting the gift plans to the user terminal and allowing the user to apply for a donation for the gift selected by the user, and means for collecting feedback on the gift from the user to improve the accuracy of the next proposal. This not only makes it possible to propose the most suitable gift plan for the user, but also makes it possible to continuously improve user satisfaction.

[0792] "Transaction history data" refers to data that includes detailed information about purchases and payments made by a user in the past.

[0793] "Location data" is data that includes information about a user's geographic location, such as where the user lives or where the user visits.

[0794] A "preference profile" is information that indicates a user's preferences, interests, and concerns, and is generated by analyzing the user's transaction history data and location data.

[0795] "Facial expression data" is data that includes information about the user's facial expression.

[0796] "Voice data" is data that includes information about the voice uttered by the user.

[0797] "Character input data" refers to data that includes character information input by a user via a keyboard or touch screen.

[0798] "Emotional state" is information indicating the emotional state of the user that is recognized by analyzing facial expression data, voice data, and character input data.

[0799] A "return gift plan" is a specific option for a return gift for hometown tax donations that is proposed to the user.

[0800] "Feedback" is information indicating opinions and impressions, such as ratings and comments, regarding the gift received by the user.

[0801] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate the optimal reward plan for each user.

[0802] A "prompt" is input text that is fed into a generative AI model to instruct it on a specific task.

[0803] The present invention is a system that collects and analyzes a user's transaction history data and location data to provide a personalized gift plan suited to the user, and further proposes a gift that takes into account the user's emotional state using an emotion engine. This system includes a server, a user terminal, a generative AI model, and an emotion engine. Specific embodiments are described below.

[0804] The server first collects the user's transaction history data and location data through the API. The transaction history data includes payment history, purchase history, etc., and this data is stored in a database such as MySQL or PostgreSQL. For example, the server collects and analyzes data such as the user's past purchases of luxury ingredients and local specialties.

[0805] The server then analyzes the collected transaction history data and location data to generate a user preference profile. This analysis is performed using data analysis tools such as the Python pandas library and SQL queries. The analysis results are saved in JSON format and stored on the server.

[0806] The emotion engine acquires facial expression data, voice data, and text input data from the user's device. This data is collected through hardware such as a camera and microphone. The server analyzes this data using an emotion analysis library such as Microsoft Azure's Cognitive Services to recognize the user's emotional state. For example, it recognizes facial expressions while the user is operating the app and infers emotions from voice input.

[0807] Next, the server uses a generative AI model to generate a gift plan suited to the user based on the preference profile and the recognition results of the emotion engine. The generative AI model uses machine learning algorithms and cloud-based AI services. The AI ​​model operates based on the prompt text, and generates a gift plan for sake based on the fact that "User A is interested in sake and his current emotional state is positive."

[0808] The user's device receives the gift plans sent from the server and presents them to the user within the app. Each gift plan is displayed in detail in a format that reflects the user's emotional state. For example, the device might display plans such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture."

[0809] Users select the desired gift plan from the presented plans and complete the donation application process within the app. This includes entering the donation amount, shipping information, and confirmation. The user's device will then complete a set procedure and send the information to the server, completing the donation application.

[0810] In addition, users can enter ratings and comments about the gifts they receive and send the feedback to the server. The server analyzes this feedback and uses it to improve the accuracy of the next gift plan generation. For example, if the gift is highly rated, the server can use that information to adjust the algorithm of the AI ​​model for generation.

[0811] As a result, the system of the present invention can realize personalized hometown tax return gift suggestions that take into account the user's emotional state, thereby continuously improving user satisfaction.

[0812] Specific prompt examples:

[0813] 1. "Please generate a recommended gift plan for user A based on their donation history."

[0814] 2. "Analyze User A's latest emotional state and provide personalized suggestions based on it."

[0815] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0816] Program processing flow

[0817] Step 1:

[0818] User Data Collection

[0819] Subject: Server

[0820] The server collects user transaction history data and location data through the API. This data includes payment history, purchase history, and location information. This data is stored in a database such as MySQL or PostgreSQL. Specifically, the server uses the REST API to obtain data on products the user has purchased in the past and saves it in the database as entries including date, time, category, and location information.

[0821] Input: Raw transaction history and location data obtained via API

[0822] Output: Structured data stored in a database

[0823] Step 2:

[0824] Generating a preference profile

[0825] Subject: Server

[0826] The server analyzes the collected transaction history data and location data to generate a user preference profile. The analysis uses Python's pandas library to calculate the purchase frequency for each category. The results are saved in JSON format. Specifically, the number of purchases and purchase amounts for each category are tallied to generate each user's preference profile.

[0827] Input: Transaction history data and location data stored in a database

[0828] Output: User preference profile in JSON format

[0829] Step 3:

[0830] Recognition of emotional states

[0831] Subject: Server

[0832] The server uses an emotion engine to analyze facial expression data, voice data, and text input data sent from the user's device. It uses Microsoft Azure's Cognitive Services to determine the user's emotional state from their facial expression. Specifically, it analyzes captured image and voice data and generates emotion labels such as positive, negative, and neutral.

[0833] Input: Facial expression data, voice data, and text input data sent from the user device

[0834] Output: Emotional state data in JSON format

[0835] Step 4:

[0836] Generate a gift plan

[0837] Subject: Server

[0838] The server uses a generative AI model to generate a gift plan suited to the user based on their preference profile and emotional state data. During this process, a machine learning algorithm is used to analyze the data and generate multiple gift plans. Specifically, based on the prompt text, the AI ​​model creates a plan that recommends gifts that best suit the user's preferences and current emotional state.

[0839] Input: Preference profile, emotional state data

[0840] Output: Multiple gift plans generated by the generative AI model

[0841] Step 5:

[0842] Presentation of return gift plan

[0843] Subject: Terminal

[0844] The user's device receives the gift plans sent from the server and presents them to the user within the app. The plans are displayed in detail in a format that corresponds to the user's emotional state. Specifically, a UI framework is used to display an image and description of each gift plan on the screen.

[0845] Input: Gift plan received from the server

[0846] Output: Gift plan displayed on the user's device screen

[0847] Step 6:

[0848] Selecting a reward plan and applying

[0849] Subject: User

[0850] Users select the desired gift plan from the presented options and complete the donation application process within the app. They enter the necessary information and confirm it on the confirmation screen to complete the application. Specifically, they enter the donation amount and shipping address information, and then confirm it.

[0851] Input: The gift plan presented, the donation amount and shipping information entered by the user

[0852] Output: Donation request data sent to the server

[0853] Step 7:

[0854] Collecting feedback

[0855] Subject: User

[0856] Users can enter their ratings and comments about the gifts they receive in the app. Specifically, they enter their opinions and thoughts in the rating form and press the submit button.

[0857] Input: Ratings and comments entered by users

[0858] Output: Feedback data sent to the server

[0859] Step 8:

[0860] Analyze feedback and improve your next plan

[0861] Subject: Server

[0862] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine. This improves the accuracy of generating the next reward plan. Specifically, it analyzes the evaluation content using natural language processing and adjusts the model parameters.

[0863] Input: Feedback data

[0864] Output: Next reward plan with improved generative AI model and emotion engine

[0865] The above processing steps make it possible to propose personalized gift plans based on the user's preferences and emotional state, thereby improving user satisfaction.

[0866] (Application example 2)

[0867] 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."

[0868] Conventional hometown tax donation systems often offer a uniform gift proposal without considering the user's preferences or emotional state, resulting in low user satisfaction. Furthermore, proposals based solely on transaction history and location data are unable to provide detailed proposals that reflect the user's emotional state. Therefore, there is a need for a system that offers personalized gift proposals and application procedures that take the user's emotional state into account.

[0869] The identification process by the identification 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 collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for recognizing the user's emotional state, and means for proposing multiple personalized return gifts to the user based on the emotional state and preference profile. This enables detailed suggestions that take the user's emotional state into consideration.

[0870] "Transaction history data" refers to data that records the purchases and payments that a user has made in the past.

[0871] A "preference profile" is a collection of information indicating a user's preferences and interests, obtained by analyzing the user's transaction history data.

[0872] The "emotional state" is a state that indicates the user's current emotions and mood, and is information acquired through facial expression recognition and voice analysis.

[0873] "Personalized offers" refer to personalized offers of rewards or services that are tailored to a user based on that user's preference profile and emotional state.

[0874] "Feedback" refers to evaluations and comments on the gift provided by the user, and is information used to improve the accuracy of suggestions next time.

[0875] "Location data" is data that indicates the user's current location and past travel routes.

[0876] The "donation application procedure" is a procedure for officially making a donation for the return gift selected by the user.

[0877] The "donation limit" refers to the upper limit of donations set when a user receives tax benefits.

[0878] "Documents" refers to official documents, consent forms, etc. required for donation applications.

[0879] "Progress" is information that indicates the current status or stage of the donation application process.

[0880] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[0881] Program processing explanation

[0882] Hardware and Software

[0883] 1. Server:

[0884] Collected Data: Collects user transaction history data and location data.

[0885] Analytical Data: Collected data is analyzed to generate a profile of your preferences.

[0886] Emotion engine: Analyzes the user's facial expression and voice data to recognize their emotional state.

[0887] Generative AI model: Generates personalized reward offers based on the user's preference profile and emotional state.

[0888] 2. User Device:

[0889] Received plan: Receives the reward plan sent from the server and presents it to the user within the app.

[0890] Selection procedure: Select the desired reward plan from the presented plans.

[0891] Application process: Complete the donation application process within the app.

[0892] Feedback: Enter your feedback on the proposed reward.

[0893] Data processing and calculation

[0894] 1. User Data Collection and Analysis:

[0895] The server collects the user's transaction history and location data through API calls, and uses the collected data to create a user preference profile. For example, it analyzes data such as the user's past purchases of luxury ingredients or local specialties to identify the user's interests.

[0896] 2. Recognition of emotional states:

[0897] The server uses an emotion engine to analyze the user's facial expression and voice data. It recognizes the user's current emotional state and uses it, along with their preference profile, to generate a reward plan. For example, if the user is smiling while using the app, it is determined to be in a positive emotional state.

[0898] 3. Generate personalized reward plans:

[0899] The server uses the generative AI model to generate multiple gift plans suitable for the user based on their preference profile and emotional state. For example, if the user is interested in sake and their current emotional state is positive, the server generates gift plans for sake offered by local governments across the country.

[0900] Examples of concrete examples and prompts

[0901] Examples:

[0902] Usage: Proposing a cashback campaign to improve the hometown tax donation experience on smartphone apps.

[0903] Input: transaction history, location, facial expression images, audio clips.

[0904] Output: A reward plan that matches the user's preferences and emotional state.

[0905] Example prompts to be input to the generative AI model

[0906] text

[0907] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[0908] User data: {user_data}

[0909] Emotional state: {emotion}

[0910] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[0911] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0912] Step 1:

[0913] The server collects user transaction history data and location data through API calls. This includes the user's past purchases and payments, as well as current and past location information. The collected data is stored in a database. The input is the user ID, and the output is the transaction history data and location data.

[0914] Step 2:

[0915] The server analyzes the collected transaction history data and generates a user preference profile. For example, it analyzes whether the user likes a particular category of products or which services they frequently use. The input is the transaction history data, and the output is the user preference profile.

[0916] Step 3:

[0917] The user terminal collects the user's facial expression and voice data through a camera and microphone. These data are transmitted to the server. The input is the user's facial expression image and voice clip, and the output is the transmitted data.

[0918] Step 4:

[0919] The server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This determines whether the emotion is positive or negative based on factors such as smiles and tone of voice. The input is facial expression images and voice data, and the output is the recognized emotional state.

[0920] Step 5:

[0921] The server uses a generative AI model to generate personalized gift plans based on the user's preference profile and emotional state. For example, if a user is interested in sake and has a positive emotional state, multiple gift plans for sake are created. The input is the user's preference profile and emotional state, and the output is the gift plans.

[0922] Step 6:

[0923] The user device receives the gift plan sent from the server and presents it to the user within the app. The details of the gift plan are displayed on the screen in a design that corresponds to the user's emotional state. The input is the gift plan, and the output is the information presented to the user.

[0924] Step 7:

[0925] The user selects the desired plan from the presented gift plans and completes the donation application process within the app. Based on the selected plan, the user enters the required information. The input is the selected gift plan and application information, and the output is application completion information.

[0926] Step 8:

[0927] The server collects feedback from users. The feedback includes ratings and comments about the rewards. The collected feedback data is used to improve the accuracy of the next proposal. The input is feedback information, and the output is improvement data for the next proposal.

[0928] Step 9:

[0929] The server analyzes the collected feedback data and reflects it in the generative AI model and emotion engine. This improves the accuracy and quality of the next gift plan generation. The input is the feedback data, and the output is improved model and engine parameters.

[0930] (Example of a prompt)

[0931] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[0932] User data: {user_data}

[0933] Emotional state: {emotion}

[0934] 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.

[0935] 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.

[0936] 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.

[0937] [Third embodiment]

[0938] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0939] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0940] 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).

[0941] 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.

[0942] 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.

[0943] 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).

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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."

[0950] The system of the present invention collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. The system includes a server, a user terminal, and a generative AI model.

[0951] Program processing

[0952] 1. Collection and analysis of user data

[0953] Subject: Server

[0954] The server collects the user's transaction history data. Specifically, the server captures the user's payment history, residential location information, and in-app behavior history, and stores this information in a database. The server then analyzes this data to generate a user preference profile. A generative AI model is used to identify personalized preferences based on the user's purchase history, product categories of interest, and other factors.

[0955] Examples:

[0956] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients.

[0957] 2. Generate personalized reward plans

[0958] Subject: Server

[0959] The server generates multiple gift plans based on the preference profile output by the generative AI model. This includes logic for selecting gifts that match the user's donation limit and preferences. To maximize user satisfaction, the server also takes past feedback data into account and suggests the most likely options for the next time.

[0960] Examples:

[0961] The server generates a gift plan for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen."

[0962] 3. Present a gift plan

[0963] Subject: Terminal

[0964] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the screen.

[0965] Examples:

[0966] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," including "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo."

[0967] 4. Select a gift plan and apply

[0968] Subject: User

[0969] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[0970] Examples:

[0971] User B selects "Niigata Prefecture Junmai Sake," enters the donation amount and shipping information within the app, and completes the application process with just a few clicks.

[0972] 5. Gather feedback and improve your next plan

[0973] Subject: Server

[0974] The server collects feedback about the gifts received by the user, including ratings and comments made by the user about the gifts, and uses this feedback when generating the next plan to help provide a plan that better matches the user's preferences.

[0975] Examples:

[0976] If user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the server will reflect this feedback in the next plan generation and suggest a more accurate sake plan to the user.

[0977] With this configuration, the present invention can realize a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[0978] The processing flow will be explained below.

[0979] Step 1: Collect user data

[0980] Subject: Server

[0981] The server accesses a database to collect user transaction history data, including the user's past payment history, residential location information, and in-app activity history. The server periodically checks this information to obtain the latest data.

[0982] Specific behavior:

[0983] The server executes an SQL query to retrieve the most recent payment history from the transaction history table.

[0984] Get registered address and GPS data from the user profile table.

[0985] Analyze log data of page visits and product views within the app.

[0986] Step 2: Generate a preference profile

[0987] Subject: Server

[0988] The server inputs the collected data into a generative AI model to generate a user preference profile, which involves analyzing patterns of the user's past purchases and pages viewed.

[0989] Specific behavior:

[0990] The server preprocesses the collected data to match the input format of the generative AI model.

[0991] The preprocessed data is fed into an AI model to generate a preference profile.

[0992] The generated preference profile is linked to the user information in the database and saved.

[0993] Step 3: Create a reward plan

[0994] Subject: Server

[0995] The server generates a reward plan suitable for the user based on the preference profile and the donation limit, which includes a process of suggesting multiple reward options.

[0996] Specific behavior:

[0997] The server filters the reward information in the database based on the preference profile data.

[0998] A gift is selected from the search results so that it falls within the user's donation limit.

[0999] A number of plans are generated and a list is created to be proposed to the user.

[1000] Step 4: Present your gift plan

[1001] Subject: Terminal

[1002] The terminal displays the gift plan sent from the server on the user interface, and the user can view the details of the proposed plan on the screen.

[1003] Specific behavior:

[1004] The terminal parses the received gift plan data.

[1005] Bind the parsed data to a UI component to display the details.

[1006] Build an interface for users to view plan details.

[1007] Step 5: Select a reward plan and apply

[1008] Subject: User

[1009] Users can select the desired plan from the multiple gift plans presented, and then complete the donation application process within the app.

[1010] Specific behavior:

[1011] The user selects a desired plan from the displayed plans.

[1012] Follow the instructions on the device to enter your donation amount and shipping information.

[1013] Tap the Apply button, confirm all the information, and then complete the process.

[1014] Step 6: Gather feedback

[1015] Subject: User

[1016] Users can enter their ratings and comments on the gifts they receive, and this feedback will be used to generate the next plan.

[1017] Specific behavior:

[1018] Users can enter feedback about the gift through a rating form.

[1019] The terminal transmits the input feedback to the server.

[1020] Step 7: Analyze feedback and improve your next plan

[1021] Subject: Server

[1022] The server analyzes the received feedback and reflects it in the generative AI model, thereby improving the accuracy of the next plan generation.

[1023] Specific behavior:

[1024] The server stores the feedback data in a database.

[1025] The saved data is input into a generative AI model and used as training data for the model.

[1026] The improved model is used to generate the next reward plan.

[1027] Example 1

[1028] 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."

[1029] The problem with hometown tax donations is that the process of users selecting a donation recipient and gift in return is complicated and requires a great deal of time and effort. It can also be difficult to find a gift that matches a user's preferences, which can result in lower satisfaction. Additionally, the criteria for selecting the best gift from the wide variety of gifts available are unclear, making it difficult for users to make an appropriate choice.

[1030] 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.

[1031] In this invention, the server includes a means for collecting user transaction history, a means for analyzing the transaction history to generate a user preference profile, and a means for generating multiple return gift plans based on the preference profile. This allows users to easily select the optimal return gift plan based on their preferences, simplifies the donation application process, and further allows for feedback to be collected to improve the accuracy of future proposals.

[1032] "Means for collecting user transaction history" refers to a device or program for acquiring and recording data such as a user's past purchase history, payment history, and residential information.

[1033] The "means for generating a user preference profile" is a device or program for analyzing collected user transaction history data and identifying the user's purchasing tendencies and preferences.

[1034] The "means for generating multiple return gift plans" is a device or program for proposing multiple return gifts that are optimal for a user based on the user's preference profile.

[1035] "Means for displaying the generated gift plan on the user's terminal" refers to a device or program for transmitting the gift plan generated by the server to the user's terminal and displaying it on the screen.

[1036] The "means for carrying out donation application procedures" refers to a device or program for carrying out the necessary donation application for the return gift plan selected by the user.

[1037] The "means for collecting feedback" is a device or program for obtaining and collecting feedback such as ratings and comments on the return gift from users.

[1038] The "means for improving the accuracy of the next proposal" is a device or program that analyzes the collected feedback and reflects it when generating the next return gift plan, thereby making proposals that are more in line with the user's preferences.

[1039] The "means for collecting user location data" refers to a device or program for acquiring and recording location information such as the user's current location and past movement history.

[1040] The "means for calculating the maximum donation amount" is a device or program for calculating the maximum amount that a user can donate based on the collected location data and transaction history data.

[1041] The "means for generating documents required for donation applications" refers to a device or program that automatically generates the documents and forms required when a user applies for a donation.

[1042] "Means for updating the progress of a donation request" refers to a device or program for managing and updating the progress of a donation request (e.g., application in progress, completed) based on the gift plan selected by the user.

[1043] The present invention is a system that collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. This system includes a server, a user terminal, and a generative AI model.

[1044] The server collects the user's transaction history data. Specifically, the server captures information such as the user's payment history, location information, and in-app behavior history, and stores this data in a database. The server then analyzes this data to generate a user preference profile. A generative AI model (e.g., OpenAI's GPT-4) is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[1045] Examples:

[1046] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in local specialties and luxury ingredients. An example of a prompt sentence is input to the generative AI model: "Analyze the user's transaction history data and generate a food preference profile."

[1047] The server then generates multiple gift plans based on the preference profile output by the generative AI model. The server then executes logic to select gifts that fit the user's donation limit and preferences, and takes past feedback data into account to suggest the most likely options for the next time, thereby maximizing user satisfaction.

[1048] Examples:

[1049] The server generates gift plans for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen." As an example of a prompt, the server inputs the instruction "Generate an appropriate gift plan based on User A's preference profile" into the generation AI model.

[1050] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the device, allowing the user to select one.

[1051] Examples:

[1052] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," such as "Junmai sake from Niigata Prefecture" and "Daiginjo from Yamagata Prefecture." As an example of a prompt, the instruction "Display detailed information about the proposed return gift plan" is input to the generative AI model.

[1053] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[1054] Examples:

[1055] User B selects "Junmai sake from Niigata Prefecture," enters the donation amount and shipping information in the app, and completes the application process with just a few clicks. An example of a prompt sentence is input to the generative AI model: "Complete the donation application process according to the gift plan you selected."

[1056] Finally, the server collects feedback about the gifts received by the user, including ratings and comments the user makes about the gifts, and uses this feedback to generate the next plan to help provide a plan that better matches the user's preferences.

[1057] Examples:

[1058] If User C gives a high rating to the "Niigata Prefecture Junmai Sake" that he received, the server will reflect this feedback in the next plan generation and propose a more accurate sake plan to the user. An example of a prompt sentence is to input the instruction "Collect feedback about the gift received and use it to generate the next plan" into the generative AI model.

[1059] With this configuration, the present invention enables a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[1060] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1061] Step 1:

[1062] The server collects the user's transaction history. First, it calls an API to obtain the user's past purchase history data from the payment system and stores the results in a database. Next, it obtains the user's residential location information and stores it in the database as well. It also analyzes the user's behavioral history within the app and saves it in the database as transaction history data.

[1063] Input: User purchase history, payment history, residential location information, in-app behavior history

[1064] Output: Collected transaction history data stored in a database

[1065] Specific operation: The server calls the API to obtain PayPal and credit card payment history and stores it in a database within the system.

[1066] Step 2:

[1067] The server analyzes the collected transaction history data to generate a user preference profile. Using a generative AI model (e.g., OpenAI's GPT-4), it analyzes the transaction history retrieved from the database to identify the user's purchasing trends and product categories that indicate their interests.

[1068] Input: Transaction history data

[1069] Output: User preference profile

[1070] Specific operation: The server inputs the transaction history data into the generation AI model and generates a profile using the prompt sentence, "Analyze the user's transaction history data and generate a food preference profile."

[1071] Step 3:

[1072] The server generates multiple gift plans based on the generated preference profile, and uses a generative AI model to create the optimal gift plan, taking into account the user's donation limit and past feedback data.

[1073] Inputs: Preference profile, donation limit, feedback data

[1074] Output: Personalized gift plan

[1075] Specific operation: The server inputs a prompt statement such as "Generate an appropriate gift plan based on user A's preference profile" into the generative AI model and combines gift plans.

[1076] Step 4:

[1077] The user's device receives the gift plan data sent from the server and displays it within the app, allowing the user to check detailed information about the gift plan.

[1078] Input: Gift plan data

[1079] Output: Display of reward plan on the app screen

[1080] Specific operation: The device parses the data sent from the server in JSON format and displays detailed information such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture" on the screen.

[1081] Step 5:

[1082] The user selects the desired gift plan from the displayed options and completes the donation application process. The user enters the donation amount and shipping address information and follows the application procedure guide to complete the application.

[1083] Input: User selection, donation amount, shipping information

[1084] Output: Notification of application completion and corresponding data update

[1085] Specific operation: User B selects "Niigata Prefecture Junmai Sake," enters the donation amount of 50,000 yen and shipping information, and clicks the "Application Complete" button.

[1086] Step 6:

[1087] The server collects feedback from users, including ratings and comments on the gifts they received, and stores them in a database as reference information for generating the next gift plan.

[1088] Input: User feedback (ratings, comments)

[1089] Output: Feedback data stored in a database and the next plan generated based on the feedback

[1090] Specific operation: The server obtains the user's rating (e.g., "I gave this Junmai sake from Niigata Prefecture five stars") from the feedback section within the app and reflects it in the next prompt sentence for the generative AI model.

[1091] (Application example 1)

[1092] 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."

[1093] Conventional systems did not adequately personalize products and gifts to match users' preferences, making it difficult to improve the user experience. Additionally, the accuracy of suggestions was low, making it difficult to increase user satisfaction. This made it difficult for users to find the best product for them from the many options available, requiring cumbersome search tasks. Furthermore, there was no system for incorporating feedback into the next suggestions, making it difficult to ensure continued user satisfaction.

[1094] 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.

[1095] In this invention, the server includes means for collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for suggesting multiple gift items or products suitable for the user, means for the user to purchase a gift item or product selected from the suggested gift items or products, and means for collecting feedback from the user on the gift items or products and improving the accuracy of subsequent suggestions. This allows users to easily find products that suit their preferences, improving their purchasing experience. Furthermore, the use of a generative AI model improves the accuracy of data analysis, enabling more personalized suggestions. Furthermore, by incorporating user feedback into subsequent suggestions, continued improvement in user satisfaction can be expected.

[1096] "User transaction history data" refers to information about a user's past purchasing behavior and payments, including details such as product name, purchase date and time, payment amount, and payment method.

[1097] A "preference profile" is a collection of information that indicates a user's preferences and interests, generated based on the user's past behavioral data. This profile includes information such as the user's preferred categories, brands, and price ranges.

[1098] A "return gift" is a product or service that a user can receive when making a donation to a specific service.

[1099] "Product" is a general term for products and services that users can purchase, including physical goods, digital content, and services.

[1100] "Donation Request Process" means the formal process by which a User may make a donation to a particular service or cause, including entering a donation amount and submitting any required documentation.

[1101] "Feedback" refers to the evaluation or comments that users make on the products or gifts they receive, which can help improve the accuracy of future suggestions.

[1102] "Location data" refers to information about a user's current location and past visit history, collected through GPS, Wi-Fi, and base station data.

[1103] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and predicts user preferences and patterns. This model uses machine learning and deep learning techniques.

[1104] A "prompt" is text data that is input to a particular generative AI model and is an instruction that the model uses to generate the appropriate output.

[1105] The system of the present invention collects and analyzes users' transaction history data and location data to propose personalized products and return gifts suitable for the user, simplifying the purchasing process and donation application process. This system includes a server, a user terminal, and a generative AI model. Specific embodiments are described in detail below.

[1106] Collection and analysis of user data

[1107] The server collects data on users' transaction history, location, and purchasing behavior, including payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. The data is then analyzed using Pandas and Scikit-Learn to generate a user preference profile.

[1108] Examples:

[1109] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients. A generative AI model is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[1110] Generate personalized suggestions

[1111] The server generates multiple rewards or product plans based on the preference profile output by the generative AI model. This generative AI model uses machine learning and deep learning technologies to analyze users' purchasing preferences and behavioral patterns.

[1112] Examples:

[1113] Based on the information that "User A is interested in a particular brand and has a budget of 50,000 yen," the server generates a shopping plan that includes information on popular products and special sales from that brand.

[1114] Presenting the proposal

[1115] The user device receives the personalized gift or product plan sent from the server and presents it to the user within the app, where detailed information, ratings, prices, discount information, etc. for each gift or product are displayed.

[1116] Examples:

[1117] The device displays multiple product plans in a section called "Recommended Shopping Plans," such as "luxury bags from specific brands" and "latest smartphone models."

[1118] Purchase or donation process

[1119] Users select the desired gift or product plan from the presented options, and then follow the in-app guide to complete the purchase or donation application process, simplifying the complicated process.

[1120] Examples:

[1121] User B selects "luxury bag from a specific brand," enters the purchase amount and shipping information within the app, and completes the purchase process with just a few clicks.

[1122] Feedback and suggestions for next improvements

[1123] The server collects feedback about the gifts or products received by the user, including ratings and comments the user makes about the products or gifts, and uses this feedback when generating subsequent suggestions to help provide suggestions that better match the user's preferences.

[1124] Examples:

[1125] If user C gives a high rating to a "luxury bag from a specific brand" that he / she purchased, the server will reflect this feedback in the next proposal generation and propose a more accurate plan to the user.

[1126] Prompt Sentence Examples

[1127] The following prompt sentences could be input to the generative AI model:

[1128] "Cluster the patterns in the following data and generate the most suitable product list for a specific profile. Data: purchase history, amount, category. Desired result: product list for a specific profile."

[1129] In this way, the present invention enables personalized offers based on user preferences, improving the purchasing experience and ongoing user satisfaction.

[1130] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1131] Step 1:

[1132] The server collects the user's transaction history data and location data. This input data includes the user's payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. This allows the user's past activity data to be managed centrally.

[1133] Step 2:

[1134] The server analyzes the collected transaction history data and location data to generate a user preference profile. This analysis uses Pandas to process the data and Scikit-Learn to apply machine learning models such as clustering. Based on the input data, the server identifies the user's past preferred categories, brands, and price ranges and outputs them as a preference profile.

[1135] Step 3:

[1136] The server uses a generative AI model to generate multiple rewards or product plans based on the user's preference profile. The prompt is "Please generate a product list that matches a specific preference profile." The generative AI model outputs a list of products or rewards that best fit the user's preference profile.

[1137] Step 4:

[1138] The server sends the generated reward or product plan to the user's device, which receives it and presents it to the user within the app. Input includes a product list, ratings, prices, discount information, etc., which are displayed on the screen for easy understanding by the user.

[1139] Step 5:

[1140] The user selects the desired gift or product plan from the presented gift plans. The user terminal sends information about the selected product or gift to the server. The input data is the product information selected by the user, and the server receives this information and proceeds with the purchase procedure or donation application procedure.

[1141] Step 6:

[1142] The server generates the documents required for the purchase process and sends them to the user. The user can then check the documents sent within the app and complete the process with a few simple operations. The input data includes purchase information and shipping address information, and the documents are generated based on this information.

[1143] Step 7:

[1144] After receiving a product or gift, the user provides feedback within the app. The user's device sends this feedback to the server. The input data is the user's rating and comments, and the server analyzes them and reflects them in the next proposal.

[1145] Step 8:

[1146] The server adjusts the prompts for the generative AI model based on the collected feedback to improve the accuracy of the next recommendation. Specifically, it uses prompts such as, "Please consider the user's feedback and generate a product list that will improve the accuracy of the next recommendation." The generative AI model outputs further suggestions based on the new profile and feedback information.

[1147] Through this series of steps, the system will suggest personalized products and gifts based on the user's preferences, improving the user experience.

[1148] 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.

[1149] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[1150] Program processing

[1151] 1. Collection and analysis of user data

[1152] Subject: Server

[1153] The server collects the user's transaction history data and location data and stores them in a database, including the user's payment history, residential location information, behavioral history, etc. The collected data is then analyzed to generate a user preference profile.

[1154] Examples:

[1155] The server collects data such as luxury ingredients and local specialties purchased by the user in the past, and analyzes whether the user is interested in a particular category.

[1156] 2. Emotional state recognition using an emotion engine

[1157] Subject: Server

[1158] The emotion engine analyzes the user's facial expression data, voice data, and text input data to recognize the user's current emotional state. This recognition result, along with the user's preference profile, is used to generate a reward plan.

[1159] Examples:

[1160] The server recognizes facial expressions while the user is operating the app and estimates emotions from voice input. For example, if the user is smiling, it determines that the user is in a positive emotional state.

[1161] 3. Generate personalized reward plans

[1162] Subject: Server

[1163] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the preference profile and the recognition results of the emotion engine. This involves selecting gifts that match the user's preferences within the user's donation limit and adjusting the plan according to the user's emotional state.

[1164] Examples:

[1165] Based on the fact that "User A is interested in sake and his / her current emotional state is positive," the server generates a gift plan for sake offered by local governments across the country and makes suggestions according to the user's emotional state.

[1166] 4. Present a gift plan

[1167] Subject: Terminal

[1168] The user device receives the gift plans sent from the server and presents them to the user within the app. Details of each gift plan are displayed on the screen in a format that corresponds to the user's emotional state.

[1169] Examples:

[1170] The device displays "recommended hometown tax donation plans" such as "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo" with a UI designed to make users feel emotionally satisfied.

[1171] 5. Select a gift plan and apply

[1172] Subject: User

[1173] Users select the desired gift plan from the presented options and complete the donation application process within the app.

[1174] Examples:

[1175] User B selects "Niigata Prefecture Junmai Sake" and enters the donation amount and shipping address information with simple operations to complete the application process.

[1176] 6. Gathering Feedback

[1177] Subject: User

[1178] The user inputs a rating and comment on the gift they received.

[1179] Examples:

[1180] When user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the feedback data is sent to the server.

[1181] 7. Analyze feedback and improve your next plan

[1182] Subject: Server

[1183] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine, thereby improving the accuracy of the next plan generation.

[1184] Examples:

[1185] Based on the positive feedback, the server will improve the model so that it can propose more accurate sake plans the next time it generates a gift plan.

[1186] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[1187] The processing flow will be explained below.

[1188] Step 1: Collect user data

[1189] Subject: Server

[1190] The server collects user transaction history data and location data, including user payment history, residential location information, and in-app activity history.

[1191] Specific behavior:

[1192] The server runs SQL queries against the transaction history database to retrieve past payment records.

[1193] Registered address and GPS data are retrieved from the user profile database.

[1194] Collect user behavior logs within the app (page visits, purchase history).

[1195] Step 2: Generate a preference profile

[1196] Subject: Server

[1197] The server inputs the collected data into a generative AI model to generate a user preference profile.

[1198] Specific behavior:

[1199] The server preprocesses the collected data and converts it into an input format for the AI ​​model.

[1200] The preprocessed data is input into a generative AI model to analyze user preference patterns.

[1201] The generated preference profile is stored in a database.

[1202] Step 3: Recognizing emotional states using the emotion engine

[1203] Subject: Terminal

[1204] The terminal collects the user's facial expression data, voice data, and character input data and sends them to the emotion engine, which recognizes the user's current emotional state.

[1205] Specific behavior:

[1206] The device uses the front camera to capture the user's facial expressions.

[1207] A microphone is used to record the user's voice and the voice data is sent to the emotion engine.

[1208] Character data is collected from touch and keyboard input and sent to the emotion engine.

[1209] The emotion engine analyzes the received data and recognizes the user's emotional state.

[1210] Step 4: Create a personalized reward plan

[1211] Subject: Server

[1212] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the user's preference profile and the recognition results of the emotion engine.

[1213] Specific behavior:

[1214] The server inputs preference profile data and emotion recognition results into the AI ​​model.

[1215] The AI ​​model searches for rewards that match the user's donation limit and generates a list.

[1216] Create a list of suggested gift plans and send it to the user.

[1217] Step 5: Present your gift plan

[1218] Subject: Terminal

[1219] The terminal receives the return gift plan sent from the server and displays it on the user interface.

[1220] Specific behavior:

[1221] The terminal parses the received gift plan data.

[1222] Bind the parsed data to a UI component to display the details.

[1223] The display format is presented to the user according to the emotional state.

[1224] Step 6: Select a reward plan and apply

[1225] Subject: User

[1226] The user selects the desired plan from the presented return gift plans and completes the donation application procedure.

[1227] Specific behavior:

[1228] The user taps the selection button on the screen to select the desired gift plan.

[1229] Enter your donation amount and shipping information.

[1230] Click the Apply button to complete the process.

[1231] Step 7: Gather feedback

[1232] Subject: User

[1233] The user inputs a rating and comment on the gift they received.

[1234] Specific behavior:

[1235] Users can access the in-app rating form and provide feedback about the gift.

[1236] The terminal transmits the input feedback data to the server.

[1237] Step 8: Analyze feedback and improve your next plan

[1238] Subject: Server

[1239] The server analyzes the collected feedback and applies it to the generative AI model and emotion engine, thereby improving the accuracy of the next reward plan.

[1240] Specific behavior:

[1241] The server stores the feedback data in a database.

[1242] The feedback data is input into a generative AI model and used as training data for the model.

[1243] The improved AI model will be used to generate the next reward plan.

[1244] Example 2

[1245] 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."

[1246] Current hometown tax donation systems do not adequately provide personalized suggestions that take into account users' preferences and emotional state. This makes it difficult for users to find the perfect gift from the many options available, resulting in a lack of improvement in the user experience. Furthermore, the lack of effective feedback collection and improvement of the next proposal makes it difficult to continuously improve user satisfaction.

[1247] 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.

[1248] In this invention, the server includes means for collecting user transaction history data and location data, means for analyzing the user transaction history data and location data to generate a user preference profile, means for analyzing the user's facial expression data, voice data, and character input data to recognize the user's emotional state, means for proposing multiple gift plans generated based on the preference profile and emotional state, means for presenting the gift plans to the user terminal and allowing the user to apply for a donation for the gift selected by the user, and means for collecting feedback on the gift from the user to improve the accuracy of the next proposal. This not only makes it possible to propose the most suitable gift plan for the user, but also makes it possible to continuously improve user satisfaction.

[1249] "Transaction history data" refers to data that includes detailed information about purchases and payments made by a user in the past.

[1250] "Location data" is data that includes information about a user's geographic location, such as where the user lives or where the user visits.

[1251] A "preference profile" is information that indicates a user's preferences, interests, and concerns, and is generated by analyzing the user's transaction history data and location data.

[1252] "Facial expression data" is data that includes information about the user's facial expression.

[1253] "Voice data" is data that includes information about the voice uttered by the user.

[1254] "Character input data" refers to data that includes character information input by a user via a keyboard or touch screen.

[1255] "Emotional state" is information indicating the emotional state of the user that is recognized by analyzing facial expression data, voice data, and character input data.

[1256] A "return gift plan" is a specific option for a return gift for hometown tax donations that is proposed to the user.

[1257] "Feedback" is information indicating opinions and impressions, such as ratings and comments, regarding the gift received by the user.

[1258] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate the optimal reward plan for each user.

[1259] A "prompt" is input text that is fed into a generative AI model to instruct it on a specific task.

[1260] The present invention is a system that collects and analyzes a user's transaction history data and location data to provide a personalized gift plan suited to the user, and further proposes a gift that takes into account the user's emotional state using an emotion engine. This system includes a server, a user terminal, a generative AI model, and an emotion engine. Specific embodiments are described below.

[1261] The server first collects the user's transaction history data and location data through the API. The transaction history data includes payment history, purchase history, etc., and this data is stored in a database such as MySQL or PostgreSQL. For example, the server collects and analyzes data such as the user's past purchases of luxury ingredients and local specialties.

[1262] The server then analyzes the collected transaction history data and location data to generate a user preference profile. This analysis is performed using data analysis tools such as the Python pandas library and SQL queries. The analysis results are saved in JSON format and stored on the server.

[1263] The emotion engine acquires facial expression data, voice data, and text input data from the user's device. This data is collected through hardware such as a camera and microphone. The server analyzes this data using an emotion analysis library such as Microsoft Azure's Cognitive Services to recognize the user's emotional state. For example, it recognizes facial expressions while the user is operating the app and infers emotions from voice input.

[1264] Next, the server uses a generative AI model to generate a gift plan suited to the user based on the preference profile and the recognition results of the emotion engine. The generative AI model uses machine learning algorithms and cloud-based AI services. The AI ​​model operates based on the prompt text, and generates a gift plan for sake based on the fact that "User A is interested in sake and his current emotional state is positive."

[1265] The user's device receives the gift plans sent from the server and presents them to the user within the app. Each gift plan is displayed in detail in a format that reflects the user's emotional state. For example, the device might display plans such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture."

[1266] Users select the desired gift plan from the presented plans and complete the donation application process within the app. This includes entering the donation amount, shipping information, and confirmation. The user's device will then complete a set procedure and send the information to the server, completing the donation application.

[1267] In addition, users can enter ratings and comments about the gifts they receive and send the feedback to the server. The server analyzes this feedback and uses it to improve the accuracy of the next gift plan generation. For example, if the gift is highly rated, the server can use that information to adjust the algorithm of the AI ​​model for generation.

[1268] As a result, the system of the present invention can realize personalized hometown tax return gift suggestions that take into account the user's emotional state, thereby continuously improving user satisfaction.

[1269] Specific prompt examples:

[1270] 1. "Please generate a recommended gift plan for user A based on their donation history."

[1271] 2. "Analyze User A's latest emotional state and provide personalized suggestions based on it."

[1272] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1273] Program processing flow

[1274] Step 1:

[1275] User Data Collection

[1276] Subject: Server

[1277] The server collects user transaction history data and location data through the API. This data includes payment history, purchase history, and location information. This data is stored in a database such as MySQL or PostgreSQL. Specifically, the server uses the REST API to obtain data on products the user has purchased in the past and saves it in the database as entries including date, time, category, and location information.

[1278] Input: Raw transaction history and location data obtained via API

[1279] Output: Structured data stored in a database

[1280] Step 2:

[1281] Generating a preference profile

[1282] Subject: Server

[1283] The server analyzes the collected transaction history data and location data to generate a user preference profile. The analysis uses Python's pandas library to calculate the purchase frequency for each category. The results are saved in JSON format. Specifically, the number of purchases and purchase amounts for each category are tallied to generate each user's preference profile.

[1284] Input: Transaction history data and location data stored in a database

[1285] Output: User preference profile in JSON format

[1286] Step 3:

[1287] Recognition of emotional states

[1288] Subject: Server

[1289] The server uses an emotion engine to analyze facial expression data, voice data, and text input data sent from the user's device. It uses Microsoft Azure's Cognitive Services to determine the user's emotional state from their facial expression. Specifically, it analyzes captured image and voice data and generates emotion labels such as positive, negative, and neutral.

[1290] Input: Facial expression data, voice data, and text input data sent from the user device

[1291] Output: Emotional state data in JSON format

[1292] Step 4:

[1293] Generate a gift plan

[1294] Subject: Server

[1295] The server uses a generative AI model to generate a gift plan suited to the user based on their preference profile and emotional state data. During this process, a machine learning algorithm is used to analyze the data and generate multiple gift plans. Specifically, based on the prompt text, the AI ​​model creates a plan that recommends gifts that best suit the user's preferences and current emotional state.

[1296] Input: Preference profile, emotional state data

[1297] Output: Multiple gift plans generated by the generative AI model

[1298] Step 5:

[1299] Presentation of return gift plan

[1300] Subject: Terminal

[1301] The user's device receives the gift plans sent from the server and presents them to the user within the app. The plans are displayed in detail in a format that corresponds to the user's emotional state. Specifically, a UI framework is used to display an image and description of each gift plan on the screen.

[1302] Input: Gift plan received from the server

[1303] Output: Gift plan displayed on the user's device screen

[1304] Step 6:

[1305] Selecting a reward plan and applying

[1306] Subject: User

[1307] Users select the desired gift plan from the presented options and complete the donation application process within the app. They enter the necessary information and confirm it on the confirmation screen to complete the application. Specifically, they enter the donation amount and shipping address information, and then confirm it.

[1308] Input: The gift plan presented, the donation amount and shipping information entered by the user

[1309] Output: Donation request data sent to the server

[1310] Step 7:

[1311] Collecting feedback

[1312] Subject: User

[1313] Users can enter their ratings and comments about the gifts they receive in the app. Specifically, they enter their opinions and thoughts in the rating form and press the submit button.

[1314] Input: Ratings and comments entered by users

[1315] Output: Feedback data sent to the server

[1316] Step 8:

[1317] Analyze feedback and improve your next plan

[1318] Subject: Server

[1319] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine. This improves the accuracy of generating the next reward plan. Specifically, it analyzes the evaluation content using natural language processing and adjusts the model parameters.

[1320] Input: Feedback data

[1321] Output: Next reward plan with improved generative AI model and emotion engine

[1322] The above processing steps make it possible to propose personalized gift plans based on the user's preferences and emotional state, thereby improving user satisfaction.

[1323] (Application example 2)

[1324] 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."

[1325] Conventional hometown tax donation systems often offer a uniform gift proposal without considering the user's preferences or emotional state, resulting in low user satisfaction. Furthermore, proposals based solely on transaction history and location data are unable to provide detailed proposals that reflect the user's emotional state. Therefore, there is a need for a system that offers personalized gift proposals and application procedures that take the user's emotional state into account.

[1326] The identification process by the identification 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 collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for recognizing the user's emotional state, and means for proposing multiple personalized return gifts to the user based on the emotional state and preference profile. This enables detailed suggestions that take the user's emotional state into consideration.

[1327] "Transaction history data" refers to data that records the purchases and payments that a user has made in the past.

[1328] A "preference profile" is a collection of information indicating a user's preferences and interests, obtained by analyzing the user's transaction history data.

[1329] The "emotional state" is a state that indicates the user's current emotions and mood, and is information acquired through facial expression recognition and voice analysis.

[1330] "Personalized offers" refer to personalized offers of rewards or services that are tailored to a user based on that user's preference profile and emotional state.

[1331] "Feedback" refers to evaluations and comments on the gift provided by the user, and is information used to improve the accuracy of suggestions next time.

[1332] "Location data" is data that indicates the user's current location and past travel routes.

[1333] The "donation application procedure" is a procedure for officially making a donation for the return gift selected by the user.

[1334] The "donation limit" refers to the upper limit of donations set when a user receives tax benefits.

[1335] "Documents" refers to official documents, consent forms, etc. required for donation applications.

[1336] "Progress" is information that indicates the current status or stage of the donation application process.

[1337] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[1338] Program processing explanation

[1339] Hardware and Software

[1340] 1. Server:

[1341] Collected Data: Collects user transaction history data and location data.

[1342] Analytical Data: Collected data is analyzed to generate a profile of your preferences.

[1343] Emotion engine: Analyzes the user's facial expression and voice data to recognize their emotional state.

[1344] Generative AI model: Generates personalized reward offers based on the user's preference profile and emotional state.

[1345] 2. User Device:

[1346] Received plan: Receives the reward plan sent from the server and presents it to the user within the app.

[1347] Selection procedure: Select the desired reward plan from the presented plans.

[1348] Application process: Complete the donation application process within the app.

[1349] Feedback: Enter your feedback on the proposed reward.

[1350] Data processing and calculation

[1351] 1. User Data Collection and Analysis:

[1352] The server collects the user's transaction history and location data through API calls, and uses the collected data to create a user preference profile. For example, it analyzes data such as the user's past purchases of luxury ingredients or local specialties to identify the user's interests.

[1353] 2. Recognition of emotional states:

[1354] The server uses an emotion engine to analyze the user's facial expression and voice data. It recognizes the user's current emotional state and uses it, along with their preference profile, to generate a reward plan. For example, if the user is smiling while using the app, it is determined to be in a positive emotional state.

[1355] 3. Generate personalized reward plans:

[1356] The server uses the generative AI model to generate multiple gift plans suitable for the user based on their preference profile and emotional state. For example, if the user is interested in sake and their current emotional state is positive, the server generates gift plans for sake offered by local governments across the country.

[1357] Examples of concrete examples and prompts

[1358] Examples:

[1359] Usage: Proposing a cashback campaign to improve the hometown tax donation experience on smartphone apps.

[1360] Input: transaction history, location, facial expression images, audio clips.

[1361] Output: A reward plan that matches the user's preferences and emotional state.

[1362] Example prompts to be input to the generative AI model

[1363] text

[1364] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[1365] User data: {user_data}

[1366] Emotional state: {emotion}

[1367] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[1368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1369] Step 1:

[1370] The server collects user transaction history data and location data through API calls. This includes the user's past purchases and payments, as well as current and past location information. The collected data is stored in a database. The input is the user ID, and the output is the transaction history data and location data.

[1371] Step 2:

[1372] The server analyzes the collected transaction history data and generates a user preference profile. For example, it analyzes whether the user likes a particular category of products or which services they frequently use. The input is the transaction history data, and the output is the user preference profile.

[1373] Step 3:

[1374] The user terminal collects the user's facial expression and voice data through a camera and microphone. These data are transmitted to the server. The input is the user's facial expression image and voice clip, and the output is the transmitted data.

[1375] Step 4:

[1376] The server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This determines whether the emotion is positive or negative based on factors such as smiles and tone of voice. The input is facial expression images and voice data, and the output is the recognized emotional state.

[1377] Step 5:

[1378] The server uses a generative AI model to generate personalized gift plans based on the user's preference profile and emotional state. For example, if a user is interested in sake and has a positive emotional state, multiple gift plans for sake are created. The input is the user's preference profile and emotional state, and the output is the gift plans.

[1379] Step 6:

[1380] The user device receives the gift plan sent from the server and presents it to the user within the app. The details of the gift plan are displayed on the screen in a design that corresponds to the user's emotional state. The input is the gift plan, and the output is the information presented to the user.

[1381] Step 7:

[1382] The user selects the desired plan from the presented gift plans and completes the donation application process within the app. Based on the selected plan, the user enters the required information. The input is the selected gift plan and application information, and the output is application completion information.

[1383] Step 8:

[1384] The server collects feedback from users. The feedback includes ratings and comments about the rewards. The collected feedback data is used to improve the accuracy of the next proposal. The input is feedback information, and the output is improvement data for the next proposal.

[1385] Step 9:

[1386] The server analyzes the collected feedback data and reflects it in the generative AI model and emotion engine. This improves the accuracy and quality of the next gift plan generation. The input is the feedback data, and the output is improved model and engine parameters.

[1387] (Example of a prompt)

[1388] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[1389] User data: {user_data}

[1390] Emotional state: {emotion}

[1391] 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.

[1392] 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.

[1393] 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.

[1394] [Fourth embodiment]

[1395] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1396] 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.

[1397] 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).

[1398] 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.

[1399] 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.

[1400] 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).

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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."

[1408] The system of the present invention collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. The system includes a server, a user terminal, and a generative AI model.

[1409] Program processing

[1410] 1. Collection and analysis of user data

[1411] Subject: Server

[1412] The server collects the user's transaction history data. Specifically, the server captures the user's payment history, residential location information, and in-app behavior history, and stores this information in a database. The server then analyzes this data to generate a user preference profile. A generative AI model is used to identify personalized preferences based on the user's purchase history, product categories of interest, and other factors.

[1413] Examples:

[1414] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients.

[1415] 2. Generate personalized reward plans

[1416] Subject: Server

[1417] The server generates multiple gift plans based on the preference profile output by the generative AI model. This includes logic for selecting gifts that match the user's donation limit and preferences. To maximize user satisfaction, the server also takes past feedback data into account and suggests the most likely options for the next time.

[1418] Examples:

[1419] The server generates a gift plan for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen."

[1420] 3. Present a gift plan

[1421] Subject: Terminal

[1422] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the screen.

[1423] Examples:

[1424] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," including "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo."

[1425] 4. Select a gift plan and apply

[1426] Subject: User

[1427] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[1428] Examples:

[1429] User B selects "Niigata Prefecture Junmai Sake," enters the donation amount and shipping information within the app, and completes the application process with just a few clicks.

[1430] 5. Gather feedback and improve your next plan

[1431] Subject: Server

[1432] The server collects feedback about the gifts received by the user, including ratings and comments made by the user about the gifts, and uses this feedback when generating the next plan to help provide a plan that better matches the user's preferences.

[1433] Examples:

[1434] If user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the server will reflect this feedback in the next plan generation and suggest a more accurate sake plan to the user.

[1435] With this configuration, the present invention can realize a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[1436] The processing flow will be explained below.

[1437] Step 1: Collect user data

[1438] Subject: Server

[1439] The server accesses a database to collect user transaction history data, including the user's past payment history, residential location information, and in-app activity history. The server periodically checks this information to obtain the latest data.

[1440] Specific behavior:

[1441] The server executes an SQL query to retrieve the most recent payment history from the transaction history table.

[1442] Get registered address and GPS data from the user profile table.

[1443] Analyze log data of page visits and product views within the app.

[1444] Step 2: Generate a preference profile

[1445] Subject: Server

[1446] The server inputs the collected data into a generative AI model to generate a user preference profile, which involves analyzing patterns of the user's past purchases and pages viewed.

[1447] Specific behavior:

[1448] The server preprocesses the collected data to match the input format of the generative AI model.

[1449] The preprocessed data is fed into an AI model to generate a preference profile.

[1450] The generated preference profile is linked to the user information in the database and saved.

[1451] Step 3: Create a reward plan

[1452] Subject: Server

[1453] The server generates a reward plan suitable for the user based on the preference profile and the donation limit, which includes a process of suggesting multiple reward options.

[1454] Specific behavior:

[1455] The server filters the reward information in the database based on the preference profile data.

[1456] A gift is selected from the search results so that it falls within the user's donation limit.

[1457] A number of plans are generated and a list is created to be proposed to the user.

[1458] Step 4: Present your gift plan

[1459] Subject: Terminal

[1460] The terminal displays the gift plan sent from the server on the user interface, and the user can view the details of the proposed plan on the screen.

[1461] Specific behavior:

[1462] The terminal parses the received gift plan data.

[1463] Bind the parsed data to a UI component to display the details.

[1464] Build an interface for users to view plan details.

[1465] Step 5: Select a reward plan and apply

[1466] Subject: User

[1467] Users can select the desired plan from the multiple gift plans presented, and then complete the donation application process within the app.

[1468] Specific behavior:

[1469] The user selects a desired plan from the displayed plans.

[1470] Follow the instructions on the device to enter your donation amount and shipping information.

[1471] Tap the Apply button, confirm all the information, and then complete the process.

[1472] Step 6: Gather feedback

[1473] Subject: User

[1474] Users can enter their ratings and comments on the gifts they receive, and this feedback will be used to generate the next plan.

[1475] Specific behavior:

[1476] Users can enter feedback about the gift through a rating form.

[1477] The terminal transmits the input feedback to the server.

[1478] Step 7: Analyze feedback and improve your next plan

[1479] Subject: Server

[1480] The server analyzes the received feedback and reflects it in the generative AI model, thereby improving the accuracy of the next plan generation.

[1481] Specific behavior:

[1482] The server stores the feedback data in a database.

[1483] The saved data is input into a generative AI model and used as training data for the model.

[1484] The improved model is used to generate the next reward plan.

[1485] Example 1

[1486] 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."

[1487] The problem with hometown tax donations is that the process of users selecting a donation recipient and gift in return is complicated and requires a great deal of time and effort. It can also be difficult to find a gift that matches a user's preferences, which can result in lower satisfaction. Additionally, the criteria for selecting the best gift from the wide variety of gifts available are unclear, making it difficult for users to make an appropriate choice.

[1488] 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.

[1489] In this invention, the server includes a means for collecting user transaction history, a means for analyzing the transaction history to generate a user preference profile, and a means for generating multiple return gift plans based on the preference profile. This allows users to easily select the optimal return gift plan based on their preferences, simplifies the donation application process, and further allows for feedback to be collected to improve the accuracy of future proposals.

[1490] "Means for collecting user transaction history" refers to a device or program for acquiring and recording data such as a user's past purchase history, payment history, and residential information.

[1491] The "means for generating a user preference profile" is a device or program for analyzing collected user transaction history data and identifying the user's purchasing tendencies and preferences.

[1492] The "means for generating multiple return gift plans" is a device or program for proposing multiple return gifts that are optimal for a user based on the user's preference profile.

[1493] "Means for displaying the generated gift plan on the user's terminal" refers to a device or program for transmitting the gift plan generated by the server to the user's terminal and displaying it on the screen.

[1494] The "means for carrying out donation application procedures" refers to a device or program for carrying out the necessary donation application for the return gift plan selected by the user.

[1495] The "means for collecting feedback" is a device or program for obtaining and collecting feedback such as ratings and comments on the return gift from users.

[1496] The "means for improving the accuracy of the next proposal" is a device or program that analyzes the collected feedback and reflects it when generating the next return gift plan, thereby making proposals that are more in line with the user's preferences.

[1497] The "means for collecting user location data" refers to a device or program for acquiring and recording location information such as the user's current location and past movement history.

[1498] The "means for calculating the maximum donation amount" is a device or program for calculating the maximum amount that a user can donate based on the collected location data and transaction history data.

[1499] The "means for generating documents required for donation applications" refers to a device or program that automatically generates the documents and forms required when a user applies for a donation.

[1500] "Means for updating the progress of a donation request" refers to a device or program for managing and updating the progress of a donation request (e.g., application in progress, completed) based on the gift plan selected by the user.

[1501] The present invention is a system that collects and analyzes users' transaction history data to provide personalized gift plans suited to each user and complete the entire donation application process. This system includes a server, a user terminal, and a generative AI model.

[1502] The server collects the user's transaction history data. Specifically, the server captures information such as the user's payment history, location information, and in-app behavior history, and stores this data in a database. The server then analyzes this data to generate a user preference profile. A generative AI model (e.g., OpenAI's GPT-4) is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[1503] Examples:

[1504] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in local specialties and luxury ingredients. An example of a prompt sentence is input to the generative AI model: "Analyze the user's transaction history data and generate a food preference profile."

[1505] The server then generates multiple gift plans based on the preference profile output by the generative AI model. The server then executes logic to select gifts that fit the user's donation limit and preferences, and takes past feedback data into account to suggest the most likely options for the next time, thereby maximizing user satisfaction.

[1506] Examples:

[1507] The server generates gift plans for sake offered by local governments across the country based on the fact that "User A is interested in sake and the maximum donation amount is 50,000 yen." As an example of a prompt, the server inputs the instruction "Generate an appropriate gift plan based on User A's preference profile" into the generation AI model.

[1508] The user device receives the personalized gift plans sent from the server and presents them to the user within the app. Detailed information about each gift plan is displayed on the device, allowing the user to select one.

[1509] Examples:

[1510] The device displays several sake plans in a section called "Recommended Hometown Tax Donation Plans," such as "Junmai sake from Niigata Prefecture" and "Daiginjo from Yamagata Prefecture." As an example of a prompt, the instruction "Display detailed information about the proposed return gift plan" is input to the generative AI model.

[1511] Users select the desired plan from the presented gift plans. Next, they follow the in-app guide to complete the donation application process, simplifying the complicated process for users.

[1512] Examples:

[1513] User B selects "Junmai sake from Niigata Prefecture," enters the donation amount and shipping information in the app, and completes the application process with just a few clicks. An example of a prompt sentence is input to the generative AI model: "Complete the donation application process according to the gift plan you selected."

[1514] Finally, the server collects feedback about the gifts received by the user, including ratings and comments the user makes about the gifts, and uses this feedback to generate the next plan to help provide a plan that better matches the user's preferences.

[1515] Examples:

[1516] If User C gives a high rating to the "Niigata Prefecture Junmai Sake" that he received, the server will reflect this feedback in the next plan generation and propose a more accurate sake plan to the user. An example of a prompt sentence is to input the instruction "Collect feedback about the gift received and use it to generate the next plan" into the generative AI model.

[1517] With this configuration, the present invention enables a simple and personalized hometown tax payment procedure, thereby improving user satisfaction.

[1518] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1519] Step 1:

[1520] The server collects the user's transaction history. First, it calls an API to obtain the user's past purchase history data from the payment system and stores the results in a database. Next, it obtains the user's residential location information and stores it in the database as well. It also analyzes the user's behavioral history within the app and saves it in the database as transaction history data.

[1521] Input: User purchase history, payment history, residential location information, in-app behavior history

[1522] Output: Collected transaction history data stored in a database

[1523] Specific operation: The server calls the API to obtain PayPal and credit card payment history and stores it in a database within the system.

[1524] Step 2:

[1525] The server analyzes the collected transaction history data to generate a user preference profile. Using a generative AI model (e.g., OpenAI's GPT-4), it analyzes the transaction history retrieved from the database to identify the user's purchasing trends and product categories that indicate their interests.

[1526] Input: Transaction history data

[1527] Output: User preference profile

[1528] Specific operation: The server inputs the transaction history data into the generation AI model and generates a profile using the prompt sentence, "Analyze the user's transaction history data and generate a food preference profile."

[1529] Step 3:

[1530] The server generates multiple gift plans based on the generated preference profile, and uses a generative AI model to create the optimal gift plan, taking into account the user's donation limit and past feedback data.

[1531] Inputs: Preference profile, donation limit, feedback data

[1532] Output: Personalized gift plan

[1533] Specific operation: The server inputs a prompt statement such as "Generate an appropriate gift plan based on user A's preference profile" into the generative AI model and combines gift plans.

[1534] Step 4:

[1535] The user's device receives the gift plan data sent from the server and displays it within the app, allowing the user to check detailed information about the gift plan.

[1536] Input: Gift plan data

[1537] Output: Display of reward plan on the app screen

[1538] Specific operation: The device parses the data sent from the server in JSON format and displays detailed information such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture" on the screen.

[1539] Step 5:

[1540] The user selects the desired gift plan from the displayed options and completes the donation application process. The user enters the donation amount and shipping address information and follows the application procedure guide to complete the application.

[1541] Input: User selection, donation amount, shipping information

[1542] Output: Notification of application completion and corresponding data update

[1543] Specific operation: User B selects "Niigata Prefecture Junmai Sake," enters the donation amount of 50,000 yen and shipping information, and clicks the "Application Complete" button.

[1544] Step 6:

[1545] The server collects feedback from users, including ratings and comments on the gifts they received, and stores them in a database as reference information for generating the next gift plan.

[1546] Input: User feedback (ratings, comments)

[1547] Output: Feedback data stored in a database and the next plan generated based on the feedback

[1548] Specific operation: The server obtains the user's rating (e.g., "I gave this Junmai sake from Niigata Prefecture five stars") from the feedback section within the app and reflects it in the next prompt sentence for the generative AI model.

[1549] (Application example 1)

[1550] 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."

[1551] Conventional systems did not adequately personalize products and gifts to match users' preferences, making it difficult to improve the user experience. Additionally, the accuracy of suggestions was low, making it difficult to increase user satisfaction. This made it difficult for users to find the best product for them from the many options available, requiring cumbersome search tasks. Furthermore, there was no system for incorporating feedback into the next suggestions, making it difficult to ensure continued user satisfaction.

[1552] 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.

[1553] In this invention, the server includes means for collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for suggesting multiple gift items or products suitable for the user, means for the user to purchase a gift item or product selected from the suggested gift items or products, and means for collecting feedback from the user on the gift items or products and improving the accuracy of subsequent suggestions. This allows users to easily find products that suit their preferences, improving their purchasing experience. Furthermore, the use of a generative AI model improves the accuracy of data analysis, enabling more personalized suggestions. Furthermore, by incorporating user feedback into subsequent suggestions, continued improvement in user satisfaction can be expected.

[1554] "User transaction history data" refers to information about a user's past purchasing behavior and payments, including details such as product name, purchase date and time, payment amount, and payment method.

[1555] A "preference profile" is a collection of information that indicates a user's preferences and interests, generated based on the user's past behavioral data. This profile includes information such as the user's preferred categories, brands, and price ranges.

[1556] A "return gift" is a product or service that a user can receive when making a donation to a specific service.

[1557] "Product" is a general term for products and services that users can purchase, including physical goods, digital content, and services.

[1558] "Donation Request Process" means the formal process by which a User may make a donation to a particular service or cause, including entering a donation amount and submitting any required documentation.

[1559] "Feedback" refers to the evaluation or comments that users make on the products or gifts they receive, which can help improve the accuracy of future suggestions.

[1560] "Location data" refers to information about a user's current location and past visit history, collected through GPS, Wi-Fi, and base station data.

[1561] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and predicts user preferences and patterns. This model uses machine learning and deep learning techniques.

[1562] A "prompt" is text data that is input to a particular generative AI model and is an instruction that the model uses to generate the appropriate output.

[1563] The system of the present invention collects and analyzes users' transaction history data and location data to propose personalized products and return gifts suitable for the user, simplifying the purchasing process and donation application process. This system includes a server, a user terminal, and a generative AI model. Specific embodiments are described in detail below.

[1564] Collection and analysis of user data

[1565] The server collects data on users' transaction history, location, and purchasing behavior, including payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. The data is then analyzed using Pandas and Scikit-Learn to generate a user preference profile.

[1566] Examples:

[1567] The server collects the user's payment history for past food and beverage purchases and analyzes the user's interest in specific regional specialties and luxury ingredients. A generative AI model is used to identify personalized preferences based on the user's purchase history and product categories of interest.

[1568] Generate personalized suggestions

[1569] The server generates multiple rewards or product plans based on the preference profile output by the generative AI model. This generative AI model uses machine learning and deep learning technologies to analyze users' purchasing preferences and behavioral patterns.

[1570] Examples:

[1571] Based on the information that "User A is interested in a particular brand and has a budget of 50,000 yen," the server generates a shopping plan that includes information on popular products and special sales from that brand.

[1572] Presenting the proposal

[1573] The user device receives the personalized gift or product plan sent from the server and presents it to the user within the app, where detailed information, ratings, prices, discount information, etc. for each gift or product are displayed.

[1574] Examples:

[1575] The device displays multiple product plans in a section called "Recommended Shopping Plans," such as "luxury bags from specific brands" and "latest smartphone models."

[1576] Purchase or donation process

[1577] Users select the desired gift or product plan from the presented options, and then follow the in-app guide to complete the purchase or donation application process, simplifying the complicated process.

[1578] Examples:

[1579] User B selects "luxury bag from a specific brand," enters the purchase amount and shipping information within the app, and completes the purchase process with just a few clicks.

[1580] Feedback and suggestions for next improvements

[1581] The server collects feedback about the gifts or products received by the user, including ratings and comments the user makes about the products or gifts, and uses this feedback when generating subsequent suggestions to help provide suggestions that better match the user's preferences.

[1582] Examples:

[1583] If user C gives a high rating to a "luxury bag from a specific brand" that he / she purchased, the server will reflect this feedback in the next proposal generation and propose a more accurate plan to the user.

[1584] Prompt Sentence Examples

[1585] The following prompt sentences could be input to the generative AI model:

[1586] "Cluster the patterns in the following data and generate the most suitable product list for a specific profile. Data: purchase history, amount, category. Desired result: product list for a specific profile."

[1587] In this way, the present invention enables personalized offers based on user preferences, improving the purchasing experience and ongoing user satisfaction.

[1588] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1589] Step 1:

[1590] The server collects the user's transaction history data and location data. This input data includes the user's payment history, residential location information, and in-app activity history. The collected data is stored in a database using Python's Django framework. This allows the user's past activity data to be managed centrally.

[1591] Step 2:

[1592] The server analyzes the collected transaction history data and location data to generate a user preference profile. This analysis uses Pandas to process the data and Scikit-Learn to apply machine learning models such as clustering. Based on the input data, the server identifies the user's past preferred categories, brands, and price ranges and outputs them as a preference profile.

[1593] Step 3:

[1594] The server uses a generative AI model to generate multiple rewards or product plans based on the user's preference profile. The prompt is "Please generate a product list that matches a specific preference profile." The generative AI model outputs a list of products or rewards that best fit the user's preference profile.

[1595] Step 4:

[1596] The server sends the generated reward or product plan to the user's device, which receives it and presents it to the user within the app. Input includes a product list, ratings, prices, discount information, etc., which are displayed on the screen for easy understanding by the user.

[1597] Step 5:

[1598] The user selects the desired gift or product plan from the presented gift plans. The user terminal sends information about the selected product or gift to the server. The input data is the product information selected by the user, and the server receives this information and proceeds with the purchase procedure or donation application procedure.

[1599] Step 6:

[1600] The server generates the documents required for the purchase process and sends them to the user. The user can then check the documents sent within the app and complete the process with a few simple operations. The input data includes purchase information and shipping address information, and the documents are generated based on this information.

[1601] Step 7:

[1602] After receiving a product or gift, the user provides feedback within the app. The user's device sends this feedback to the server. The input data is the user's rating and comments, and the server analyzes them and reflects them in the next proposal.

[1603] Step 8:

[1604] The server adjusts the prompts for the generative AI model based on the collected feedback to improve the accuracy of the next recommendation. Specifically, it uses prompts such as, "Please consider the user's feedback and generate a product list that will improve the accuracy of the next recommendation." The generative AI model outputs further suggestions based on the new profile and feedback information.

[1605] Through this series of steps, the system will suggest personalized products and gifts based on the user's preferences, improving the user experience.

[1606] 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.

[1607] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[1608] Program processing

[1609] 1. Collection and analysis of user data

[1610] Subject: Server

[1611] The server collects the user's transaction history data and location data and stores them in a database, including the user's payment history, residential location information, behavioral history, etc. The collected data is then analyzed to generate a user preference profile.

[1612] Examples:

[1613] The server collects data such as luxury ingredients and local specialties purchased by the user in the past, and analyzes whether the user is interested in a particular category.

[1614] 2. Emotional state recognition using an emotion engine

[1615] Subject: Server

[1616] The emotion engine analyzes the user's facial expression data, voice data, and text input data to recognize the user's current emotional state. This recognition result, along with the user's preference profile, is used to generate a reward plan.

[1617] Examples:

[1618] The server recognizes facial expressions while the user is operating the app and estimates emotions from voice input. For example, if the user is smiling, it determines that the user is in a positive emotional state.

[1619] 3. Generate personalized reward plans

[1620] Subject: Server

[1621] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the preference profile and the recognition results of the emotion engine. This involves selecting gifts that match the user's preferences within the user's donation limit and adjusting the plan according to the user's emotional state.

[1622] Examples:

[1623] Based on the fact that "User A is interested in sake and his / her current emotional state is positive," the server generates a gift plan for sake offered by local governments across the country and makes suggestions according to the user's emotional state.

[1624] 4. Present a gift plan

[1625] Subject: Terminal

[1626] The user device receives the gift plans sent from the server and presents them to the user within the app. Details of each gift plan are displayed on the screen in a format that corresponds to the user's emotional state.

[1627] Examples:

[1628] The device displays "recommended hometown tax donation plans" such as "Niigata Prefecture Junmai Sake" and "Yamagata Prefecture Daiginjo" with a UI designed to make users feel emotionally satisfied.

[1629] 5. Select a gift plan and apply

[1630] Subject: User

[1631] Users select the desired gift plan from the presented options and complete the donation application process within the app.

[1632] Examples:

[1633] User B selects "Niigata Prefecture Junmai Sake" and enters the donation amount and shipping address information with simple operations to complete the application process.

[1634] 6. Gathering Feedback

[1635] Subject: User

[1636] The user inputs a rating and comment on the gift they received.

[1637] Examples:

[1638] When user C gives a high rating to the "Niigata Prefecture Junmai Sake" he received, the feedback data is sent to the server.

[1639] 7. Analyze feedback and improve your next plan

[1640] Subject: Server

[1641] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine, thereby improving the accuracy of the next plan generation.

[1642] Examples:

[1643] Based on the positive feedback, the server will improve the model so that it can propose more accurate sake plans the next time it generates a gift plan.

[1644] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[1645] The processing flow will be explained below.

[1646] Step 1: Collect user data

[1647] Subject: Server

[1648] The server collects user transaction history data and location data, including user payment history, residential location information, and in-app activity history.

[1649] Specific behavior:

[1650] The server runs SQL queries against the transaction history database to retrieve past payment records.

[1651] Registered address and GPS data are retrieved from the user profile database.

[1652] Collect user behavior logs within the app (page visits, purchase history).

[1653] Step 2: Generate a preference profile

[1654] Subject: Server

[1655] The server inputs the collected data into a generative AI model to generate a user preference profile.

[1656] Specific behavior:

[1657] The server preprocesses the collected data and converts it into an input format for the AI ​​model.

[1658] The preprocessed data is input into a generative AI model to analyze user preference patterns.

[1659] The generated preference profile is stored in a database.

[1660] Step 3: Recognizing emotional states using the emotion engine

[1661] Subject: Terminal

[1662] The terminal collects the user's facial expression data, voice data, and character input data and sends them to the emotion engine, which recognizes the user's current emotional state.

[1663] Specific behavior:

[1664] The device uses the front camera to capture the user's facial expressions.

[1665] A microphone is used to record the user's voice and the voice data is sent to the emotion engine.

[1666] Character data is collected from touch and keyboard input and sent to the emotion engine.

[1667] The emotion engine analyzes the received data and recognizes the user's emotional state.

[1668] Step 4: Create a personalized reward plan

[1669] Subject: Server

[1670] The server uses a generative AI model to generate multiple gift plans suitable for the user based on the user's preference profile and the recognition results of the emotion engine.

[1671] Specific behavior:

[1672] The server inputs preference profile data and emotion recognition results into the AI ​​model.

[1673] The AI ​​model searches for rewards that match the user's donation limit and generates a list.

[1674] Create a list of suggested gift plans and send it to the user.

[1675] Step 5: Present your gift plan

[1676] Subject: Terminal

[1677] The terminal receives the return gift plan sent from the server and displays it on the user interface.

[1678] Specific behavior:

[1679] The terminal parses the received gift plan data.

[1680] Bind the parsed data to a UI component to display the details.

[1681] The display format is presented to the user according to the emotional state.

[1682] Step 6: Select a reward plan and apply

[1683] Subject: User

[1684] The user selects the desired plan from the presented return gift plans and completes the donation application procedure.

[1685] Specific behavior:

[1686] The user taps the selection button on the screen to select the desired gift plan.

[1687] Enter your donation amount and shipping information.

[1688] Click the Apply button to complete the process.

[1689] Step 7: Gather feedback

[1690] Subject: User

[1691] The user inputs a rating and comment on the gift they received.

[1692] Specific behavior:

[1693] Users can access the in-app rating form and provide feedback about the gift.

[1694] The terminal transmits the input feedback data to the server.

[1695] Step 8: Analyze feedback and improve your next plan

[1696] Subject: Server

[1697] The server analyzes the collected feedback and applies it to the generative AI model and emotion engine, thereby improving the accuracy of the next reward plan.

[1698] Specific behavior:

[1699] The server stores the feedback data in a database.

[1700] The feedback data is input into a generative AI model and used as training data for the model.

[1701] The improved AI model will be used to generate the next reward plan.

[1702] Example 2

[1703] 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."

[1704] Current hometown tax donation systems do not adequately provide personalized suggestions that take into account users' preferences and emotional state. This makes it difficult for users to find the perfect gift from the many options available, resulting in a lack of improvement in the user experience. Furthermore, the lack of effective feedback collection and improvement of the next proposal makes it difficult to continuously improve user satisfaction.

[1705] 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.

[1706] In this invention, the server includes means for collecting user transaction history data and location data, means for analyzing the user transaction history data and location data to generate a user preference profile, means for analyzing the user's facial expression data, voice data, and character input data to recognize the user's emotional state, means for proposing multiple gift plans generated based on the preference profile and emotional state, means for presenting the gift plans to the user terminal and allowing the user to apply for a donation for the gift selected by the user, and means for collecting feedback on the gift from the user to improve the accuracy of the next proposal. This not only makes it possible to propose the most suitable gift plan for the user, but also makes it possible to continuously improve user satisfaction.

[1707] "Transaction history data" refers to data that includes detailed information about purchases and payments made by a user in the past.

[1708] "Location data" is data that includes information about a user's geographic location, such as where the user lives or where the user visits.

[1709] A "preference profile" is information that indicates a user's preferences, interests, and concerns, and is generated by analyzing the user's transaction history data and location data.

[1710] "Facial expression data" is data that includes information about the user's facial expression.

[1711] "Voice data" is data that includes information about the voice uttered by the user.

[1712] "Character input data" refers to data that includes character information input by a user via a keyboard or touch screen.

[1713] "Emotional state" is information indicating the emotional state of the user that is recognized by analyzing facial expression data, voice data, and character input data.

[1714] A "return gift plan" is a specific option for a return gift for hometown tax donations that is proposed to the user.

[1715] "Feedback" is information indicating opinions and impressions, such as ratings and comments, regarding the gift received by the user.

[1716] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate the optimal reward plan for each user.

[1717] A "prompt" is input text that is fed into a generative AI model to instruct it on a specific task.

[1718] The present invention is a system that collects and analyzes a user's transaction history data and location data to provide a personalized gift plan suited to the user, and further proposes a gift that takes into account the user's emotional state using an emotion engine. This system includes a server, a user terminal, a generative AI model, and an emotion engine. Specific embodiments are described below.

[1719] The server first collects the user's transaction history data and location data through the API. The transaction history data includes payment history, purchase history, etc., and this data is stored in a database such as MySQL or PostgreSQL. For example, the server collects and analyzes data such as the user's past purchases of luxury ingredients and local specialties.

[1720] The server then analyzes the collected transaction history data and location data to generate a user preference profile. This analysis is performed using data analysis tools such as the Python pandas library and SQL queries. The analysis results are saved in JSON format and stored on the server.

[1721] The emotion engine acquires facial expression data, voice data, and text input data from the user's device. This data is collected through hardware such as a camera and microphone. The server analyzes this data using an emotion analysis library such as Microsoft Azure's Cognitive Services to recognize the user's emotional state. For example, it recognizes facial expressions while the user is operating the app and infers emotions from voice input.

[1722] Next, the server uses a generative AI model to generate a gift plan suited to the user based on the preference profile and the recognition results of the emotion engine. The generative AI model uses machine learning algorithms and cloud-based AI services. The AI ​​model operates based on the prompt text, and generates a gift plan for sake based on the fact that "User A is interested in sake and his current emotional state is positive."

[1723] The user's device receives the gift plans sent from the server and presents them to the user within the app. Each gift plan is displayed in detail in a format that reflects the user's emotional state. For example, the device might display plans such as "Junmai sake from Niigata Prefecture" and "Daiginjo sake from Yamagata Prefecture."

[1724] Users select the desired gift plan from the presented plans and complete the donation application process within the app. This includes entering the donation amount, shipping information, and confirmation. The user's device will then complete a set procedure and send the information to the server, completing the donation application.

[1725] In addition, users can enter ratings and comments about the gifts they receive and send the feedback to the server. The server analyzes this feedback and uses it to improve the accuracy of the next gift plan generation. For example, if the gift is highly rated, the server can use that information to adjust the algorithm of the AI ​​model for generation.

[1726] As a result, the system of the present invention can realize personalized hometown tax return gift suggestions that take into account the user's emotional state, thereby continuously improving user satisfaction.

[1727] Specific prompt examples:

[1728] 1. "Please generate a recommended gift plan for user A based on their donation history."

[1729] 2. "Analyze User A's latest emotional state and provide personalized suggestions based on it."

[1730] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1731] Program processing flow

[1732] Step 1:

[1733] User Data Collection

[1734] Subject: Server

[1735] The server collects user transaction history data and location data through the API. This data includes payment history, purchase history, and location information. This data is stored in a database such as MySQL or PostgreSQL. Specifically, the server uses the REST API to obtain data on products the user has purchased in the past and saves it in the database as entries including date, time, category, and location information.

[1736] Input: Raw transaction history and location data obtained via API

[1737] Output: Structured data stored in a database

[1738] Step 2:

[1739] Generating a preference profile

[1740] Subject: Server

[1741] The server analyzes the collected transaction history data and location data to generate a user preference profile. The analysis uses Python's pandas library to calculate the purchase frequency for each category. The results are saved in JSON format. Specifically, the number of purchases and purchase amounts for each category are tallied to generate each user's preference profile.

[1742] Input: Transaction history data and location data stored in a database

[1743] Output: User preference profile in JSON format

[1744] Step 3:

[1745] Recognition of emotional states

[1746] Subject: Server

[1747] The server uses an emotion engine to analyze facial expression data, voice data, and text input data sent from the user's device. It uses Microsoft Azure's Cognitive Services to determine the user's emotional state from their facial expression. Specifically, it analyzes captured image and voice data and generates emotion labels such as positive, negative, and neutral.

[1748] Input: Facial expression data, voice data, and text input data sent from the user device

[1749] Output: Emotional state data in JSON format

[1750] Step 4:

[1751] Generate a gift plan

[1752] Subject: Server

[1753] The server uses a generative AI model to generate a gift plan suited to the user based on their preference profile and emotional state data. During this process, a machine learning algorithm is used to analyze the data and generate multiple gift plans. Specifically, based on the prompt text, the AI ​​model creates a plan that recommends gifts that best suit the user's preferences and current emotional state.

[1754] Input: Preference profile, emotional state data

[1755] Output: Multiple gift plans generated by the generative AI model

[1756] Step 5:

[1757] Presentation of return gift plan

[1758] Subject: Terminal

[1759] The user's device receives the gift plans sent from the server and presents them to the user within the app. The plans are displayed in detail in a format that corresponds to the user's emotional state. Specifically, a UI framework is used to display an image and description of each gift plan on the screen.

[1760] Input: Gift plan received from the server

[1761] Output: Gift plan displayed on the user's device screen

[1762] Step 6:

[1763] Selecting a reward plan and applying

[1764] Subject: User

[1765] Users select the desired gift plan from the presented options and complete the donation application process within the app. They enter the necessary information and confirm it on the confirmation screen to complete the application. Specifically, they enter the donation amount and shipping address information, and then confirm it.

[1766] Input: The gift plan presented, the donation amount and shipping information entered by the user

[1767] Output: Donation request data sent to the server

[1768] Step 7:

[1769] Collecting feedback

[1770] Subject: User

[1771] Users can enter their ratings and comments about the gifts they receive in the app. Specifically, they enter their opinions and thoughts in the rating form and press the submit button.

[1772] Input: Ratings and comments entered by users

[1773] Output: Feedback data sent to the server

[1774] Step 8:

[1775] Analyze feedback and improve your next plan

[1776] Subject: Server

[1777] The server analyzes the received feedback and reflects it in the generative AI model and emotion engine. This improves the accuracy of generating the next reward plan. Specifically, it analyzes the evaluation content using natural language processing and adjusts the model parameters.

[1778] Input: Feedback data

[1779] Output: Next reward plan with improved generative AI model and emotion engine

[1780] The above processing steps make it possible to propose personalized gift plans based on the user's preferences and emotional state, thereby improving user satisfaction.

[1781] (Application example 2)

[1782] 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."

[1783] Conventional hometown tax donation systems often offer a uniform gift proposal without considering the user's preferences or emotional state, resulting in low user satisfaction. Furthermore, proposals based solely on transaction history and location data are unable to provide detailed proposals that reflect the user's emotional state. Therefore, there is a need for a system that offers personalized gift proposals and application procedures that take the user's emotional state into account.

[1784] The identification process by the identification 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 collecting user transaction history data, means for analyzing the transaction history data and generating a user preference profile, means for recognizing the user's emotional state, and means for proposing multiple personalized return gifts to the user based on the emotional state and preference profile. This enables detailed suggestions that take the user's emotional state into consideration.

[1785] "Transaction history data" refers to data that records the purchases and payments that a user has made in the past.

[1786] A "preference profile" is a collection of information indicating a user's preferences and interests, obtained by analyzing the user's transaction history data.

[1787] The "emotional state" is a state that indicates the user's current emotions and mood, and is information acquired through facial expression recognition and voice analysis.

[1788] "Personalized offers" refer to personalized offers of rewards or services that are tailored to a user based on that user's preference profile and emotional state.

[1789] "Feedback" refers to evaluations and comments on the gift provided by the user, and is information used to improve the accuracy of suggestions next time.

[1790] "Location data" is data that indicates the user's current location and past travel routes.

[1791] The "donation application procedure" is a procedure for officially making a donation for the return gift selected by the user.

[1792] The "donation limit" refers to the upper limit of donations set when a user receives tax benefits.

[1793] "Documents" refers to official documents, consent forms, etc. required for donation applications.

[1794] "Progress" is information that indicates the current status or stage of the donation application process.

[1795] The system of the present invention collects and analyzes users' transaction history data and location data to provide personalized gift plans suited to the users, and uses an emotion engine to suggest gift plans that take into account the user's emotional state. This system includes a server, a user terminal, a generative AI model, and an emotion engine.

[1796] Program processing explanation

[1797] Hardware and Software

[1798] 1. Server:

[1799] Collected Data: Collects user transaction history data and location data.

[1800] Analytical Data: Collected data is analyzed to generate a profile of your preferences.

[1801] Emotion engine: Analyzes the user's facial expression and voice data to recognize their emotional state.

[1802] Generative AI model: Generates personalized reward offers based on the user's preference profile and emotional state.

[1803] 2. User Device:

[1804] Received plan: Receives the reward plan sent from the server and presents it to the user within the app.

[1805] Selection procedure: Select the desired reward plan from the presented plans.

[1806] Application process: Complete the donation application process within the app.

[1807] Feedback: Enter your feedback on the proposed reward.

[1808] Data processing and calculation

[1809] 1. User Data Collection and Analysis:

[1810] The server collects the user's transaction history and location data through API calls, and uses the collected data to create a user preference profile. For example, it analyzes data such as the user's past purchases of luxury ingredients or local specialties to identify the user's interests.

[1811] 2. Recognition of emotional states:

[1812] The server uses an emotion engine to analyze the user's facial expression and voice data. It recognizes the user's current emotional state and uses it, along with their preference profile, to generate a reward plan. For example, if the user is smiling while using the app, it is determined to be in a positive emotional state.

[1813] 3. Generate personalized reward plans:

[1814] The server uses the generative AI model to generate multiple gift plans suitable for the user based on their preference profile and emotional state. For example, if the user is interested in sake and their current emotional state is positive, the server generates gift plans for sake offered by local governments across the country.

[1815] Examples of concrete examples and prompts

[1816] Examples:

[1817] Usage: Proposing a cashback campaign to improve the hometown tax donation experience on smartphone apps.

[1818] Input: transaction history, location, facial expression images, audio clips.

[1819] Output: A reward plan that matches the user's preferences and emotional state.

[1820] Example prompts to be input to the generative AI model

[1821] text

[1822] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[1823] User data: {user_data}

[1824] Emotional state: {emotion}

[1825] In this way, the present invention can realize a personalized hometown tax payment procedure that takes into account the user's emotional state, thereby improving user satisfaction.

[1826] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1827] Step 1:

[1828] The server collects user transaction history data and location data through API calls. This includes the user's past purchases and payments, as well as current and past location information. The collected data is stored in a database. The input is the user ID, and the output is the transaction history data and location data.

[1829] Step 2:

[1830] The server analyzes the collected transaction history data and generates a user preference profile. For example, it analyzes whether the user likes a particular category of products or which services they frequently use. The input is the transaction history data, and the output is the user preference profile.

[1831] Step 3:

[1832] The user terminal collects the user's facial expression and voice data through a camera and microphone. These data are transmitted to the server. The input is the user's facial expression image and voice clip, and the output is the transmitted data.

[1833] Step 4:

[1834] The server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This determines whether the emotion is positive or negative based on factors such as smiles and tone of voice. The input is facial expression images and voice data, and the output is the recognized emotional state.

[1835] Step 5:

[1836] The server uses a generative AI model to generate personalized gift plans based on the user's preference profile and emotional state. For example, if a user is interested in sake and has a positive emotional state, multiple gift plans for sake are created. The input is the user's preference profile and emotional state, and the output is the gift plans.

[1837] Step 6:

[1838] The user device receives the gift plan sent from the server and presents it to the user within the app. The details of the gift plan are displayed on the screen in a design that corresponds to the user's emotional state. The input is the gift plan, and the output is the information presented to the user.

[1839] Step 7:

[1840] The user selects the desired plan from the presented gift plans and completes the donation application process within the app. Based on the selected plan, the user enters the required information. The input is the selected gift plan and application information, and the output is application completion information.

[1841] Step 8:

[1842] The server collects feedback from users. The feedback includes ratings and comments about the rewards. The collected feedback data is used to improve the accuracy of the next proposal. The input is feedback information, and the output is improvement data for the next proposal.

[1843] Step 9:

[1844] The server analyzes the collected feedback data and reflects it in the generative AI model and emotion engine. This improves the accuracy and quality of the next gift plan generation. The input is the feedback data, and the output is improved model and engine parameters.

[1845] (Example of a prompt)

[1846] Generate optimal cashback plans based on the user's transaction history data and current emotional state.

[1847] User data: {user_data}

[1848] Emotional state: {emotion}

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1854] 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.

[1855] 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).

[1856] 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.

[1857] 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."

[1858] 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.

[1859] 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).

[1860] 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.

[1861] 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.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] The following is further disclosed regarding the above embodiment.

[1871] (Claim 1)

[1872] A means for collecting user transaction history data;

[1873] means for analyzing the transaction history data and generating a user preference profile;

[1874] A means for proposing multiple return gifts suitable for the user;

[1875] A means for carrying out a donation application procedure for a gift selected by the user from among the proposed gifts;

[1876] A means to collect feedback from users about the rewards and improve the accuracy of the next proposal.

[1877] A system including:

[1878] (Claim 2)

[1879] means for collecting user location data;

[1880] A means for calculating the user's hometown tax donation upper limit based on the location data and transaction history data;

[1881] 10. The system of claim 1, comprising:

[1882] (Claim 3)

[1883] means for generating and transmitting to the user documents required for the donation request;

[1884] A means for updating the status of the donation request based on the gift plan selected by the user;

[1885] 10. The system of claim 1, comprising:

[1886] "Example 1"

[1887] (Claim 1)

[1888] A means for collecting user transaction history;

[1889] means for analyzing the transaction history and generating a user preference profile;

[1890] A means for generating a plurality of return gift plans based on the preference profile;

[1891] A means for displaying the generated return gift plan on a user terminal;

[1892] A means for carrying out a donation application procedure for the reward plan selected by the user;

[1893] A means to collect feedback from users about the rewards and improve the accuracy of the next proposal.

[1894] A system including:

[1895] (Claim 2)

[1896] means for collecting user location data;

[1897] means for calculating a user's donation limit based on the location data and transaction history;

[1898] 10. The system of claim 1, comprising:

[1899] (Claim 3)

[1900] means for generating and transmitting to the user documents required for the donation request;

[1901] A means for updating the progress of the donation application based on the gift plan selected by the user;

[1902] 10. The system of claim 1, comprising:

[1903] "Application Example 1"

[1904] (Claim 1)

[1905] A means for collecting user transaction history data;

[1906] means for analyzing the transaction history data and generating a user preference profile;

[1907] A means for proposing multiple return gifts or products suitable for the user;

[1908] A means for carrying out a purchase procedure for a gift or product selected by the user from the suggested gift or product;

[1909] A means for collecting feedback from users about the rewards or products to improve the accuracy of next suggestions;

[1910] A system including:

[1911] (Claim 2)

[1912] means for collecting user location data;

[1913] A means for calculating a maximum donation amount for the hometown tax payment of the user based on the location data and the transaction history data, and a means for using a generative AI model to identify the user's purchasing preferences;

[1914] 10. The system of claim 1, comprising:

[1915] (Claim 3)

[1916] means for generating and transmitting to the user documents required for the purchase process;

[1917] A means for updating the status of the purchase procedure based on the gift or product plan selected by the user, and a means for generating prompt sentences using the generative AI model to improve the accuracy of suggestions;

[1918] 10. The system of claim 1, comprising:

[1919] "Example 2: Combining Emotion Engines"

[1920] (Claim 1)

[1921] means for collecting user transaction history data and location data;

[1922] means for analyzing the transaction history data and location data to generate a user preference profile;

[1923] means for analyzing facial expression data, voice data, and character input data of a user to recognize the emotional state of the user;

[1924] A means for proposing a plurality of gift plans based on the preference profile and the emotional state;

[1925] A means for presenting a gift plan to a user terminal and carrying out a donation application procedure for the gift selected by the user;

[1926] A means to collect feedback from users about the rewards and improve the accuracy of the next proposal.

[1927] A system including:

[1928] (Claim 2)

[1929] 10. The system of claim 1, further comprising means for calculating a user's contribution limit.

[1930] (Claim 3)

[1931] means for generating and transmitting to the user documents required for the donation request;

[1932] A means for updating the status of the donation request based on the gift plan selected by the user;

[1933] 10. The system of claim 1, comprising:

[1934] "Application example 2 when combining emotion engines"

[1935] (Claim 1)

[1936] A means for collecting user transaction history data;

[1937] means for analyzing the transaction history data and generating a user preference profile;

[1938] means for recognizing the emotional state of a user;

[1939] a means for suggesting a plurality of personalized rewards to the user based on the user's emotional state and preference profile;

[1940] A means for carrying out a donation application procedure for a gift selected by the user from among the proposed gifts;

[1941] A means of collecting user evaluations of return gifts and improving the accuracy of next proposals;

[1942] A system including:

[1943] (Claim 2)

[1944] means for collecting user location data;

[1945] means for calculating a user's donation limit based on the location data and transaction history data;

[1946] 10. The system of claim 1, comprising:

[1947] (Claim 3)

[1948] means for generating and transmitting to the user documents required for the donation request;

[1949] A means for updating the progress of the donation request based on the reward plan selected by the user;

[1950] 10. The system of claim 1, comprising: [Explanation of symbols]

[1951] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting user transaction history data; means for analyzing the transaction history data and generating a user preference profile; A means for proposing multiple return gifts suitable for the user; A means for carrying out a donation application procedure for a gift selected by the user from among the proposed gifts; A means to collect feedback from users about the rewards and improve the accuracy of the next proposal. A system including:

2. means for collecting user location data; A means for calculating the user's hometown tax donation upper limit based on the location data and transaction history data; The system of claim 1 , comprising:

3. means for generating and transmitting to the user documents required for the donation request; A means for updating the status of the donation request based on the gift plan selected by the user; The system of claim 1 , comprising:

Images