System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring the property information of a user, a means for receiving budget information inputted by the user, a means including a generation AI for selecting the optimal merchandise on the basis of the property information and budget information of the user and online action history and trend information, a means for packaging and shipping the selected merchandise as lucky bag, and a means for notifying the user of the shipping information of the lucky bag.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] Traditional lucky bag delivery methods often included products that did not match the user's attributes or interests, resulting in low satisfaction. Furthermore, online shops faced the challenge of finding optimal products that match each user's needs, preventing sales growth. Furthermore, because it was not possible to customize lucky bags to reflect each user's budget, some users might perceive them as too expensive or too low in value. [Means for solving the problem]

[0005] The present invention provides a system that utilizes user attribute information (such as gender and age), online behavior history, and trend information to provide users with the most suitable lucky bag. Specifically, the system includes a means for acquiring user attribute information, a means for receiving budget information entered by the user, a means including a generation AI that selects the most suitable products based on the user's attribute information, budget information, online behavior history, and trend information, a means for packaging and shipping the selected products as a lucky bag, and a means for notifying users of lucky bag shipping information. This configuration makes it possible to provide users with lucky bags that match their interests and are within their budget, providing a high level of satisfaction, and also contributing to increased sales for online shops.

[0006] "Users" are consumers who use the system to receive lucky bags.

[0007] "Attribute information" refers to a user's gender, age, and other personal data.

[0008] "Budget information" is information entered by the user about the amount of money they wish to allocate to a particular lucky bag.

[0009] "Online behavior history" refers to data such as the user's online activity history, purchasing history, and browsing history.

[0010] "Trend information" is data about products and categories based on current market trends and fashions.

[0011] "Generative AI" is an artificial intelligence technology that comprehensively analyzes a user's attribute information, budget information, online behavior history, and trend information to select the most suitable product.

[0012] A "lucky bag" is a bag or box in which multiple products are packaged, either randomly or selected.

[0013] "Packaging" is the process of assembling and packaging the selected products into a lucky bag.

[0014] "Shipping means" refers to the method and system for physically delivering the packaged lucky bag to the user's address.

[0015] "Notification means" refers to a system related to email and messaging services that notify users of lucky bag shipping information. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[0038] 1. Obtaining user information

[0039] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[0040] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[0041] 2. Enter your budget

[0042] User: The user enters budget information into a budget entry form on the system.

[0043] On your device: Enter your budget information and it will be sent to the server.

[0044] Example: User A enters a budget of 5,000 yen.

[0045] 3. Product selection process

[0046] Server: The server obtains online behavior history and trend information based on the user's attribute information and budget information.

[0047] Server: Based on the acquired information, the generation AI module is called to select the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[0048] Example: For example, a generative AI selects sportswear and the latest gadget accessories for user A.

[0049] 4. Creation and shipping of lucky bags

[0050] Server: Sends the selected product list to the shipping system as a lucky bag list.

[0051] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[0052] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0053] 5. Notice to Users

[0054] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[0055] On your device: You will receive a notification on your device and be able to check the status.

[0056] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0057] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes and interests, and online shops can expect to increase their sales by providing products that meet the needs of each user.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] User: Log in to the system.

[0061] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[0062] Step 2:

[0063] Server: Authenticates the login information and obtains the user's attribute information.

[0064] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[0065] Step 3:

[0066] Server: Save the acquired user attribute information as session data.

[0067] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[0068] Step 4:

[0069] User: Enters a budget into a form where budget information is entered.

[0070] Users enter their budget in the input form displayed on the system.

[0071] Step 5:

[0072] Terminal: Sends the entered budget to the server.

[0073] Budget information from the input form is sent to the server.

[0074] Step 6:

[0075] Server: Save the received budget information as session data.

[0076] Add and save the budget information in the session data.

[0077] Step 7:

[0078] Server: Refers to the trend database based on the user's attribute information and budget information to obtain a list of trending products.

[0079] Access the trend database to retrieve the current trending product list and add it to the session data.

[0080] Step 8:

[0081] Server: Calls the generative AI module and passes the user information and trending product list as input.

[0082] User attribute information, budget information, and trending product lists are provided as input data to the generation AI.

[0083] Step 9:

[0084] Generative AI: Generates optimal product candidate lists, evaluates them, and selects the best combination.

[0085] Product scoring is performed taking into account the user's interest tags and online behavior history to generate an optimal product candidate list.

[0086] Step 10:

[0087] Server: Receives the optimal lucky bag candidate list output by the generation AI and saves it as a lucky bag item list.

[0088] The optimal product candidate list is saved as a lucky bag item list in the session data.

[0089] Step 11:

[0090] Server: Sends the lucky bag item list to the shipping system.

[0091] The lucky bag item list is formatted and the data is sent to the shipping system.

[0092] Step 12:

[0093] Shipping system: Picks and packages products based on the lucky bag item list.

[0094] The listed items are taken from the inventory system and packaged as lucky bags.

[0095] Step 13:

[0096] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[0097] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[0098] Step 14:

[0099] Server: Notify the user's contacts of the lucky bag shipping information.

[0100] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[0101] Step 15:

[0102] On your device: You will receive a notification on your device to check the status.

[0103] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[0104] Example 1

[0105] 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."

[0106] The previous system was unable to fully utilize user attributes, budget information, online behavior history, and trend information, making it difficult to select the most suitable product for each individual user. Furthermore, the packaging and shipping processes for selected products were inefficient, and notifications to users were sometimes delayed. This resulted in low user satisfaction and hindered the company's hopes of increasing sales.

[0107] 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.

[0108] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information, means for generating a list of selected products as a lucky bag list and sending it to a shipping system, means for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to a delivery company, and means for notifying the user of lucky bag shipping information. This makes it possible to efficiently select and ship optimal products for users and quickly notify them.

[0109] "User attribute information" is information that indicates individual characteristics of a user, such as gender, age, and interest tags.

[0110] "Budget information" is information indicating the budget amount for purchases entered by the user on the system.

[0111] "Online behavior history" is information that records a user's behavior on the Internet, such as browsing history and purchase history.

[0112] "Trend information" is information about products and services that are currently or recently popular.

[0113] "Generative AI" is an algorithm that uses artificial intelligence technology to select the most suitable product based on a user's attribute information, budget information, online behavior history, and trend information.

[0114] The "Lucky Bag List" is a list of the most suitable products selected by the generation AI.

[0115] The "shipping system" is a system that is responsible for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to the delivery company.

[0116] "Means of notification" refers to the means used to inform users of the shipping information for the lucky bag, and includes email and messaging services.

[0117] A "product candidate list" is a list of candidate products that the generation AI selects based on the user's information and that will ultimately be provided.

[0118] This invention relates to a system that uses generative AI to select the most suitable products for a user based on the user's attribute information, budget information, online behavior history, and trend information, and provides them as a lucky bag. This system consists of the following components:

[0119] 1. Obtaining user information

[0120] The server detects the user's login and retrieves the user's attribute information from an external database. The retrieved attribute information includes gender, age, and interest tags. This information is used as the basis for the generative AI to select appropriate products.

[0121] Example: When a 30-year-old male user A logs into the system, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from an external database and saves them as session information.

[0122] 2. Enter your budget

[0123] The user enters budget information into a budget input form on the system's UI. The entered budget information is sent to the server via the terminal and saved. This budget information is used as one of the product selection criteria.

[0124] Example: User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[0125] 3. Product selection process

[0126] The server retrieves relevant online behavior history and the latest trend information based on the user's attribute information and budget information, which is obtained from external APIs and internal databases.

[0127] Next, the server calls the generation AI module and inputs the user information, budget, and the acquired behavior history and trend information as a prompt sentence, which is as follows:

[0128] text

[0129] User Attributes:

[0130] Gender: Male

[0131] Age: 30

[0132] Interests Tags: Sports, Technology

[0133] Budget: 5,000 yen

[0134] Suggest three products that are ideal for this user. Consider the latest trending products and the user's past purchasing history as criteria for product selection.

[0135] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[0136] Example: Generative AI selects, for example, sportswear and the latest gadget accessories based on user A's interests and budget.

[0137] 4. Creation and shipping of lucky bags

[0138] The server creates a list of selected products as a lucky bag list and sends it to the shipping system. The shipping system picks up products from inventory based on this lucky bag list and packages them. The packaged lucky bags are then handed over to a delivery company.

[0139] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0140] 5. Notice to Users

[0141] The server will notify the user's contacts of the lucky bag delivery information via email or messaging service, and the device will receive the notification and allow the user to check the delivery status of the lucky bag.

[0142] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] A user logs into the system

[0146] Specific behavior:

[0147] A user logs in to the system.

[0148] The server detects that the user has logged in and obtains the user ID.

[0149] Input: User ID

[0150] Output: User session information

[0151] Data processing / calculation:

[0152] The server generates session information corresponding to the user ID and saves it for use in subsequent processing.

[0153] Step 2:

[0154] Get user attribute information

[0155] Specific behavior:

[0156] The server retrieves user attribute information from an external database, including gender, age, and interest tags.

[0157] The acquired attribute information is added to the session information in the server.

[0158] Input: User ID

[0159] Output: User demographic information (gender, age, interest tags)

[0160] Data processing / calculation:

[0161] The server queries an external database to obtain user attribute information and stores it in session information.

[0162] Examples:

[0163] When a 30-year-old male user A logs in, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from the database and adds them to the session information.

[0164] Step 3:

[0165] Entering budget information

[0166] Specific behavior:

[0167] The user enters budget information into the budget input form on the system UI.

[0168] The terminal transmits the input budget information to the server.

[0169] The server adds the budget information to the session information.

[0170] Input: Budget Information

[0171] Output: Updated session information

[0172] Data processing / calculation:

[0173] The server receives the budget information sent from the terminal and stores it in the session information.

[0174] Examples:

[0175] User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[0176] Step 4:

[0177] Acquisition of online behavior history and trend information

[0178] Specific behavior:

[0179] The server retrieves the user's online behavior history and the latest trend information from external APIs and internal databases.

[0180] Add the acquired information to the session information.

[0181] Input: User demographic information and budget information

[0182] Output: User's online behavior history and trend information

[0183] Data processing / calculation:

[0184] The server calls external APIs to retrieve behavioral history and trend information that matches the user's attribute information, and also retrieves related information from the internal database.

[0185] Examples:

[0186] The server retrieves recent online behavior and trend information related to "sports" and "technology" from the API and stores it internally.

[0187] Step 5:

[0188] Product selection using generative AI models

[0189] Specific behavior:

[0190] The server calls the generation AI module and inputs user information, budget, and acquired behavioral history and trend information as prompt statements.

[0191] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[0192] Input: Prompt text (user demographic information, budget, online behavior history, trend information)

[0193] Output: Product candidate list

[0194] Data processing / calculation:

[0195] The generation AI analyzes the prompt text, scores the best products for the user, generates a list of optimal product candidates, and outputs it to the server.

[0196] Examples:

[0197] The generative AI selects sportswear and the latest gadget accessories based on User A's interests and budget.

[0198] Step 6:

[0199] Creating a lucky bag list and preparing for shipping

[0200] Specific behavior:

[0201] The server generates a lucky bag list from the product candidate list received from the generation AI.

[0202] Send the lucky bag list to the shipping system.

[0203] The shipping system will pick up items from inventory based on the lucky bag list and package them.

[0204] Hand over the packaged lucky bag to the delivery company.

[0205] Input: Product Suggestion List

[0206] Output: Lucky bag list and packaged lucky bags

[0207] Data processing / calculation:

[0208] The server generates a lucky bag list based on the product candidate list and sends it to the shipping system, which checks inventory information, packages the relevant products, and hands them over to the delivery company.

[0209] Examples:

[0210] A lucky bag containing sportswear and gadget accessories is generated and shipped to User A's address.

[0211] Step 7:

[0212] User Notification

[0213] Specific behavior:

[0214] The server notifies the user's contact information about the delivery of the lucky bag.

[0215] The device will display notifications to the user and allow them to check the shipping status.

[0216] Input: Lucky bag shipping information

[0217] Output: A notification message to the user

[0218] Data processing / calculation:

[0219] The server sends notifications to the user's contacts based on information from the shipping system, and the device displays the received notifications to the user.

[0220] Examples:

[0221] User A receives a message on his smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0222] (Application example 1)

[0223] 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."

[0224] In today's online shopping world, proposing optimal products to users and offering them as lucky bags is an important factor in increasing user satisfaction. Conventional systems make it difficult to select optimal products based on the user's attributes and individual budget, and the products actually offered often do not meet the user's expectations. Efficiently notifying users of shipping information is also a challenge. In particular, smartphone applications are rarely used to provide users with real-time information, and this issue is also in need of improvement.

[0225] 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.

[0226] In this invention, the server includes: means for acquiring user attribute information; means for receiving budget information entered by the user; means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information; means for packaging and shipping the selected products as a lucky bag; means for notifying the user of lucky bag shipping information; means for having a smartphone application and displaying it using the generation AI; means for providing the user with shipping information and notifications through the smartphone application; and means for prompting the generation AI based on the above information and trend information. This makes it possible to select optimal products based on the user's individual attribute information and budget and provide them as a lucky bag, and to efficiently notify the user.

[0227] "User attribute information" is information that indicates characteristics such as the user's gender, age, and interests.

[0228] "Budget information" is information that indicates the upper limit of the amount set by the user when making a purchase.

[0229] "Online behavior history" is information that represents the history of operations, selections, browsing, etc. that a user has performed on the Internet.

[0230] "Trend information" refers to information that indicates currently popular products and services and trends in fashion.

[0231] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate items or results that are optimal for a specific purpose.

[0232] A "lucky bag" is a type of product that offers multiple different products in one package.

[0233] "Packaging" is the process of packing selected items and preparing them for shipment.

[0234] A "smartphone application" is software that runs on a smartphone device and is a program that provides specific functions or services.

[0235] A "notification" is a means of conveying information from a system to a user, typically via email or push notification.

[0236] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[0237] 1. Obtaining user information

[0238] When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[0239] As a specific example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are "sports" and "technology."

[0240] 2. Enter your budget

[0241] The user enters budget information into a budget entry form on the system. Once the terminal inputs the budget information, the information is sent to the server.

[0242] For example, a user enters a budget of "5,000 yen."

[0243] 3. Product selection process

[0244] The server acquires online behavior history and trend information based on the user's attribute information and budget information. Based on the acquired information, it calls the generation AI module and selects the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[0245] As a concrete example, generative AI will select sportswear and the latest gadget accessories for users.

[0246] 4. Creation and shipping of lucky bags

[0247] The server sends the selected product list as a lucky bag list to the shipping system. The shipping system picks up each product from inventory based on the lucky bag list and packages it. Once packaged, the lucky bag is handed over to the delivery company.

[0248] As a specific example, a lucky bag containing sportswear and gadget accessories is generated and shipped to the user's address.

[0249] 5. Notice to Users

[0250] The server will notify the user's contacts of the delivery of the lucky bag. Notifications will be sent via email or messaging services. The user will receive a notification on their device and can check the status.

[0251] As a concrete example, a user receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0252] In this invention, the generative AI creates prompts based on the user's attribute information, budget information, online behavior history, and trend information, and then selects the most suitable product based on these.The main hardware and software used include a server, user terminal, generative AI model, database, inventory management system, delivery system, notification service, mail server, etc.

[0253] Example prompt sentence:

[0254] Based on the user's demographic information (gender: male, age: 30, interests: sports, technology), online behavior history, budget: 5,000 yen, and a list of trending products (e.g., "smartwatch," "sneakers," "headphones"), select the most suitable products for the user and generate a lucky bag.

[0255] By implementing the present invention, it is possible to select optimal products based on the user's individual attribute information and budget, and offer them as lucky bags, and to efficiently notify users. This can improve user satisfaction and is expected to increase sales for online shops.

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1:

[0258] Retrieving User Information

[0259] When a user logs in to the system, the server retrieves the user's attribute information from an external database. The retrieved information includes gender, age, and interest tags. Based on this information, a personalized profile of the user is generated. The input is the user ID, and the output is the user's attribute information.

[0260] Step 2:

[0261] Enter your budget

[0262] The user enters budget information into a form on the system. The terminal retrieves this information and sends it to the server. The input is the budget amount entered by the user, and the output is the budget information sent to the server.

[0263] Step 3:

[0264] Acquisition of online behavior history and trend information

[0265] The server obtains users' online behavioral history and trend information from external sources. The behavioral history includes past purchase history and browsing history, and the trend information includes currently popular products and services. The input is a request for online behavioral history and trend information, and the output is the obtained behavioral history and trend information.

[0266] Step 4:

[0267] Generate prompt statement

[0268] The server creates a prompt for the generative AI model based on the acquired user attribute information, budget information, online behavior history, and trend information. This prompt is used to request a list of optimal products from the generative AI model. The input is the user's composite information, and the output is the prompt.

[0269] Step 5:

[0270] Selection of the optimal product

[0271] The server calls the generative AI model and inputs a prompt to select the most suitable product. The generative AI model considers the user's interests and trends, performs scoring, and selects products. The input is the prompt, and the output is a list of selected products.

[0272] Step 6:

[0273] Creation of lucky bags

[0274] The server generates a list of selected products as a lucky bag list and sends it to the shipping system. At this time, it checks the inventory status of the products and issues instructions to pick up the products from the inventory. The input is the product list, and the output is the lucky bag list.

[0275] Step 7:

[0276] Shipping and Packaging

[0277] The shipping system picks up products from inventory based on the lucky bag list and packages them. Once packaged, the lucky bags are handed over to the delivery company. The input is the lucky bag list, and the output is the packaged lucky bag.

[0278] Step 8:

[0279] User Notification

[0280] The server notifies the user's contacts of the lucky bag shipping information. Notifications are sent via email or push notification. The user receives a notification on their device and can check the status. The input is the shipping information, and the output is a notification message to the user.

[0281] 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.

[0282] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[0283] The system consists of the following components:

[0284] 1. Obtaining user information

[0285] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[0286] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[0287] 2. Enter your budget

[0288] User: The user enters budget information into a budget entry form on the system.

[0289] On your device: Enter your budget information and it will be sent to the server.

[0290] Example: User A enters a budget of 5,000 yen.

[0291] 3. Acquiring emotional information

[0292] Server: Calls the emotion engine that analyzes the user's online behavior and input data to obtain the user's emotional information.

[0293] Server: Save the acquired user emotion information as session data.

[0294] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[0295] 4. Product selection process

[0296] Server: Calls the generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[0297] Generative AI: Analyzes each data item in a comprehensive manner, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[0298] Example: For example, a generative AI might select a combination of sportswear and the latest gadget accessories for user A.

[0299] 5. Creation and shipping of lucky bags

[0300] Server: Sends the selected product list to the shipping system as a lucky bag list.

[0301] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[0302] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0303] 6. Notice to Users

[0304] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[0305] On your device: You will receive a notification on your device and be able to check the status.

[0306] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0307] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[0308] The processing flow will be explained below.

[0309] Step 1:

[0310] User: Log in to the system.

[0311] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[0312] Step 2:

[0313] Server: Authenticates the login information and obtains the user's attribute information.

[0314] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[0315] Step 3:

[0316] Server: Save the acquired user attribute information as session data.

[0317] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[0318] Step 4:

[0319] User: Enters a budget into a form where budget information is entered.

[0320] Users enter their budget in the input form displayed on the system.

[0321] Step 5:

[0322] Terminal: Sends the entered budget to the server.

[0323] Budget information from the input form is sent to the server.

[0324] Step 6:

[0325] Server: Save the received budget information as session data.

[0326] Add and save the budget information in the session data.

[0327] Step 7:

[0328] Server: Calls the emotion engine to analyze users' online behavior and obtain emotion information.

[0329] The emotion engine analyzes and extracts emotional information based on the user's browsing history and input data.

[0330] Step 8:

[0331] Server: Add the acquired emotion information to the session data and save it.

[0332] The emotional information (e.g., excitement, anticipation) output by the emotion engine is stored in the session data.

[0333] Step 9:

[0334] Server: Inputs data into the generative AI module based on the user's attribute information, budget information, sentiment information, and trend information, and selects the most suitable product.

[0335] Generative AI analyzes data and generates product lists suitable for users.

[0336] Step 10:

[0337] Generative AI: It scores products based on user interest tags and emotional information to generate an optimal list of product candidates.

[0338] Save the product candidate list in session data.

[0339] Step 11:

[0340] Server: Save the optimal product list output by the generation AI as a lucky bag item list.

[0341] Convert the product list into a lucky bag item list and save it.

[0342] Step 12:

[0343] Server: Sends the lucky bag item list to the shipping system.

[0344] The lucky bag item list is formatted and the data is sent to the shipping system.

[0345] Step 13:

[0346] Shipping system: Picks and packages products based on the lucky bag item list.

[0347] The listed items are taken from the inventory system and packaged as lucky bags.

[0348] Step 14:

[0349] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[0350] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[0351] Step 15:

[0352] Server: Notify the user's contacts of the lucky bag shipping information.

[0353] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[0354] Step 16:

[0355] On your device: You will receive a notification on your device to check the status.

[0356] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[0357] Example 2

[0358] 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."

[0359] Conventional online shopping systems can select products based on user attributes and budget information, but they have the problem of not being able to select products that take into account user emotions or real-time trend information. As a result, user satisfaction declines and sales growth cannot be expected. Furthermore, there has been no effective system for providing users with the best lucky bags.

[0360] 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.

[0361] In this invention, the server includes a means for acquiring user attribute information, a means for receiving budget information entered by the user, and a means for selecting optimal products in consideration of the user's emotional state based on the user's attribute information, budget information, online behavior history, analysis results by the generation AI, and trend information. This makes it possible to provide optimal lucky bags that reflect the user's emotions and real-time trends.

[0362] "User attribute information" refers to personal characteristics such as the user's gender, age, and tags that indicate their interests.

[0363] "Budget Information" means the amount information entered by a User within the System to be used for purchases.

[0364] "Online behavior history" refers to the history of a user's activities on the Internet, and specifically includes browsing history, purchasing history, etc.

[0365] "Generative AI" refers to artificial intelligence that analyzes input data and selects products that are suitable for the user.

[0366] "Trend information" refers to information that shows current trends in the market and consumer interests.

[0367] "Emotional state" refers to emotions analyzed from a user's online behavior and input data, such as joy, excitement, and anticipation.

[0368] A "lucky bag" is a package containing a selection of multiple products.

[0369] "Packaging" refers to the process of wrapping selected products and assembling them into a single package.

[0370] "Notification" refers to a means of informing users of lucky bag shipping information, and is done via email or messaging services.

[0371] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion analysis engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[0372] The system consists of the following components:

[0373] 1. Obtaining user information

[0374] When a user logs into the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[0375] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[0376] 2. Enter your budget

[0377] The user enters budget information into a budget entry form on the system.

[0378] The terminal sends the information to the server.

[0379] Example: User A enters a budget of 5,000 yen.

[0380] 3. Acquiring emotional information

[0381] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[0382] The emotion information acquired by the server is saved as session data.

[0383] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[0384] 4. Product selection process

[0385] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[0386] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[0387] Example: A generative AI selects a combination of sportswear and the latest gadget accessories for user A.

[0388] Example prompt sentence:

[0389] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[0390] Budget: 5,000 yen

[0391] Emotional information: excitement, anticipation

[0392] Online behavior history: browsing sports-related sites, reading gadget review articles

[0393] Latest trends: new sportswear, hot gadget accessories

[0394] 5. Creation and shipping of lucky bags

[0395] The server transmits the selected product list to the shipping business system as a lucky bag list.

[0396] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[0397] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0398] 6. Notice to Users

[0399] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[0400] The device will notify the user.

[0401] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0402] The above is a specific embodiment of the present invention. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[0403] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0404] Step 1:

[0405] Obtaining user attribute information

[0406] A user logs in to the system.

[0407] Specific operation: User A enters his / her username and password and clicks the login button.

[0408] Input: Username and Password

[0409] Output: After successful login, the user ID is obtained.

[0410] The server accesses an external database and obtains the user's attribute information (gender, age, interest tags).

[0411] Specific operation: The server executes an SQL query and saves the retrieved attribute data of User A ("Male", "30 years old", "Sports", "Technology") as session data.

[0412] Input: User ID

[0413] Output: User demographic information (gender, age, interest tags)

[0414] Step 2:

[0415] Enter your budget

[0416] Access a form where users enter budget information.

[0417] Specific behavior: User A opens the budget input form.

[0418] Input: Budget input form URL

[0419] Output: The budget entry page is displayed.

[0420] The user enters budget information and clicks the submit button.

[0421] Specific actions: User A enters "5,000 yen" and presses the send button.

[0422] Input: Budget information (5000 yen)

[0423] Output: The form data is sent to the server.

[0424] The terminal transmits the input budget information to the server.

[0425] What happens: The form sends an HTTP POST request to the server, and the budget information arrives at the server.

[0426] Input: Budget information (5000 yen)

[0427] Output: Budget information is saved on the server.

[0428] Step 3:

[0429] Acquiring emotional information

[0430] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[0431] Specific operation: The server sends an API request to send user A's browsing history and form input data to the sentiment analysis engine.

[0432] Input: Online behavioral data, input data

[0433] Output: Emotional information (e.g., "excitement," "anticipation")

[0434] The emotion information acquired by the server is saved as session data.

[0435] Specific operation: The server stores the emotion information received from the emotion analysis engine in the session database.

[0436] Input: Emotion information

[0437] Output: Emotion information is added to the session data.

[0438] Step 4:

[0439] Product selection process

[0440] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[0441] Specific operation: The server generates a prompt and sends it to the generation AI. Prompt text:

[0442] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[0443] Budget: 5,000 yen

[0444] Emotional information: excitement, anticipation

[0445] Online behavior history: browsing sports-related sites, reading gadget review articles

[0446] Latest trends: new sportswear, hot gadget accessories

[0447] Input: Attribute information, budget information, sentiment information, behavioral history, trend information

[0448] Output: Selected product candidate list

[0449] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[0450] How it works: Generative AI scores the candidate list and selects a combination of sportswear and gadget accessories.

[0451] Input: prompt statement

[0452] Output: Optimal product mix

[0453] Step 5:

[0454] Creation and shipping of lucky bags

[0455] The server transmits the selected product list to the shipping business system as a lucky bag list.

[0456] Specific operation: The server sends an API request to send the product list to the packaging and delivery system.

[0457] Input: Selected product list

[0458] Output: Lucky bag list

[0459] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[0460] Specific operation: The warehouse system generates a picking list, and staff pack the items and hand them over to the delivery company.

[0461] Input: Lucky Bag List

[0462] Output: Packaged lucky bags

[0463] Step 6:

[0464] User Notification

[0465] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[0466] Specific operation: The server sends a message "Your lucky bag has been shipped" using email or a messaging service.

[0467] Input: Lucky bag shipping information

[0468] Output: Information message

[0469] The device will notify the user.

[0470] Specific behavior: A notification will pop up on User A's smartphone, allowing them to check the status.

[0471] Input: Notification message

[0472] Output: Notification display on smartphone

[0473] (Application example 2)

[0474] 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."

[0475] Conventional online shopping systems have mechanisms for suggesting products based on user attribute information and budget information, but they are unable to select products that take into account the user's emotional state, and therefore are unable to sufficiently increase user satisfaction. Furthermore, there is a need to provide a personalized shopping experience by proposing products that incorporate emotional information. Furthermore, offering selected products as lucky bags is also a challenge, as it provides a new shopping experience for users and increases their satisfaction.

[0476] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0477] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means for analyzing the user's online behavior and input data and acquiring emotional information, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, emotional information, and trend information, means for packaging and shipping the selected products as a lucky bag, and means for notifying the user of lucky bag shipping information. This makes it possible to select optimal products based on the user's attribute information, emotional information, and budget information, providing a personalized purchasing experience and high satisfaction.

[0478] "User attribute information" refers to basic information related to individual users, such as their gender, age, and interest tags.

[0479] "Budget information" is information indicating the upper limit of the amount set by the user for purchases.

[0480] "Online behavior" is a record of all actions a user takes on a website or application, including clicks, views, and purchases.

[0481] "Emotional information" is information that indicates a user's emotional state analyzed from their online behavior and input data.

[0482] "Generative AI" is artificial intelligence that generates optimal products based on user information.

[0483] "Trend information" is information about currently popular products and services.

[0484] A "lucky bag" is a package containing multiple selected products.

[0485] "Packaging" is the process of assembling the selected products into lucky bags.

[0486] "Shipping" is the process of sending the packaged lucky bag to the user.

[0487] "Notification" refers to the act of informing users of the shipping information for lucky bags.

[0488] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. A specific embodiment of this system is shown below.

[0489] When a user logs in to the system, the server first obtains the user's attribute information from an external database. This information includes gender, age, and interest tags. For example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are sports and technology.

[0490] Next, when the user inputs budget information through the terminal, the information is sent to the server. For example, if the user inputs a budget of 5,000 yen, this information is saved on the server.

[0491] The server then calls an emotion engine that analyzes the user's online behavior and input data to obtain the user's emotion information and save it as session data. For example, it may be analyzed to determine whether the user is feeling excitement or anticipation.

[0492] Based on this information, the server calls the AI ​​generator, which then comprehensively analyzes the user's attributes, budget, online behavior, trends, and emotional information to select the most suitable products. For example, the AI ​​generator can select a combination of sportswear and the latest gadget accessories for the user.

[0493] The server sends the selected product list as a lucky bag list to the shipping system, which then picks and packages the items from the inventory. For example, a lucky bag containing sportswear and gadget accessories is created and shipped to the user's address.

[0494] The server then notifies the user's contacts of the delivery of the lucky bag. Notifications are sent via email or messaging services, and the user can check the status on their own device. For example, the user might receive a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0495] This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can expect to increase user satisfaction and sales by providing products that meet the needs and emotions of each user.

[0496] Prompt Sentence Examples

[0497] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[0498] Budget: 5,000 yen

[0499] Emotional information: excitement, anticipation

[0500] Use this information to help you choose the best product for your users."

[0501] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0502] Step 1:

[0503] Get user attribute information

[0504] Subject: Server

[0505] Input: User logged into the system

[0506] Data processing and calculation: The server sends an API request to an external database to obtain the user's gender, age, and interest tags.

[0507] Output: User demographic information (e.g., gender - male, age - 30, interest tags - sports, technology)

[0508] Step 2:

[0509] Entering budget information

[0510] Subject: User

[0511] Input: The user inputs budget information through the device (e.g., 5,000 yen)

[0512] Data transmission: Budget information is sent from the device to the server.

[0513] Output: User's budget information saved on the server (e.g., 5,000 yen)

[0514] Step 3:

[0515] Acquiring emotional information

[0516] Subject: Server

[0517] Input: Your online behavior and input data

[0518] Data processing and calculation: The server calls the emotion analysis engine, analyzes the input data, and extracts the user's emotional information (e.g., excitement, anticipation).

[0519] Output: Obtained user sentiment information

[0520] Step 4:

[0521] Product selection process

[0522] Subject: Server

[0523] Input: User attribute information, budget information, online behavior history, trend information, emotional information

[0524] Data processing and calculation: The server inputs this information into the generation AI, which then selects the most suitable product based on the prompt (e.g., sportswear, the latest gadget accessories).

[0525] Output: List of selected products

[0526] Step 5:

[0527] Creation and shipping of lucky bags

[0528] Subject: Server and shipping system

[0529] Input: Selected product list

[0530] Data transmission and physical operation: The server sends the selected product list to the shipping system, picks up each product from the inventory, and packages it into a lucky bag.

[0531] Output: Packaged lucky bags are generated and handed over to the delivery company.

[0532] Step 6:

[0533] User Notification

[0534] Subject: Server

[0535] Input: Lucky bag shipping information

[0536] Data transmission and notification behavior: The server notifies the user's contacts (via email or messaging services)

[0537] Output: A notification message that arrives on the user's device (e.g., "Your lucky bag has been shipped. It will arrive in a few days!")

[0538] 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.

[0539] 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.

[0540] 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.

[0541] [Second embodiment]

[0542] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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).

[0548] 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. 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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."

[0554] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[0555] 1. Obtaining user information

[0556] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[0557] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[0558] 2. Enter your budget

[0559] User: The user enters budget information into a budget entry form on the system.

[0560] On your device: Enter your budget information and it will be sent to the server.

[0561] Example: User A enters a budget of 5,000 yen.

[0562] 3. Product selection process

[0563] Server: The server obtains online behavior history and trend information based on the user's attribute information and budget information.

[0564] Server: Based on the acquired information, the generation AI module is called to select the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[0565] Example: For example, a generative AI selects sportswear and the latest gadget accessories for user A.

[0566] 4. Creation and shipping of lucky bags

[0567] Server: Sends the selected product list to the shipping system as a lucky bag list.

[0568] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[0569] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0570] 5. Notice to Users

[0571] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[0572] On your device: You will receive a notification on your device and be able to check the status.

[0573] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0574] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes and interests, and online shops can expect to increase their sales by providing products that meet the needs of each user.

[0575] The processing flow will be explained below.

[0576] Step 1:

[0577] User: Log in to the system.

[0578] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[0579] Step 2:

[0580] Server: Authenticates the login information and obtains the user's attribute information.

[0581] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[0582] Step 3:

[0583] Server: Save the acquired user attribute information as session data.

[0584] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[0585] Step 4:

[0586] User: Enters a budget into a form where budget information is entered.

[0587] Users enter their budget in the input form displayed on the system.

[0588] Step 5:

[0589] Terminal: Sends the entered budget to the server.

[0590] Budget information from the input form is sent to the server.

[0591] Step 6:

[0592] Server: Save the received budget information as session data.

[0593] Add and save the budget information in the session data.

[0594] Step 7:

[0595] Server: Refers to the trend database based on the user's attribute information and budget information to obtain a list of trending products.

[0596] Access the trend database to retrieve the current trending product list and add it to the session data.

[0597] Step 8:

[0598] Server: Calls the generative AI module and passes the user information and trending product list as input.

[0599] User attribute information, budget information, and trending product lists are provided as input data to the generation AI.

[0600] Step 9:

[0601] Generative AI: Generates optimal product candidate lists, evaluates them, and selects the best combination.

[0602] Product scoring is performed taking into account the user's interest tags and online behavior history to generate an optimal product candidate list.

[0603] Step 10:

[0604] Server: Receives the optimal lucky bag candidate list output by the generation AI and saves it as a lucky bag item list.

[0605] The optimal product candidate list is saved as a lucky bag item list in the session data.

[0606] Step 11:

[0607] Server: Sends the lucky bag item list to the shipping system.

[0608] The lucky bag item list is formatted and the data is sent to the shipping system.

[0609] Step 12:

[0610] Shipping system: Picks and packages products based on the lucky bag item list.

[0611] The listed items are taken from the inventory system and packaged as lucky bags.

[0612] Step 13:

[0613] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[0614] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[0615] Step 14:

[0616] Server: Notify the user's contacts of the lucky bag shipping information.

[0617] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[0618] Step 15:

[0619] On your device: You will receive a notification on your device to check the status.

[0620] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[0621] Example 1

[0622] 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."

[0623] The previous system was unable to fully utilize user attributes, budget information, online behavior history, and trend information, making it difficult to select the most suitable product for each individual user. Furthermore, the packaging and shipping processes for selected products were inefficient, and notifications to users were sometimes delayed. This resulted in low user satisfaction and hindered the company's hopes of increasing sales.

[0624] 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.

[0625] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information, means for generating a list of selected products as a lucky bag list and sending it to a shipping system, means for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to a delivery company, and means for notifying the user of lucky bag shipping information. This makes it possible to efficiently select and ship optimal products for users and quickly notify them.

[0626] "User attribute information" is information that indicates individual characteristics of a user, such as gender, age, and interest tags.

[0627] "Budget information" is information indicating the budget amount for purchases entered by the user on the system.

[0628] "Online behavior history" is information that records a user's behavior on the Internet, such as browsing history and purchase history.

[0629] "Trend information" is information about products and services that are currently or recently popular.

[0630] "Generative AI" is an algorithm that uses artificial intelligence technology to select the most suitable product based on a user's attribute information, budget information, online behavior history, and trend information.

[0631] The "Lucky Bag List" is a list of the most suitable products selected by the generation AI.

[0632] The "shipping system" is a system that is responsible for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to the delivery company.

[0633] "Means of notification" refers to the means used to inform users of the shipping information for the lucky bag, and includes email and messaging services.

[0634] A "product candidate list" is a list of candidate products that the generation AI selects based on the user's information and that will ultimately be provided.

[0635] This invention relates to a system that uses generative AI to select the most suitable products for a user based on the user's attribute information, budget information, online behavior history, and trend information, and provides them as a lucky bag. This system consists of the following components:

[0636] 1. Obtaining user information

[0637] The server detects the user's login and retrieves the user's attribute information from an external database. The retrieved attribute information includes gender, age, and interest tags. This information is used as the basis for the generative AI to select appropriate products.

[0638] Example: When a 30-year-old male user A logs into the system, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from an external database and saves them as session information.

[0639] 2. Enter your budget

[0640] The user enters budget information into a budget input form on the system's UI. The entered budget information is sent to the server via the terminal and saved. This budget information is used as one of the product selection criteria.

[0641] Example: User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[0642] 3. Product selection process

[0643] The server retrieves relevant online behavior history and the latest trend information based on the user's attribute information and budget information, which is obtained from external APIs and internal databases.

[0644] Next, the server calls the generation AI module and inputs the user information, budget, and the acquired behavior history and trend information as a prompt sentence, which is as follows:

[0645] text

[0646] User Attributes:

[0647] Gender: Male

[0648] Age: 30

[0649] Interests Tags: Sports, Technology

[0650] Budget: 5,000 yen

[0651] Suggest three products that are ideal for this user. Consider the latest trending products and the user's past purchasing history as criteria for product selection.

[0652] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[0653] Example: Generative AI selects, for example, sportswear and the latest gadget accessories based on user A's interests and budget.

[0654] 4. Creation and shipping of lucky bags

[0655] The server creates a list of selected products as a lucky bag list and sends it to the shipping system. The shipping system picks up products from inventory based on this lucky bag list and packages them. The packaged lucky bags are then handed over to a delivery company.

[0656] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0657] 5. Notice to Users

[0658] The server will notify the user's contacts of the lucky bag delivery information via email or messaging service, and the device will receive the notification and allow the user to check the delivery status of the lucky bag.

[0659] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0660] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0661] Step 1:

[0662] A user logs into the system

[0663] Specific behavior:

[0664] A user logs in to the system.

[0665] The server detects that the user has logged in and obtains the user ID.

[0666] Input: User ID

[0667] Output: User session information

[0668] Data processing / calculation:

[0669] The server generates session information corresponding to the user ID and saves it for use in subsequent processing.

[0670] Step 2:

[0671] Get user attribute information

[0672] Specific behavior:

[0673] The server retrieves user attribute information from an external database, including gender, age, and interest tags.

[0674] The acquired attribute information is added to the session information in the server.

[0675] Input: User ID

[0676] Output: User demographic information (gender, age, interest tags)

[0677] Data processing / calculation:

[0678] The server queries an external database to obtain user attribute information and stores it in session information.

[0679] Examples:

[0680] When a 30-year-old male user A logs in, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from the database and adds them to the session information.

[0681] Step 3:

[0682] Entering budget information

[0683] Specific behavior:

[0684] The user enters budget information into the budget input form on the system UI.

[0685] The terminal transmits the input budget information to the server.

[0686] The server adds the budget information to the session information.

[0687] Input: Budget Information

[0688] Output: Updated session information

[0689] Data processing / calculation:

[0690] The server receives the budget information sent from the terminal and stores it in the session information.

[0691] Examples:

[0692] User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[0693] Step 4:

[0694] Acquisition of online behavior history and trend information

[0695] Specific behavior:

[0696] The server retrieves the user's online behavior history and the latest trend information from external APIs and internal databases.

[0697] Add the acquired information to the session information.

[0698] Input: User demographic information and budget information

[0699] Output: User's online behavior history and trend information

[0700] Data processing / calculation:

[0701] The server calls external APIs to retrieve behavioral history and trend information that matches the user's attribute information, and also retrieves related information from the internal database.

[0702] Examples:

[0703] The server retrieves recent online behavior and trend information related to "sports" and "technology" from the API and stores it internally.

[0704] Step 5:

[0705] Product selection using generative AI models

[0706] Specific behavior:

[0707] The server calls the generation AI module and inputs user information, budget, and acquired behavioral history and trend information as prompt statements.

[0708] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[0709] Input: Prompt text (user demographic information, budget, online behavior history, trend information)

[0710] Output: Product candidate list

[0711] Data processing / calculation:

[0712] The generation AI analyzes the prompt text, scores the best products for the user, generates a list of optimal product candidates, and outputs it to the server.

[0713] Examples:

[0714] The generative AI selects sportswear and the latest gadget accessories based on User A's interests and budget.

[0715] Step 6:

[0716] Creating a lucky bag list and preparing for shipping

[0717] Specific behavior:

[0718] The server generates a lucky bag list from the product candidate list received from the generation AI.

[0719] Send the lucky bag list to the shipping system.

[0720] The shipping system will pick up items from inventory based on the lucky bag list and package them.

[0721] Hand over the packaged lucky bag to the delivery company.

[0722] Input: Product Suggestion List

[0723] Output: Lucky bag list and packaged lucky bags

[0724] Data processing / calculation:

[0725] The server generates a lucky bag list based on the product candidate list and sends it to the shipping system, which checks inventory information, packages the relevant products, and hands them over to the delivery company.

[0726] Examples:

[0727] A lucky bag containing sportswear and gadget accessories is generated and shipped to User A's address.

[0728] Step 7:

[0729] User Notification

[0730] Specific behavior:

[0731] The server notifies the user's contact information about the delivery of the lucky bag.

[0732] The device will display notifications to the user and allow them to check the shipping status.

[0733] Input: Lucky bag shipping information

[0734] Output: A notification message to the user

[0735] Data processing / calculation:

[0736] The server sends notifications to the user's contacts based on information from the shipping system, and the device displays the received notifications to the user.

[0737] Examples:

[0738] User A receives a message on his smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0739] (Application example 1)

[0740] 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."

[0741] In today's online shopping world, proposing optimal products to users and offering them as lucky bags is an important factor in increasing user satisfaction. Conventional systems make it difficult to select optimal products based on the user's attributes and individual budget, and the products actually offered often do not meet the user's expectations. Efficiently notifying users of shipping information is also a challenge. In particular, smartphone applications are rarely used to provide users with real-time information, and this issue is also in need of improvement.

[0742] 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.

[0743] In this invention, the server includes: means for acquiring user attribute information; means for receiving budget information entered by the user; means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information; means for packaging and shipping the selected products as a lucky bag; means for notifying the user of lucky bag shipping information; means for having a smartphone application and displaying it using the generation AI; means for providing the user with shipping information and notifications through the smartphone application; and means for prompting the generation AI based on the above information and trend information. This makes it possible to select optimal products based on the user's individual attribute information and budget and provide them as a lucky bag, and to efficiently notify the user.

[0744] "User attribute information" is information that indicates characteristics such as the user's gender, age, and interests.

[0745] "Budget information" is information that indicates the upper limit of the amount set by the user when making a purchase.

[0746] "Online behavior history" is information that represents the history of operations, selections, browsing, etc. that a user has performed on the Internet.

[0747] "Trend information" refers to information that indicates currently popular products and services and trends in fashion.

[0748] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate items or results that are optimal for a specific purpose.

[0749] A "lucky bag" is a type of product that offers multiple different products in one package.

[0750] "Packaging" is the process of packing selected items and preparing them for shipment.

[0751] A "smartphone application" is software that runs on a smartphone device and is a program that provides specific functions or services.

[0752] A "notification" is a means of conveying information from a system to a user, typically via email or push notification.

[0753] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[0754] 1. Obtaining user information

[0755] When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[0756] As a specific example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are "sports" and "technology."

[0757] 2. Enter your budget

[0758] The user enters budget information into a budget entry form on the system. Once the terminal inputs the budget information, the information is sent to the server.

[0759] For example, a user enters a budget of "5,000 yen."

[0760] 3. Product selection process

[0761] The server acquires online behavior history and trend information based on the user's attribute information and budget information. Based on the acquired information, it calls the generation AI module and selects the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[0762] As a concrete example, generative AI will select sportswear and the latest gadget accessories for users.

[0763] 4. Creation and shipping of lucky bags

[0764] The server sends the selected product list as a lucky bag list to the shipping system. The shipping system picks up each product from inventory based on the lucky bag list and packages it. Once packaged, the lucky bag is handed over to the delivery company.

[0765] As a specific example, a lucky bag containing sportswear and gadget accessories is generated and shipped to the user's address.

[0766] 5. Notice to Users

[0767] The server will notify the user's contacts of the delivery of the lucky bag. Notifications will be sent via email or messaging services. The user will receive a notification on their device and can check the status.

[0768] As a concrete example, a user receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0769] In this invention, the generative AI creates prompts based on the user's attribute information, budget information, online behavior history, and trend information, and then selects the most suitable product based on these.The main hardware and software used include a server, user terminal, generative AI model, database, inventory management system, delivery system, notification service, mail server, etc.

[0770] Example prompt sentence:

[0771] Based on the user's demographic information (gender: male, age: 30, interests: sports, technology), online behavior history, budget: 5,000 yen, and a list of trending products (e.g., "smartwatch," "sneakers," "headphones"), select the most suitable products for the user and generate a lucky bag.

[0772] By implementing the present invention, it is possible to select optimal products based on the user's individual attribute information and budget, and offer them as lucky bags, and to efficiently notify users. This can improve user satisfaction and is expected to increase sales for online shops.

[0773] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0774] Step 1:

[0775] Retrieving User Information

[0776] When a user logs in to the system, the server retrieves the user's attribute information from an external database. The retrieved information includes gender, age, and interest tags. Based on this information, a personalized profile of the user is generated. The input is the user ID, and the output is the user's attribute information.

[0777] Step 2:

[0778] Enter your budget

[0779] The user enters budget information into a form on the system. The terminal retrieves this information and sends it to the server. The input is the budget amount entered by the user, and the output is the budget information sent to the server.

[0780] Step 3:

[0781] Acquisition of online behavior history and trend information

[0782] The server obtains users' online behavioral history and trend information from external sources. The behavioral history includes past purchase history and browsing history, and the trend information includes currently popular products and services. The input is a request for online behavioral history and trend information, and the output is the obtained behavioral history and trend information.

[0783] Step 4:

[0784] Generate prompt statement

[0785] The server creates a prompt for the generative AI model based on the acquired user attribute information, budget information, online behavior history, and trend information. This prompt is used to request a list of optimal products from the generative AI model. The input is the user's composite information, and the output is the prompt.

[0786] Step 5:

[0787] Selection of the optimal product

[0788] The server calls the generative AI model and inputs a prompt to select the most suitable product. The generative AI model considers the user's interests and trends, performs scoring, and selects products. The input is the prompt, and the output is a list of selected products.

[0789] Step 6:

[0790] Creation of lucky bags

[0791] The server generates a list of selected products as a lucky bag list and sends it to the shipping system. At this time, it checks the inventory status of the products and issues instructions to pick up the products from the inventory. The input is the product list, and the output is the lucky bag list.

[0792] Step 7:

[0793] Shipping and Packaging

[0794] The shipping system picks up products from inventory based on the lucky bag list and packages them. Once packaged, the lucky bags are handed over to the delivery company. The input is the lucky bag list, and the output is the packaged lucky bag.

[0795] Step 8:

[0796] User Notification

[0797] The server notifies the user's contacts of the lucky bag shipping information. Notifications are sent via email or push notification. The user receives a notification on their device and can check the status. The input is the shipping information, and the output is a notification message to the user.

[0798] 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.

[0799] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[0800] The system consists of the following components:

[0801] 1. Obtaining user information

[0802] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[0803] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[0804] 2. Enter your budget

[0805] User: The user enters budget information into a budget entry form on the system.

[0806] On your device: Enter your budget information and it will be sent to the server.

[0807] Example: User A enters a budget of 5,000 yen.

[0808] 3. Acquiring emotional information

[0809] Server: Calls the emotion engine that analyzes the user's online behavior and input data to obtain the user's emotional information.

[0810] Server: Save the acquired user emotion information as session data.

[0811] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[0812] 4. Product selection process

[0813] Server: Calls the generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[0814] Generative AI: Analyzes each data item in a comprehensive manner, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[0815] Example: For example, a generative AI might select a combination of sportswear and the latest gadget accessories for user A.

[0816] 5. Creation and shipping of lucky bags

[0817] Server: Sends the selected product list to the shipping system as a lucky bag list.

[0818] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[0819] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0820] 6. Notice to Users

[0821] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[0822] On your device: You will receive a notification on your device and be able to check the status.

[0823] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0824] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[0825] The processing flow will be explained below.

[0826] Step 1:

[0827] User: Log in to the system.

[0828] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[0829] Step 2:

[0830] Server: Authenticates the login information and obtains the user's attribute information.

[0831] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[0832] Step 3:

[0833] Server: Save the acquired user attribute information as session data.

[0834] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[0835] Step 4:

[0836] User: Enters a budget into a form where budget information is entered.

[0837] Users enter their budget in the input form displayed on the system.

[0838] Step 5:

[0839] Terminal: Sends the entered budget to the server.

[0840] Budget information from the input form is sent to the server.

[0841] Step 6:

[0842] Server: Save the received budget information as session data.

[0843] Add and save the budget information in the session data.

[0844] Step 7:

[0845] Server: Calls the emotion engine to analyze users' online behavior and obtain emotion information.

[0846] The emotion engine analyzes and extracts emotional information based on the user's browsing history and input data.

[0847] Step 8:

[0848] Server: Add the acquired emotion information to the session data and save it.

[0849] The emotional information (e.g., excitement, anticipation) output by the emotion engine is stored in the session data.

[0850] Step 9:

[0851] Server: Inputs data into the generative AI module based on the user's attribute information, budget information, sentiment information, and trend information, and selects the most suitable product.

[0852] Generative AI analyzes data and generates product lists suitable for users.

[0853] Step 10:

[0854] Generative AI: It scores products based on user interest tags and emotional information to generate an optimal list of product candidates.

[0855] Save the product candidate list in session data.

[0856] Step 11:

[0857] Server: Save the optimal product list output by the generation AI as a lucky bag item list.

[0858] Convert the product list into a lucky bag item list and save it.

[0859] Step 12:

[0860] Server: Sends the lucky bag item list to the shipping system.

[0861] The lucky bag item list is formatted and the data is sent to the shipping system.

[0862] Step 13:

[0863] Shipping system: Picks and packages products based on the lucky bag item list.

[0864] The listed items are taken from the inventory system and packaged as lucky bags.

[0865] Step 14:

[0866] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[0867] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[0868] Step 15:

[0869] Server: Notify the user's contacts of the lucky bag shipping information.

[0870] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[0871] Step 16:

[0872] On your device: You will receive a notification on your device to check the status.

[0873] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[0874] Example 2

[0875] 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."

[0876] Conventional online shopping systems can select products based on user attributes and budget information, but they have the problem of not being able to select products that take into account user emotions or real-time trend information. As a result, user satisfaction declines and sales growth cannot be expected. Furthermore, there has been no effective system for providing users with the best lucky bags.

[0877] 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.

[0878] In this invention, the server includes a means for acquiring user attribute information, a means for receiving budget information entered by the user, and a means for selecting optimal products in consideration of the user's emotional state based on the user's attribute information, budget information, online behavior history, analysis results by the generation AI, and trend information. This makes it possible to provide optimal lucky bags that reflect the user's emotions and real-time trends.

[0879] "User attribute information" refers to personal characteristics such as the user's gender, age, and tags that indicate their interests.

[0880] "Budget Information" means the amount information entered by a User within the System to be used for purchases.

[0881] "Online behavior history" refers to the history of a user's activities on the Internet, and specifically includes browsing history, purchasing history, etc.

[0882] "Generative AI" refers to artificial intelligence that analyzes input data and selects products that are suitable for the user.

[0883] "Trend information" refers to information that shows current trends in the market and consumer interests.

[0884] "Emotional state" refers to emotions analyzed from a user's online behavior and input data, such as joy, excitement, and anticipation.

[0885] A "lucky bag" is a package containing a selection of multiple products.

[0886] "Packaging" refers to the process of wrapping selected products and assembling them into a single package.

[0887] "Notification" refers to a means of informing users of lucky bag shipping information, and is done via email or messaging services.

[0888] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion analysis engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[0889] The system consists of the following components:

[0890] 1. Obtaining user information

[0891] When a user logs into the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[0892] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[0893] 2. Enter your budget

[0894] The user enters budget information into a budget entry form on the system.

[0895] The terminal sends the information to the server.

[0896] Example: User A enters a budget of 5,000 yen.

[0897] 3. Acquiring emotional information

[0898] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[0899] The emotion information acquired by the server is saved as session data.

[0900] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[0901] 4. Product selection process

[0902] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[0903] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[0904] Example: A generative AI selects a combination of sportswear and the latest gadget accessories for user A.

[0905] Example prompt sentence:

[0906] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[0907] Budget: 5,000 yen

[0908] Emotional information: excitement, anticipation

[0909] Online behavior history: browsing sports-related sites, reading gadget review articles

[0910] Latest trends: new sportswear, hot gadget accessories

[0911] 5. Creation and shipping of lucky bags

[0912] The server transmits the selected product list to the shipping business system as a lucky bag list.

[0913] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[0914] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[0915] 6. Notice to Users

[0916] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[0917] The device will notify the user.

[0918] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[0919] The above is a specific embodiment of the present invention. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[0920] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0921] Step 1:

[0922] Obtaining user attribute information

[0923] A user logs in to the system.

[0924] Specific operation: User A enters his / her username and password and clicks the login button.

[0925] Input: Username and Password

[0926] Output: After successful login, the user ID is obtained.

[0927] The server accesses an external database and obtains the user's attribute information (gender, age, interest tags).

[0928] Specific operation: The server executes an SQL query and saves the retrieved attribute data of User A ("Male", "30 years old", "Sports", "Technology") as session data.

[0929] Input: User ID

[0930] Output: User demographic information (gender, age, interest tags)

[0931] Step 2:

[0932] Enter your budget

[0933] Access a form where users enter budget information.

[0934] Specific behavior: User A opens the budget input form.

[0935] Input: Budget input form URL

[0936] Output: The budget entry page is displayed.

[0937] The user enters budget information and clicks the submit button.

[0938] Specific actions: User A enters "5,000 yen" and presses the send button.

[0939] Input: Budget information (5000 yen)

[0940] Output: The form data is sent to the server.

[0941] The terminal transmits the input budget information to the server.

[0942] What happens: The form sends an HTTP POST request to the server, and the budget information arrives at the server.

[0943] Input: Budget information (5000 yen)

[0944] Output: Budget information is saved on the server.

[0945] Step 3:

[0946] Acquiring emotional information

[0947] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[0948] Specific operation: The server sends an API request to send user A's browsing history and form input data to the sentiment analysis engine.

[0949] Input: Online behavioral data, input data

[0950] Output: Emotional information (e.g., "excitement," "anticipation")

[0951] The emotion information acquired by the server is saved as session data.

[0952] Specific operation: The server stores the emotion information received from the emotion analysis engine in the session database.

[0953] Input: Emotion information

[0954] Output: Emotion information is added to the session data.

[0955] Step 4:

[0956] Product selection process

[0957] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[0958] Specific operation: The server generates a prompt and sends it to the generation AI. Prompt text:

[0959] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[0960] Budget: 5,000 yen

[0961] Emotional information: excitement, anticipation

[0962] Online behavior history: browsing sports-related sites, reading gadget review articles

[0963] Latest trends: new sportswear, hot gadget accessories

[0964] Input: Attribute information, budget information, sentiment information, behavioral history, trend information

[0965] Output: Selected product candidate list

[0966] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[0967] How it works: Generative AI scores the candidate list and selects a combination of sportswear and gadget accessories.

[0968] Input: prompt statement

[0969] Output: Optimal product mix

[0970] Step 5:

[0971] Creation and shipping of lucky bags

[0972] The server transmits the selected product list to the shipping business system as a lucky bag list.

[0973] Specific operation: The server sends an API request to send the product list to the packaging and delivery system.

[0974] Input: Selected product list

[0975] Output: Lucky bag list

[0976] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[0977] Specific operation: The warehouse system generates a picking list, and staff pack the items and hand them over to the delivery company.

[0978] Input: Lucky Bag List

[0979] Output: Packaged lucky bags

[0980] Step 6:

[0981] User Notification

[0982] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[0983] Specific operation: The server sends a message "Your lucky bag has been shipped" using email or a messaging service.

[0984] Input: Lucky bag shipping information

[0985] Output: Information message

[0986] The device will notify the user.

[0987] Specific behavior: A notification will pop up on User A's smartphone, allowing them to check the status.

[0988] Input: Notification message

[0989] Output: Notification display on smartphone

[0990] (Application example 2)

[0991] 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."

[0992] Conventional online shopping systems have mechanisms for suggesting products based on user attribute information and budget information, but they are unable to select products that take into account the user's emotional state, and therefore are unable to sufficiently increase user satisfaction. Furthermore, there is a need to provide a personalized shopping experience by proposing products that incorporate emotional information. Furthermore, offering selected products as lucky bags is also a challenge, as it provides a new shopping experience for users and increases their satisfaction.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0994] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means for analyzing the user's online behavior and input data and acquiring emotional information, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, emotional information, and trend information, means for packaging and shipping the selected products as a lucky bag, and means for notifying the user of lucky bag shipping information. This makes it possible to select optimal products based on the user's attribute information, emotional information, and budget information, providing a personalized purchasing experience and high satisfaction.

[0995] "User attribute information" refers to basic information related to individual users, such as their gender, age, and interest tags.

[0996] "Budget information" is information indicating the upper limit of the amount set by the user for purchases.

[0997] "Online behavior" is a record of all actions a user takes on a website or application, including clicks, views, and purchases.

[0998] "Emotional information" is information that indicates a user's emotional state analyzed from their online behavior and input data.

[0999] "Generative AI" is artificial intelligence that generates optimal products based on user information.

[1000] "Trend information" is information about currently popular products and services.

[1001] A "lucky bag" is a package containing multiple selected products.

[1002] "Packaging" is the process of assembling the selected products into lucky bags.

[1003] "Shipping" is the process of sending the packaged lucky bag to the user.

[1004] "Notification" refers to the act of informing users of the shipping information for lucky bags.

[1005] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. A specific embodiment of this system is shown below.

[1006] When a user logs in to the system, the server first obtains the user's attribute information from an external database. This information includes gender, age, and interest tags. For example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are sports and technology.

[1007] Next, when the user inputs budget information through the terminal, the information is sent to the server. For example, if the user inputs a budget of 5,000 yen, this information is saved on the server.

[1008] The server then calls an emotion engine that analyzes the user's online behavior and input data to obtain the user's emotion information and save it as session data. For example, it may be analyzed to determine whether the user is feeling excitement or anticipation.

[1009] Based on this information, the server calls the AI ​​generator, which then comprehensively analyzes the user's attributes, budget, online behavior, trends, and emotional information to select the most suitable products. For example, the AI ​​generator can select a combination of sportswear and the latest gadget accessories for the user.

[1010] The server sends the selected product list as a lucky bag list to the shipping system, which then picks and packages the items from the inventory. For example, a lucky bag containing sportswear and gadget accessories is created and shipped to the user's address.

[1011] The server then notifies the user's contacts of the delivery of the lucky bag. Notifications are sent via email or messaging services, and the user can check the status on their own device. For example, the user might receive a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1012] This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can expect to increase user satisfaction and sales by providing products that meet the needs and emotions of each user.

[1013] Prompt Sentence Examples

[1014] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[1015] Budget: 5,000 yen

[1016] Emotional information: excitement, anticipation

[1017] Use this information to help you choose the best product for your users."

[1018] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1019] Step 1:

[1020] Get user attribute information

[1021] Subject: Server

[1022] Input: User logged into the system

[1023] Data processing and calculation: The server sends an API request to an external database to obtain the user's gender, age, and interest tags.

[1024] Output: User demographic information (e.g., gender - male, age - 30, interest tags - sports, technology)

[1025] Step 2:

[1026] Entering budget information

[1027] Subject: User

[1028] Input: The user inputs budget information through the device (e.g., 5,000 yen)

[1029] Data transmission: Budget information is sent from the device to the server.

[1030] Output: User's budget information saved on the server (e.g., 5,000 yen)

[1031] Step 3:

[1032] Acquiring emotional information

[1033] Subject: Server

[1034] Input: Your online behavior and input data

[1035] Data processing and calculation: The server calls the emotion analysis engine, analyzes the input data, and extracts the user's emotional information (e.g., excitement, anticipation).

[1036] Output: Obtained user sentiment information

[1037] Step 4:

[1038] Product selection process

[1039] Subject: Server

[1040] Input: User attribute information, budget information, online behavior history, trend information, emotional information

[1041] Data processing and calculation: The server inputs this information into the generation AI, which then selects the most suitable product based on the prompt (e.g., sportswear, the latest gadget accessories).

[1042] Output: List of selected products

[1043] Step 5:

[1044] Creation and shipping of lucky bags

[1045] Subject: Server and shipping system

[1046] Input: Selected product list

[1047] Data transmission and physical operation: The server sends the selected product list to the shipping system, picks up each product from the inventory, and packages it into a lucky bag.

[1048] Output: Packaged lucky bags are generated and handed over to the delivery company.

[1049] Step 6:

[1050] User Notification

[1051] Subject: Server

[1052] Input: Lucky bag shipping information

[1053] Data transmission and notification behavior: The server notifies the user's contacts (via email or messaging services)

[1054] Output: A notification message that arrives on the user's device (e.g., "Your lucky bag has been shipped. It will arrive in a few days!")

[1055] 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.

[1056] 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.

[1057] 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.

[1058] [Third embodiment]

[1059] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1060] 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.

[1061] 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).

[1062] 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.

[1063] 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.

[1064] 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).

[1065] 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. 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.

[1066] 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.

[1067] 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.

[1068] 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.

[1069] 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.

[1070] 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."

[1071] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[1072] 1. Obtaining user information

[1073] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[1074] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[1075] 2. Enter your budget

[1076] User: The user enters budget information into a budget entry form on the system.

[1077] On your device: Enter your budget information and it will be sent to the server.

[1078] Example: User A enters a budget of 5,000 yen.

[1079] 3. Product selection process

[1080] Server: The server obtains online behavior history and trend information based on the user's attribute information and budget information.

[1081] Server: Based on the acquired information, the generation AI module is called to select the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[1082] Example: For example, a generative AI selects sportswear and the latest gadget accessories for user A.

[1083] 4. Creation and shipping of lucky bags

[1084] Server: Sends the selected product list to the shipping system as a lucky bag list.

[1085] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[1086] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1087] 5. Notice to Users

[1088] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[1089] On your device: You will receive a notification on your device and be able to check the status.

[1090] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1091] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes and interests, and online shops can expect to increase their sales by providing products that meet the needs of each user.

[1092] The processing flow will be explained below.

[1093] Step 1:

[1094] User: Log in to the system.

[1095] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[1096] Step 2:

[1097] Server: Authenticates the login information and obtains the user's attribute information.

[1098] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[1099] Step 3:

[1100] Server: Save the acquired user attribute information as session data.

[1101] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[1102] Step 4:

[1103] User: Enters a budget into a form where budget information is entered.

[1104] Users enter their budget in the input form displayed on the system.

[1105] Step 5:

[1106] Terminal: Sends the entered budget to the server.

[1107] Budget information from the input form is sent to the server.

[1108] Step 6:

[1109] Server: Save the received budget information as session data.

[1110] Add and save the budget information in the session data.

[1111] Step 7:

[1112] Server: Refers to the trend database based on the user's attribute information and budget information to obtain a list of trending products.

[1113] Access the trend database to retrieve the current trending product list and add it to the session data.

[1114] Step 8:

[1115] Server: Calls the generative AI module and passes the user information and trending product list as input.

[1116] User attribute information, budget information, and trending product lists are provided as input data to the generation AI.

[1117] Step 9:

[1118] Generative AI: Generates optimal product candidate lists, evaluates them, and selects the best combination.

[1119] Product scoring is performed taking into account the user's interest tags and online behavior history to generate an optimal product candidate list.

[1120] Step 10:

[1121] Server: Receives the optimal lucky bag candidate list output by the generation AI and saves it as a lucky bag item list.

[1122] The optimal product candidate list is saved as a lucky bag item list in the session data.

[1123] Step 11:

[1124] Server: Sends the lucky bag item list to the shipping system.

[1125] The lucky bag item list is formatted and the data is sent to the shipping system.

[1126] Step 12:

[1127] Shipping system: Picks and packages products based on the lucky bag item list.

[1128] The listed items are taken from the inventory system and packaged as lucky bags.

[1129] Step 13:

[1130] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[1131] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[1132] Step 14:

[1133] Server: Notify the user's contacts of the lucky bag shipping information.

[1134] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[1135] Step 15:

[1136] On your device: You will receive a notification on your device to check the status.

[1137] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[1138] Example 1

[1139] 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."

[1140] The previous system was unable to fully utilize user attributes, budget information, online behavior history, and trend information, making it difficult to select the most suitable product for each individual user. Furthermore, the packaging and shipping processes for selected products were inefficient, and notifications to users were sometimes delayed. This resulted in low user satisfaction and hindered the company's hopes of increasing sales.

[1141] 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.

[1142] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information, means for generating a list of selected products as a lucky bag list and sending it to a shipping system, means for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to a delivery company, and means for notifying the user of lucky bag shipping information. This makes it possible to efficiently select and ship optimal products for users and quickly notify them.

[1143] "User attribute information" is information that indicates individual characteristics of a user, such as gender, age, and interest tags.

[1144] "Budget information" is information indicating the budget amount for purchases entered by the user on the system.

[1145] "Online behavior history" is information that records a user's behavior on the Internet, such as browsing history and purchase history.

[1146] "Trend information" is information about products and services that are currently or recently popular.

[1147] "Generative AI" is an algorithm that uses artificial intelligence technology to select the most suitable product based on a user's attribute information, budget information, online behavior history, and trend information.

[1148] The "Lucky Bag List" is a list of the most suitable products selected by the generation AI.

[1149] The "shipping system" is a system that is responsible for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to the delivery company.

[1150] "Means of notification" refers to the means used to inform users of the shipping information for the lucky bag, and includes email and messaging services.

[1151] A "product candidate list" is a list of candidate products that the generation AI selects based on the user's information and that will ultimately be provided.

[1152] This invention relates to a system that uses generative AI to select the most suitable products for a user based on the user's attribute information, budget information, online behavior history, and trend information, and provides them as a lucky bag. This system consists of the following components:

[1153] 1. Obtaining user information

[1154] The server detects the user's login and retrieves the user's attribute information from an external database. The retrieved attribute information includes gender, age, and interest tags. This information is used as the basis for the generative AI to select appropriate products.

[1155] Example: When a 30-year-old male user A logs into the system, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from an external database and saves them as session information.

[1156] 2. Enter your budget

[1157] The user enters budget information into a budget input form on the system's UI. The entered budget information is sent to the server via the terminal and saved. This budget information is used as one of the product selection criteria.

[1158] Example: User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[1159] 3. Product selection process

[1160] The server retrieves relevant online behavior history and the latest trend information based on the user's attribute information and budget information, which is obtained from external APIs and internal databases.

[1161] Next, the server calls the generation AI module and inputs the user information, budget, and the acquired behavior history and trend information as a prompt sentence, which is as follows:

[1162] text

[1163] User Attributes:

[1164] Gender: Male

[1165] Age: 30

[1166] Interests Tags: Sports, Technology

[1167] Budget: 5,000 yen

[1168] Suggest three products that are ideal for this user. Consider the latest trending products and the user's past purchasing history as criteria for product selection.

[1169] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[1170] Example: Generative AI selects, for example, sportswear and the latest gadget accessories based on user A's interests and budget.

[1171] 4. Creation and shipping of lucky bags

[1172] The server creates a list of selected products as a lucky bag list and sends it to the shipping system. The shipping system picks up products from inventory based on this lucky bag list and packages them. The packaged lucky bags are then handed over to a delivery company.

[1173] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1174] 5. Notice to Users

[1175] The server will notify the user's contacts of the lucky bag delivery information via email or messaging service, and the device will receive the notification and allow the user to check the delivery status of the lucky bag.

[1176] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1178] Step 1:

[1179] A user logs into the system

[1180] Specific behavior:

[1181] A user logs in to the system.

[1182] The server detects that the user has logged in and obtains the user ID.

[1183] Input: User ID

[1184] Output: User session information

[1185] Data processing / calculation:

[1186] The server generates session information corresponding to the user ID and saves it for use in subsequent processing.

[1187] Step 2:

[1188] Get user attribute information

[1189] Specific behavior:

[1190] The server retrieves user attribute information from an external database, including gender, age, and interest tags.

[1191] The acquired attribute information is added to the session information in the server.

[1192] Input: User ID

[1193] Output: User demographic information (gender, age, interest tags)

[1194] Data processing / calculation:

[1195] The server queries an external database to obtain user attribute information and stores it in session information.

[1196] Examples:

[1197] When a 30-year-old male user A logs in, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from the database and adds them to the session information.

[1198] Step 3:

[1199] Entering budget information

[1200] Specific behavior:

[1201] The user enters budget information into the budget input form on the system UI.

[1202] The terminal transmits the input budget information to the server.

[1203] The server adds the budget information to the session information.

[1204] Input: Budget Information

[1205] Output: Updated session information

[1206] Data processing / calculation:

[1207] The server receives the budget information sent from the terminal and stores it in the session information.

[1208] Examples:

[1209] User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[1210] Step 4:

[1211] Acquisition of online behavior history and trend information

[1212] Specific behavior:

[1213] The server retrieves the user's online behavior history and the latest trend information from external APIs and internal databases.

[1214] Add the acquired information to the session information.

[1215] Input: User demographic information and budget information

[1216] Output: User's online behavior history and trend information

[1217] Data processing / calculation:

[1218] The server calls external APIs to retrieve behavioral history and trend information that matches the user's attribute information, and also retrieves related information from the internal database.

[1219] Examples:

[1220] The server retrieves recent online behavior and trend information related to "sports" and "technology" from the API and stores it internally.

[1221] Step 5:

[1222] Product selection using generative AI models

[1223] Specific behavior:

[1224] The server calls the generation AI module and inputs user information, budget, and acquired behavioral history and trend information as prompt statements.

[1225] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[1226] Input: Prompt text (user demographic information, budget, online behavior history, trend information)

[1227] Output: Product candidate list

[1228] Data processing / calculation:

[1229] The generation AI analyzes the prompt text, scores the best products for the user, generates a list of optimal product candidates, and outputs it to the server.

[1230] Examples:

[1231] The generative AI selects sportswear and the latest gadget accessories based on User A's interests and budget.

[1232] Step 6:

[1233] Creating a lucky bag list and preparing for shipping

[1234] Specific behavior:

[1235] The server generates a lucky bag list from the product candidate list received from the generation AI.

[1236] Send the lucky bag list to the shipping system.

[1237] The shipping system will pick up items from inventory based on the lucky bag list and package them.

[1238] Hand over the packaged lucky bag to the delivery company.

[1239] Input: Product Suggestion List

[1240] Output: Lucky bag list and packaged lucky bags

[1241] Data processing / calculation:

[1242] The server generates a lucky bag list based on the product candidate list and sends it to the shipping system, which checks inventory information, packages the relevant products, and hands them over to the delivery company.

[1243] Examples:

[1244] A lucky bag containing sportswear and gadget accessories is generated and shipped to User A's address.

[1245] Step 7:

[1246] User Notification

[1247] Specific behavior:

[1248] The server notifies the user's contact information about the delivery of the lucky bag.

[1249] The device will display notifications to the user and allow them to check the shipping status.

[1250] Input: Lucky bag shipping information

[1251] Output: A notification message to the user

[1252] Data processing / calculation:

[1253] The server sends notifications to the user's contacts based on information from the shipping system, and the device displays the received notifications to the user.

[1254] Examples:

[1255] User A receives a message on his smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1256] (Application example 1)

[1257] 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."

[1258] In today's online shopping world, proposing optimal products to users and offering them as lucky bags is an important factor in increasing user satisfaction. Conventional systems make it difficult to select optimal products based on the user's attributes and individual budget, and the products actually offered often do not meet the user's expectations. Efficiently notifying users of shipping information is also a challenge. In particular, smartphone applications are rarely used to provide users with real-time information, and this issue is also in need of improvement.

[1259] 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.

[1260] In this invention, the server includes: means for acquiring user attribute information; means for receiving budget information entered by the user; means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information; means for packaging and shipping the selected products as a lucky bag; means for notifying the user of lucky bag shipping information; means for having a smartphone application and displaying it using the generation AI; means for providing the user with shipping information and notifications through the smartphone application; and means for prompting the generation AI based on the above information and trend information. This makes it possible to select optimal products based on the user's individual attribute information and budget and provide them as a lucky bag, and to efficiently notify the user.

[1261] "User attribute information" is information that indicates characteristics such as the user's gender, age, and interests.

[1262] "Budget information" is information that indicates the upper limit of the amount set by the user when making a purchase.

[1263] "Online behavior history" is information that represents the history of operations, selections, browsing, etc. that a user has performed on the Internet.

[1264] "Trend information" refers to information that indicates currently popular products and services and trends in fashion.

[1265] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate items or results that are optimal for a specific purpose.

[1266] A "lucky bag" is a type of product that offers multiple different products in one package.

[1267] "Packaging" is the process of packing selected items and preparing them for shipment.

[1268] A "smartphone application" is software that runs on a smartphone device and is a program that provides specific functions or services.

[1269] A "notification" is a means of conveying information from a system to a user, typically via email or push notification.

[1270] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[1271] 1. Obtaining user information

[1272] When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[1273] As a specific example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are "sports" and "technology."

[1274] 2. Enter your budget

[1275] The user enters budget information into a budget entry form on the system. Once the terminal inputs the budget information, the information is sent to the server.

[1276] For example, a user enters a budget of "5,000 yen."

[1277] 3. Product selection process

[1278] The server acquires online behavior history and trend information based on the user's attribute information and budget information. Based on the acquired information, it calls the generation AI module and selects the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[1279] As a concrete example, generative AI will select sportswear and the latest gadget accessories for users.

[1280] 4. Creation and shipping of lucky bags

[1281] The server sends the selected product list as a lucky bag list to the shipping system. The shipping system picks up each product from inventory based on the lucky bag list and packages it. Once packaged, the lucky bag is handed over to the delivery company.

[1282] As a specific example, a lucky bag containing sportswear and gadget accessories is generated and shipped to the user's address.

[1283] 5. Notice to Users

[1284] The server will notify the user's contacts of the delivery of the lucky bag. Notifications will be sent via email or messaging services. The user will receive a notification on their device and can check the status.

[1285] As a concrete example, a user receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1286] In this invention, the generative AI creates prompts based on the user's attribute information, budget information, online behavior history, and trend information, and then selects the most suitable product based on these.The main hardware and software used include a server, user terminal, generative AI model, database, inventory management system, delivery system, notification service, mail server, etc.

[1287] Example prompt sentence:

[1288] Based on the user's demographic information (gender: male, age: 30, interests: sports, technology), online behavior history, budget: 5,000 yen, and a list of trending products (e.g., "smartwatch," "sneakers," "headphones"), select the most suitable products for the user and generate a lucky bag.

[1289] By implementing the present invention, it is possible to select optimal products based on the user's individual attribute information and budget, and offer them as lucky bags, and to efficiently notify users. This can improve user satisfaction and is expected to increase sales for online shops.

[1290] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1291] Step 1:

[1292] Retrieving User Information

[1293] When a user logs in to the system, the server retrieves the user's attribute information from an external database. The retrieved information includes gender, age, and interest tags. Based on this information, a personalized profile of the user is generated. The input is the user ID, and the output is the user's attribute information.

[1294] Step 2:

[1295] Enter your budget

[1296] The user enters budget information into a form on the system. The terminal retrieves this information and sends it to the server. The input is the budget amount entered by the user, and the output is the budget information sent to the server.

[1297] Step 3:

[1298] Acquisition of online behavior history and trend information

[1299] The server obtains users' online behavioral history and trend information from external sources. The behavioral history includes past purchase history and browsing history, and the trend information includes currently popular products and services. The input is a request for online behavioral history and trend information, and the output is the obtained behavioral history and trend information.

[1300] Step 4:

[1301] Generate prompt statement

[1302] The server creates a prompt for the generative AI model based on the acquired user attribute information, budget information, online behavior history, and trend information. This prompt is used to request a list of optimal products from the generative AI model. The input is the user's composite information, and the output is the prompt.

[1303] Step 5:

[1304] Selection of the optimal product

[1305] The server calls the generative AI model and inputs a prompt to select the most suitable product. The generative AI model considers the user's interests and trends, performs scoring, and selects products. The input is the prompt, and the output is a list of selected products.

[1306] Step 6:

[1307] Creation of lucky bags

[1308] The server generates a list of selected products as a lucky bag list and sends it to the shipping system. At this time, it checks the inventory status of the products and issues instructions to pick up the products from the inventory. The input is the product list, and the output is the lucky bag list.

[1309] Step 7:

[1310] Shipping and Packaging

[1311] The shipping system picks up products from inventory based on the lucky bag list and packages them. Once packaged, the lucky bags are handed over to the delivery company. The input is the lucky bag list, and the output is the packaged lucky bag.

[1312] Step 8:

[1313] User Notification

[1314] The server notifies the user's contacts of the lucky bag shipping information. Notifications are sent via email or push notification. The user receives a notification on their device and can check the status. The input is the shipping information, and the output is a notification message to the user.

[1315] 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.

[1316] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[1317] The system consists of the following components:

[1318] 1. Obtaining user information

[1319] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[1320] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[1321] 2. Enter your budget

[1322] User: The user enters budget information into a budget entry form on the system.

[1323] On your device: Enter your budget information and it will be sent to the server.

[1324] Example: User A enters a budget of 5,000 yen.

[1325] 3. Acquiring emotional information

[1326] Server: Calls the emotion engine that analyzes the user's online behavior and input data to obtain the user's emotional information.

[1327] Server: Save the acquired user emotion information as session data.

[1328] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[1329] 4. Product selection process

[1330] Server: Calls the generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[1331] Generative AI: Analyzes each data item in a comprehensive manner, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[1332] Example: For example, a generative AI might select a combination of sportswear and the latest gadget accessories for user A.

[1333] 5. Creation and shipping of lucky bags

[1334] Server: Sends the selected product list to the shipping system as a lucky bag list.

[1335] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[1336] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1337] 6. Notice to Users

[1338] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[1339] On your device: You will receive a notification on your device and be able to check the status.

[1340] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1341] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[1342] The processing flow will be explained below.

[1343] Step 1:

[1344] User: Log in to the system.

[1345] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[1346] Step 2:

[1347] Server: Authenticates the login information and obtains the user's attribute information.

[1348] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[1349] Step 3:

[1350] Server: Save the acquired user attribute information as session data.

[1351] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[1352] Step 4:

[1353] User: Enters a budget into a form where budget information is entered.

[1354] Users enter their budget in the input form displayed on the system.

[1355] Step 5:

[1356] Terminal: Sends the entered budget to the server.

[1357] Budget information from the input form is sent to the server.

[1358] Step 6:

[1359] Server: Save the received budget information as session data.

[1360] Add and save the budget information in the session data.

[1361] Step 7:

[1362] Server: Calls the emotion engine to analyze users' online behavior and obtain emotion information.

[1363] The emotion engine analyzes and extracts emotional information based on the user's browsing history and input data.

[1364] Step 8:

[1365] Server: Add the acquired emotion information to the session data and save it.

[1366] The emotional information (e.g., excitement, anticipation) output by the emotion engine is stored in the session data.

[1367] Step 9:

[1368] Server: Inputs data into the generative AI module based on the user's attribute information, budget information, sentiment information, and trend information, and selects the most suitable product.

[1369] Generative AI analyzes data and generates product lists suitable for users.

[1370] Step 10:

[1371] Generative AI: It scores products based on user interest tags and emotional information to generate an optimal list of product candidates.

[1372] Save the product candidate list in session data.

[1373] Step 11:

[1374] Server: Save the optimal product list output by the generation AI as a lucky bag item list.

[1375] Convert the product list into a lucky bag item list and save it.

[1376] Step 12:

[1377] Server: Sends the lucky bag item list to the shipping system.

[1378] The lucky bag item list is formatted and the data is sent to the shipping system.

[1379] Step 13:

[1380] Shipping system: Picks and packages products based on the lucky bag item list.

[1381] The listed items are taken from the inventory system and packaged as lucky bags.

[1382] Step 14:

[1383] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[1384] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[1385] Step 15:

[1386] Server: Notify the user's contacts of the lucky bag shipping information.

[1387] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[1388] Step 16:

[1389] On your device: You will receive a notification on your device to check the status.

[1390] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[1391] Example 2

[1392] 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."

[1393] Conventional online shopping systems can select products based on user attributes and budget information, but they have the problem of not being able to select products that take into account user emotions or real-time trend information. As a result, user satisfaction declines and sales growth cannot be expected. Furthermore, there has been no effective system for providing users with the best lucky bags.

[1394] 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.

[1395] In this invention, the server includes a means for acquiring user attribute information, a means for receiving budget information entered by the user, and a means for selecting optimal products in consideration of the user's emotional state based on the user's attribute information, budget information, online behavior history, analysis results by the generation AI, and trend information. This makes it possible to provide optimal lucky bags that reflect the user's emotions and real-time trends.

[1396] "User attribute information" refers to personal characteristics such as the user's gender, age, and tags that indicate their interests.

[1397] "Budget Information" means the amount information entered by a User within the System to be used for purchases.

[1398] "Online behavior history" refers to the history of a user's activities on the Internet, and specifically includes browsing history, purchasing history, etc.

[1399] "Generative AI" refers to artificial intelligence that analyzes input data and selects products that are suitable for the user.

[1400] "Trend information" refers to information that shows current trends in the market and consumer interests.

[1401] "Emotional state" refers to emotions analyzed from a user's online behavior and input data, such as joy, excitement, and anticipation.

[1402] A "lucky bag" is a package containing a selection of multiple products.

[1403] "Packaging" refers to the process of wrapping selected products and assembling them into a single package.

[1404] "Notification" refers to a means of informing users of lucky bag shipping information, and is done via email or messaging services.

[1405] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion analysis engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[1406] The system consists of the following components:

[1407] 1. Obtaining user information

[1408] When a user logs into the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[1409] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[1410] 2. Enter your budget

[1411] The user enters budget information into a budget entry form on the system.

[1412] The terminal sends the information to the server.

[1413] Example: User A enters a budget of 5,000 yen.

[1414] 3. Acquiring emotional information

[1415] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[1416] The emotion information acquired by the server is saved as session data.

[1417] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[1418] 4. Product selection process

[1419] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[1420] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[1421] Example: A generative AI selects a combination of sportswear and the latest gadget accessories for user A.

[1422] Example prompt sentence:

[1423] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[1424] Budget: 5,000 yen

[1425] Emotional information: excitement, anticipation

[1426] Online behavior history: browsing sports-related sites, reading gadget review articles

[1427] Latest trends: new sportswear, hot gadget accessories

[1428] 5. Creation and shipping of lucky bags

[1429] The server transmits the selected product list to the shipping business system as a lucky bag list.

[1430] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[1431] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1432] 6. Notice to Users

[1433] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[1434] The device will notify the user.

[1435] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1436] The above is a specific embodiment of the present invention. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[1437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1438] Step 1:

[1439] Obtaining user attribute information

[1440] A user logs in to the system.

[1441] Specific operation: User A enters his / her username and password and clicks the login button.

[1442] Input: Username and Password

[1443] Output: After successful login, the user ID is obtained.

[1444] The server accesses an external database and obtains the user's attribute information (gender, age, interest tags).

[1445] Specific operation: The server executes an SQL query and saves the retrieved attribute data of User A ("Male", "30 years old", "Sports", "Technology") as session data.

[1446] Input: User ID

[1447] Output: User demographic information (gender, age, interest tags)

[1448] Step 2:

[1449] Enter your budget

[1450] Access a form where users enter budget information.

[1451] Specific behavior: User A opens the budget input form.

[1452] Input: Budget input form URL

[1453] Output: The budget entry page is displayed.

[1454] The user enters budget information and clicks the submit button.

[1455] Specific actions: User A enters "5,000 yen" and presses the send button.

[1456] Input: Budget information (5000 yen)

[1457] Output: The form data is sent to the server.

[1458] The terminal transmits the input budget information to the server.

[1459] What happens: The form sends an HTTP POST request to the server, and the budget information arrives at the server.

[1460] Input: Budget information (5000 yen)

[1461] Output: Budget information is saved on the server.

[1462] Step 3:

[1463] Acquiring emotional information

[1464] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[1465] Specific operation: The server sends an API request to send user A's browsing history and form input data to the sentiment analysis engine.

[1466] Input: Online behavioral data, input data

[1467] Output: Emotional information (e.g., "excitement," "anticipation")

[1468] The emotion information acquired by the server is saved as session data.

[1469] Specific operation: The server stores the emotion information received from the emotion analysis engine in the session database.

[1470] Input: Emotion information

[1471] Output: Emotion information is added to the session data.

[1472] Step 4:

[1473] Product selection process

[1474] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[1475] Specific operation: The server generates a prompt and sends it to the generation AI. Prompt text:

[1476] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[1477] Budget: 5,000 yen

[1478] Emotional information: excitement, anticipation

[1479] Online behavior history: browsing sports-related sites, reading gadget review articles

[1480] Latest trends: new sportswear, hot gadget accessories

[1481] Input: Attribute information, budget information, sentiment information, behavioral history, trend information

[1482] Output: Selected product candidate list

[1483] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[1484] How it works: Generative AI scores the candidate list and selects a combination of sportswear and gadget accessories.

[1485] Input: prompt statement

[1486] Output: Optimal product mix

[1487] Step 5:

[1488] Creation and shipping of lucky bags

[1489] The server transmits the selected product list to the shipping business system as a lucky bag list.

[1490] Specific operation: The server sends an API request to send the product list to the packaging and delivery system.

[1491] Input: Selected product list

[1492] Output: Lucky bag list

[1493] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[1494] Specific operation: The warehouse system generates a picking list, and staff pack the items and hand them over to the delivery company.

[1495] Input: Lucky Bag List

[1496] Output: Packaged lucky bags

[1497] Step 6:

[1498] User Notification

[1499] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[1500] Specific operation: The server sends a message "Your lucky bag has been shipped" using email or a messaging service.

[1501] Input: Lucky bag shipping information

[1502] Output: Information message

[1503] The device will notify the user.

[1504] Specific behavior: A notification will pop up on User A's smartphone, allowing them to check the status.

[1505] Input: Notification message

[1506] Output: Notification display on smartphone

[1507] (Application example 2)

[1508] 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."

[1509] Conventional online shopping systems have mechanisms for suggesting products based on user attribute information and budget information, but they are unable to select products that take into account the user's emotional state, and therefore are unable to sufficiently increase user satisfaction. Furthermore, there is a need to provide a personalized shopping experience by proposing products that incorporate emotional information. Furthermore, offering selected products as lucky bags is also a challenge, as it provides a new shopping experience for users and increases their satisfaction.

[1510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1511] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means for analyzing the user's online behavior and input data and acquiring emotional information, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, emotional information, and trend information, means for packaging and shipping the selected products as a lucky bag, and means for notifying the user of lucky bag shipping information. This makes it possible to select optimal products based on the user's attribute information, emotional information, and budget information, providing a personalized purchasing experience and high satisfaction.

[1512] "User attribute information" refers to basic information related to individual users, such as their gender, age, and interest tags.

[1513] "Budget information" is information indicating the upper limit of the amount set by the user for purchases.

[1514] "Online behavior" is a record of all actions a user takes on a website or application, including clicks, views, and purchases.

[1515] "Emotional information" is information that indicates a user's emotional state analyzed from their online behavior and input data.

[1516] "Generative AI" is artificial intelligence that generates optimal products based on user information.

[1517] "Trend information" is information about currently popular products and services.

[1518] A "lucky bag" is a package containing multiple selected products.

[1519] "Packaging" is the process of assembling the selected products into lucky bags.

[1520] "Shipping" is the process of sending the packaged lucky bag to the user.

[1521] "Notification" refers to the act of informing users of the shipping information for lucky bags.

[1522] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. A specific embodiment of this system is shown below.

[1523] When a user logs in to the system, the server first obtains the user's attribute information from an external database. This information includes gender, age, and interest tags. For example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are sports and technology.

[1524] Next, when the user inputs budget information through the terminal, the information is sent to the server. For example, if the user inputs a budget of 5,000 yen, this information is saved on the server.

[1525] The server then calls an emotion engine that analyzes the user's online behavior and input data to obtain the user's emotion information and save it as session data. For example, it may be analyzed to determine whether the user is feeling excitement or anticipation.

[1526] Based on this information, the server calls the AI ​​generator, which then comprehensively analyzes the user's attributes, budget, online behavior, trends, and emotional information to select the most suitable products. For example, the AI ​​generator can select a combination of sportswear and the latest gadget accessories for the user.

[1527] The server sends the selected product list as a lucky bag list to the shipping system, which then picks and packages the items from the inventory. For example, a lucky bag containing sportswear and gadget accessories is created and shipped to the user's address.

[1528] The server then notifies the user's contacts of the delivery of the lucky bag. Notifications are sent via email or messaging services, and the user can check the status on their own device. For example, the user might receive a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1529] This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can expect to increase user satisfaction and sales by providing products that meet the needs and emotions of each user.

[1530] Prompt Sentence Examples

[1531] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[1532] Budget: 5,000 yen

[1533] Emotional information: excitement, anticipation

[1534] Use this information to help you choose the best product for your users."

[1535] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1536] Step 1:

[1537] Get user attribute information

[1538] Subject: Server

[1539] Input: User logged into the system

[1540] Data processing and calculation: The server sends an API request to an external database to obtain the user's gender, age, and interest tags.

[1541] Output: User demographic information (e.g., gender - male, age - 30, interest tags - sports, technology)

[1542] Step 2:

[1543] Entering budget information

[1544] Subject: User

[1545] Input: The user inputs budget information through the device (e.g., 5,000 yen)

[1546] Data transmission: Budget information is sent from the device to the server.

[1547] Output: User's budget information saved on the server (e.g., 5,000 yen)

[1548] Step 3:

[1549] Acquiring emotional information

[1550] Subject: Server

[1551] Input: Your online behavior and input data

[1552] Data processing and calculation: The server calls the emotion analysis engine, analyzes the input data, and extracts the user's emotional information (e.g., excitement, anticipation).

[1553] Output: Obtained user sentiment information

[1554] Step 4:

[1555] Product selection process

[1556] Subject: Server

[1557] Input: User attribute information, budget information, online behavior history, trend information, emotional information

[1558] Data processing and calculation: The server inputs this information into the generation AI, which then selects the most suitable product based on the prompt (e.g., sportswear, the latest gadget accessories).

[1559] Output: List of selected products

[1560] Step 5:

[1561] Creation and shipping of lucky bags

[1562] Subject: Server and shipping system

[1563] Input: Selected product list

[1564] Data transmission and physical operation: The server sends the selected product list to the shipping system, picks up each product from the inventory, and packages it into a lucky bag.

[1565] Output: Packaged lucky bags are generated and handed over to the delivery company.

[1566] Step 6:

[1567] User Notification

[1568] Subject: Server

[1569] Input: Lucky bag shipping information

[1570] Data transmission and notification behavior: The server notifies the user's contacts (via email or messaging services)

[1571] Output: A notification message that arrives on the user's device (e.g., "Your lucky bag has been shipped. It will arrive in a few days!")

[1572] 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.

[1573] 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.

[1574] 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.

[1575] [Fourth embodiment]

[1576] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1577] 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.

[1578] 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).

[1579] 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.

[1580] 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.

[1581] 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).

[1582] 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. 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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."

[1589] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[1590] 1. Obtaining user information

[1591] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[1592] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[1593] 2. Enter your budget

[1594] User: The user enters budget information into a budget entry form on the system.

[1595] On your device: Enter your budget information and it will be sent to the server.

[1596] Example: User A enters a budget of 5,000 yen.

[1597] 3. Product selection process

[1598] Server: The server obtains online behavior history and trend information based on the user's attribute information and budget information.

[1599] Server: Based on the acquired information, the generation AI module is called to select the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[1600] Example: For example, a generative AI selects sportswear and the latest gadget accessories for user A.

[1601] 4. Creation and shipping of lucky bags

[1602] Server: Sends the selected product list to the shipping system as a lucky bag list.

[1603] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[1604] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1605] 5. Notice to Users

[1606] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[1607] On your device: You will receive a notification on your device and be able to check the status.

[1608] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1609] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes and interests, and online shops can expect to increase their sales by providing products that meet the needs of each user.

[1610] The processing flow will be explained below.

[1611] Step 1:

[1612] User: Log in to the system.

[1613] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[1614] Step 2:

[1615] Server: Authenticates the login information and obtains the user's attribute information.

[1616] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[1617] Step 3:

[1618] Server: Save the acquired user attribute information as session data.

[1619] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[1620] Step 4:

[1621] User: Enters a budget into a form where budget information is entered.

[1622] Users enter their budget in the input form displayed on the system.

[1623] Step 5:

[1624] Terminal: Sends the entered budget to the server.

[1625] Budget information from the input form is sent to the server.

[1626] Step 6:

[1627] Server: Save the received budget information as session data.

[1628] Add and save the budget information in the session data.

[1629] Step 7:

[1630] Server: Refers to the trend database based on the user's attribute information and budget information to obtain a list of trending products.

[1631] Access the trend database to retrieve the current trending product list and add it to the session data.

[1632] Step 8:

[1633] Server: Calls the generative AI module and passes the user information and trending product list as input.

[1634] User attribute information, budget information, and trending product lists are provided as input data to the generation AI.

[1635] Step 9:

[1636] Generative AI: Generates optimal product candidate lists, evaluates them, and selects the best combination.

[1637] Product scoring is performed taking into account the user's interest tags and online behavior history to generate an optimal product candidate list.

[1638] Step 10:

[1639] Server: Receives the optimal lucky bag candidate list output by the generation AI and saves it as a lucky bag item list.

[1640] The optimal product candidate list is saved as a lucky bag item list in the session data.

[1641] Step 11:

[1642] Server: Sends the lucky bag item list to the shipping system.

[1643] The lucky bag item list is formatted and the data is sent to the shipping system.

[1644] Step 12:

[1645] Shipping system: Picks and packages products based on the lucky bag item list.

[1646] The listed items are taken from the inventory system and packaged as lucky bags.

[1647] Step 13:

[1648] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[1649] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[1650] Step 14:

[1651] Server: Notify the user's contacts of the lucky bag shipping information.

[1652] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[1653] Step 15:

[1654] On your device: You will receive a notification on your device to check the status.

[1655] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[1656] Example 1

[1657] 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."

[1658] The previous system was unable to fully utilize user attributes, budget information, online behavior history, and trend information, making it difficult to select the most suitable product for each individual user. Furthermore, the packaging and shipping processes for selected products were inefficient, and notifications to users were sometimes delayed. This resulted in low user satisfaction and hindered the company's hopes of increasing sales.

[1659] 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.

[1660] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information, means for generating a list of selected products as a lucky bag list and sending it to a shipping system, means for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to a delivery company, and means for notifying the user of lucky bag shipping information. This makes it possible to efficiently select and ship optimal products for users and quickly notify them.

[1661] "User attribute information" is information that indicates individual characteristics of a user, such as gender, age, and interest tags.

[1662] "Budget information" is information indicating the budget amount for purchases entered by the user on the system.

[1663] "Online behavior history" is information that records a user's behavior on the Internet, such as browsing history and purchase history.

[1664] "Trend information" is information about products and services that are currently or recently popular.

[1665] "Generative AI" is an algorithm that uses artificial intelligence technology to select the most suitable product based on a user's attribute information, budget information, online behavior history, and trend information.

[1666] The "Lucky Bag List" is a list of the most suitable products selected by the generation AI.

[1667] The "shipping system" is a system that is responsible for picking up products from inventory based on the lucky bag list, packaging them, and handing them over to the delivery company.

[1668] "Means of notification" refers to the means used to inform users of the shipping information for the lucky bag, and includes email and messaging services.

[1669] A "product candidate list" is a list of candidate products that the generation AI selects based on the user's information and that will ultimately be provided.

[1670] This invention relates to a system that uses generative AI to select the most suitable products for a user based on the user's attribute information, budget information, online behavior history, and trend information, and provides them as a lucky bag. This system consists of the following components:

[1671] 1. Obtaining user information

[1672] The server detects the user's login and retrieves the user's attribute information from an external database. The retrieved attribute information includes gender, age, and interest tags. This information is used as the basis for the generative AI to select appropriate products.

[1673] Example: When a 30-year-old male user A logs into the system, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from an external database and saves them as session information.

[1674] 2. Enter your budget

[1675] The user enters budget information into a budget input form on the system's UI. The entered budget information is sent to the server via the terminal and saved. This budget information is used as one of the product selection criteria.

[1676] Example: User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[1677] 3. Product selection process

[1678] The server retrieves relevant online behavior history and the latest trend information based on the user's attribute information and budget information, which is obtained from external APIs and internal databases.

[1679] Next, the server calls the generation AI module and inputs the user information, budget, and the acquired behavior history and trend information as a prompt sentence, which is as follows:

[1680] text

[1681] User Attributes:

[1682] Gender: Male

[1683] Age: 30

[1684] Interests Tags: Sports, Technology

[1685] Budget: 5,000 yen

[1686] Suggest three products that are ideal for this user. Consider the latest trending products and the user's past purchasing history as criteria for product selection.

[1687] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[1688] Example: Generative AI selects, for example, sportswear and the latest gadget accessories based on user A's interests and budget.

[1689] 4. Creation and shipping of lucky bags

[1690] The server creates a list of selected products as a lucky bag list and sends it to the shipping system. The shipping system picks up products from inventory based on this lucky bag list and packages them. The packaged lucky bags are then handed over to a delivery company.

[1691] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1692] 5. Notice to Users

[1693] The server will notify the user's contacts of the lucky bag delivery information via email or messaging service, and the device will receive the notification and allow the user to check the delivery status of the lucky bag.

[1694] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1695] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1696] Step 1:

[1697] A user logs into the system

[1698] Specific behavior:

[1699] A user logs in to the system.

[1700] The server detects that the user has logged in and obtains the user ID.

[1701] Input: User ID

[1702] Output: User session information

[1703] Data processing / calculation:

[1704] The server generates session information corresponding to the user ID and saves it for use in subsequent processing.

[1705] Step 2:

[1706] Get user attribute information

[1707] Specific behavior:

[1708] The server retrieves user attribute information from an external database, including gender, age, and interest tags.

[1709] The acquired attribute information is added to the session information in the server.

[1710] Input: User ID

[1711] Output: User demographic information (gender, age, interest tags)

[1712] Data processing / calculation:

[1713] The server queries an external database to obtain user attribute information and stores it in session information.

[1714] Examples:

[1715] When a 30-year-old male user A logs in, the server retrieves his gender as "male," his age as "30," and his interest tags as "sports" and "technology" from the database and adds them to the session information.

[1716] Step 3:

[1717] Entering budget information

[1718] Specific behavior:

[1719] The user enters budget information into the budget input form on the system UI.

[1720] The terminal transmits the input budget information to the server.

[1721] The server adds the budget information to the session information.

[1722] Input: Budget Information

[1723] Output: Updated session information

[1724] Data processing / calculation:

[1725] The server receives the budget information sent from the terminal and stores it in the session information.

[1726] Examples:

[1727] User A enters a budget of "5,000 yen" and the information is sent from the device to the server.

[1728] Step 4:

[1729] Acquisition of online behavior history and trend information

[1730] Specific behavior:

[1731] The server retrieves the user's online behavior history and the latest trend information from external APIs and internal databases.

[1732] Add the acquired information to the session information.

[1733] Input: User demographic information and budget information

[1734] Output: User's online behavior history and trend information

[1735] Data processing / calculation:

[1736] The server calls external APIs to retrieve behavioral history and trend information that matches the user's attribute information, and also retrieves related information from the internal database.

[1737] Examples:

[1738] The server retrieves recent online behavior and trend information related to "sports" and "technology" from the API and stores it internally.

[1739] Step 5:

[1740] Product selection using generative AI models

[1741] Specific behavior:

[1742] The server calls the generation AI module and inputs user information, budget, and acquired behavioral history and trend information as prompt statements.

[1743] The generative AI module scores the most suitable products based on the input information and generates a list of candidate products.

[1744] Input: Prompt text (user demographic information, budget, online behavior history, trend information)

[1745] Output: Product candidate list

[1746] Data processing / calculation:

[1747] The generation AI analyzes the prompt text, scores the best products for the user, generates a list of optimal product candidates, and outputs it to the server.

[1748] Examples:

[1749] The generative AI selects sportswear and the latest gadget accessories based on User A's interests and budget.

[1750] Step 6:

[1751] Creating a lucky bag list and preparing for shipping

[1752] Specific behavior:

[1753] The server generates a lucky bag list from the product candidate list received from the generation AI.

[1754] Send the lucky bag list to the shipping system.

[1755] The shipping system will pick up items from inventory based on the lucky bag list and package them.

[1756] Hand over the packaged lucky bag to the delivery company.

[1757] Input: Product Suggestion List

[1758] Output: Lucky bag list and packaged lucky bags

[1759] Data processing / calculation:

[1760] The server generates a lucky bag list based on the product candidate list and sends it to the shipping system, which checks inventory information, packages the relevant products, and hands them over to the delivery company.

[1761] Examples:

[1762] A lucky bag containing sportswear and gadget accessories is generated and shipped to User A's address.

[1763] Step 7:

[1764] User Notification

[1765] Specific behavior:

[1766] The server notifies the user's contact information about the delivery of the lucky bag.

[1767] The device will display notifications to the user and allow them to check the shipping status.

[1768] Input: Lucky bag shipping information

[1769] Output: A notification message to the user

[1770] Data processing / calculation:

[1771] The server sends notifications to the user's contacts based on information from the shipping system, and the device displays the received notifications to the user.

[1772] Examples:

[1773] User A receives a message on his smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1774] (Application example 1)

[1775] 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."

[1776] In today's online shopping world, proposing optimal products to users and offering them as lucky bags is an important factor in increasing user satisfaction. Conventional systems make it difficult to select optimal products based on the user's attributes and individual budget, and the products actually offered often do not meet the user's expectations. Efficiently notifying users of shipping information is also a challenge. In particular, smartphone applications are rarely used to provide users with real-time information, and this issue is also in need of improvement.

[1777] 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.

[1778] In this invention, the server includes: means for acquiring user attribute information; means for receiving budget information entered by the user; means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, and trend information; means for packaging and shipping the selected products as a lucky bag; means for notifying the user of lucky bag shipping information; means for having a smartphone application and displaying it using the generation AI; means for providing the user with shipping information and notifications through the smartphone application; and means for prompting the generation AI based on the above information and trend information. This makes it possible to select optimal products based on the user's individual attribute information and budget and provide them as a lucky bag, and to efficiently notify the user.

[1779] "User attribute information" is information that indicates characteristics such as the user's gender, age, and interests.

[1780] "Budget information" is information that indicates the upper limit of the amount set by the user when making a purchase.

[1781] "Online behavior history" is information that represents the history of operations, selections, browsing, etc. that a user has performed on the Internet.

[1782] "Trend information" refers to information that indicates currently popular products and services and trends in fashion.

[1783] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate items or results that are optimal for a specific purpose.

[1784] A "lucky bag" is a type of product that offers multiple different products in one package.

[1785] "Packaging" is the process of packing selected items and preparing them for shipment.

[1786] A "smartphone application" is software that runs on a smartphone device and is a program that provides specific functions or services.

[1787] A "notification" is a means of conveying information from a system to a user, typically via email or push notification.

[1788] This invention is a system that uses generative AI to select the most suitable products for a user based on their attribute information, budget information, online behavior history, and trend information, and provides them as lucky bags. This system consists of the following components:

[1789] 1. Obtaining user information

[1790] When a user logs in to the system, the server retrieves the user's attribute information from an external database, including the user's gender, age, and interest tags.

[1791] As a specific example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are "sports" and "technology."

[1792] 2. Enter your budget

[1793] The user enters budget information into a budget entry form on the system. Once the terminal inputs the budget information, the information is sent to the server.

[1794] For example, a user enters a budget of "5,000 yen."

[1795] 3. Product selection process

[1796] The server acquires online behavior history and trend information based on the user's attribute information and budget information. Based on the acquired information, it calls the generation AI module and selects the most suitable products. The generation AI performs scoring based on past purchase history, interest tags, and trending product lists to generate the most suitable product candidate list.

[1797] As a concrete example, generative AI will select sportswear and the latest gadget accessories for users.

[1798] 4. Creation and shipping of lucky bags

[1799] The server sends the selected product list as a lucky bag list to the shipping system. The shipping system picks up each product from inventory based on the lucky bag list and packages it. Once packaged, the lucky bag is handed over to the delivery company.

[1800] As a specific example, a lucky bag containing sportswear and gadget accessories is generated and shipped to the user's address.

[1801] 5. Notice to Users

[1802] The server will notify the user's contacts of the delivery of the lucky bag. Notifications will be sent via email or messaging services. The user will receive a notification on their device and can check the status.

[1803] As a concrete example, a user receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1804] In this invention, the generative AI creates prompts based on the user's attribute information, budget information, online behavior history, and trend information, and then selects the most suitable product based on these.The main hardware and software used include a server, user terminal, generative AI model, database, inventory management system, delivery system, notification service, mail server, etc.

[1805] Example prompt sentence:

[1806] Based on the user's demographic information (gender: male, age: 30, interests: sports, technology), online behavior history, budget: 5,000 yen, and a list of trending products (e.g., "smartwatch," "sneakers," "headphones"), select the most suitable products for the user and generate a lucky bag.

[1807] By implementing the present invention, it is possible to select optimal products based on the user's individual attribute information and budget, and offer them as lucky bags, and to efficiently notify users. This can improve user satisfaction and is expected to increase sales for online shops.

[1808] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1809] Step 1:

[1810] Retrieving User Information

[1811] When a user logs in to the system, the server retrieves the user's attribute information from an external database. The retrieved information includes gender, age, and interest tags. Based on this information, a personalized profile of the user is generated. The input is the user ID, and the output is the user's attribute information.

[1812] Step 2:

[1813] Enter your budget

[1814] The user enters budget information into a form on the system. The terminal retrieves this information and sends it to the server. The input is the budget amount entered by the user, and the output is the budget information sent to the server.

[1815] Step 3:

[1816] Acquisition of online behavior history and trend information

[1817] The server obtains users' online behavioral history and trend information from external sources. The behavioral history includes past purchase history and browsing history, and the trend information includes currently popular products and services. The input is a request for online behavioral history and trend information, and the output is the obtained behavioral history and trend information.

[1818] Step 4:

[1819] Generate prompt statement

[1820] The server creates a prompt for the generative AI model based on the acquired user attribute information, budget information, online behavior history, and trend information. This prompt is used to request a list of optimal products from the generative AI model. The input is the user's composite information, and the output is the prompt.

[1821] Step 5:

[1822] Selection of the optimal product

[1823] The server calls the generative AI model and inputs a prompt to select the most suitable product. The generative AI model considers the user's interests and trends, performs scoring, and selects products. The input is the prompt, and the output is a list of selected products.

[1824] Step 6:

[1825] Creation of lucky bags

[1826] The server generates a list of selected products as a lucky bag list and sends it to the shipping system. At this time, it checks the inventory status of the products and issues instructions to pick up the products from the inventory. The input is the product list, and the output is the lucky bag list.

[1827] Step 7:

[1828] Shipping and Packaging

[1829] The shipping system picks up products from inventory based on the lucky bag list and packages them. Once packaged, the lucky bags are handed over to the delivery company. The input is the lucky bag list, and the output is the packaged lucky bag.

[1830] Step 8:

[1831] User Notification

[1832] The server notifies the user's contacts of the lucky bag shipping information. Notifications are sent via email or push notification. The user receives a notification on their device and can check the status. The input is the shipping information, and the output is a notification message to the user.

[1833] 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.

[1834] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[1835] The system consists of the following components:

[1836] 1. Obtaining user information

[1837] Server: When a user logs in to the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[1838] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[1839] 2. Enter your budget

[1840] User: The user enters budget information into a budget entry form on the system.

[1841] On your device: Enter your budget information and it will be sent to the server.

[1842] Example: User A enters a budget of 5,000 yen.

[1843] 3. Acquiring emotional information

[1844] Server: Calls the emotion engine that analyzes the user's online behavior and input data to obtain the user's emotional information.

[1845] Server: Save the acquired user emotion information as session data.

[1846] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[1847] 4. Product selection process

[1848] Server: Calls the generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[1849] Generative AI: Analyzes each data item in a comprehensive manner, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[1850] Example: For example, a generative AI might select a combination of sportswear and the latest gadget accessories for user A.

[1851] 5. Creation and shipping of lucky bags

[1852] Server: Sends the selected product list to the shipping system as a lucky bag list.

[1853] Shipping system: Based on the lucky bag list, each item is picked up from inventory and packaged. Once packaged, the lucky bag is handed over to the delivery company.

[1854] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1855] 6. Notice to Users

[1856] Server: Notifies the user's contacts of the lucky bag delivery information via email or messaging service.

[1857] On your device: You will receive a notification on your device and be able to check the status.

[1858] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1859] The above is a concrete example of how to put the present invention into practice. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[1860] The processing flow will be explained below.

[1861] Step 1:

[1862] User: Log in to the system.

[1863] Users access the "AI Lucky Bag" system from their smartphone or PC and perform login authentication.

[1864] Step 2:

[1865] Server: Authenticates the login information and obtains the user's attribute information.

[1866] Based on the login information, attribute information such as the user's gender, age, and interest tags is obtained from an external database.

[1867] Step 3:

[1868] Server: Save the acquired user attribute information as session data.

[1869] The acquired user attribute information (gender, age, interest tags) is stored in the session data.

[1870] Step 4:

[1871] User: Enters a budget into a form where budget information is entered.

[1872] Users enter their budget in the input form displayed on the system.

[1873] Step 5:

[1874] Terminal: Sends the entered budget to the server.

[1875] Budget information from the input form is sent to the server.

[1876] Step 6:

[1877] Server: Save the received budget information as session data.

[1878] Add and save the budget information in the session data.

[1879] Step 7:

[1880] Server: Calls the emotion engine to analyze users' online behavior and obtain emotion information.

[1881] The emotion engine analyzes and extracts emotional information based on the user's browsing history and input data.

[1882] Step 8:

[1883] Server: Add the acquired emotion information to the session data and save it.

[1884] The emotional information (e.g., excitement, anticipation) output by the emotion engine is stored in the session data.

[1885] Step 9:

[1886] Server: Inputs data into the generative AI module based on the user's attribute information, budget information, sentiment information, and trend information, and selects the most suitable product.

[1887] Generative AI analyzes data and generates product lists suitable for users.

[1888] Step 10:

[1889] Generative AI: It scores products based on user interest tags and emotional information to generate an optimal list of product candidates.

[1890] Save the product candidate list in session data.

[1891] Step 11:

[1892] Server: Save the optimal product list output by the generation AI as a lucky bag item list.

[1893] Convert the product list into a lucky bag item list and save it.

[1894] Step 12:

[1895] Server: Sends the lucky bag item list to the shipping system.

[1896] The lucky bag item list is formatted and the data is sent to the shipping system.

[1897] Step 13:

[1898] Shipping system: Picks and packages products based on the lucky bag item list.

[1899] The listed items are taken from the inventory system and packaged as lucky bags.

[1900] Step 14:

[1901] Shipping system: Prepares packaged lucky bags for delivery to the delivery company.

[1902] Print the shipping label and prepare the lucky bag for delivery to the delivery company.

[1903] Step 15:

[1904] Server: Notify the user's contacts of the lucky bag shipping information.

[1905] The shipping status of the lucky bag will be sent to the user's contact information (email or message service) along with a tracking number.

[1906] Step 16:

[1907] On your device: You will receive a notification on your device to check the status.

[1908] Users will receive a notification on their smartphone or PC that their lucky bag has been shipped, and will be able to check the shipping status.

[1909] Example 2

[1910] 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."

[1911] Conventional online shopping systems can select products based on user attributes and budget information, but they have the problem of not being able to select products that take into account user emotions or real-time trend information. As a result, user satisfaction declines and sales growth cannot be expected. Furthermore, there has been no effective system for providing users with the best lucky bags.

[1912] 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.

[1913] In this invention, the server includes a means for acquiring user attribute information, a means for receiving budget information entered by the user, and a means for selecting optimal products in consideration of the user's emotional state based on the user's attribute information, budget information, online behavior history, analysis results by the generation AI, and trend information. This makes it possible to provide optimal lucky bags that reflect the user's emotions and real-time trends.

[1914] "User attribute information" refers to personal characteristics such as the user's gender, age, and tags that indicate their interests.

[1915] "Budget Information" means the amount information entered by a User within the System to be used for purchases.

[1916] "Online behavior history" refers to the history of a user's activities on the Internet, and specifically includes browsing history, purchasing history, etc.

[1917] "Generative AI" refers to artificial intelligence that analyzes input data and selects products that are suitable for the user.

[1918] "Trend information" refers to information that shows current trends in the market and consumer interests.

[1919] "Emotional state" refers to emotions analyzed from a user's online behavior and input data, such as joy, excitement, and anticipation.

[1920] A "lucky bag" is a package containing a selection of multiple products.

[1921] "Packaging" refers to the process of wrapping selected products and assembling them into a single package.

[1922] "Notification" refers to a means of informing users of lucky bag shipping information, and is done via email or messaging services.

[1923] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. In particular, by combining it with an emotion analysis engine that analyzes user emotions, it becomes possible to select products that take into account the user's emotional state.

[1924] The system consists of the following components:

[1925] 1. Obtaining user information

[1926] When a user logs into the system, the server retrieves the user's attribute information from an external database, including gender, age, and interest tags.

[1927] Example: For example, when a 30-year-old male user A logs in, the server obtains that his gender is "male," his age is "30," and his interest tags are "sports" and "technology."

[1928] 2. Enter your budget

[1929] The user enters budget information into a budget entry form on the system.

[1930] The terminal sends the information to the server.

[1931] Example: User A enters a budget of 5,000 yen.

[1932] 3. Acquiring emotional information

[1933] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[1934] The emotion information acquired by the server is saved as session data.

[1935] Example: Emotional information such as "excitement" and "anticipation" is extracted from user A's input data and browsing history.

[1936] 4. Product selection process

[1937] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[1938] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[1939] Example: A generative AI selects a combination of sportswear and the latest gadget accessories for user A.

[1940] Example prompt sentence:

[1941] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[1942] Budget: 5,000 yen

[1943] Emotional information: excitement, anticipation

[1944] Online behavior history: browsing sports-related sites, reading gadget review articles

[1945] Latest trends: new sportswear, hot gadget accessories

[1946] 5. Creation and shipping of lucky bags

[1947] The server transmits the selected product list to the shipping business system as a lucky bag list.

[1948] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[1949] Example: A lucky bag containing sportswear and gadget accessories is generated and shipped to user A's address.

[1950] 6. Notice to Users

[1951] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[1952] The device will notify the user.

[1953] Example: User A receives a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[1954] The above is a specific embodiment of the present invention. This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can provide products that meet the needs and emotions of each user, which is expected to increase user satisfaction and sales.

[1955] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1956] Step 1:

[1957] Obtaining user attribute information

[1958] A user logs in to the system.

[1959] Specific operation: User A enters his / her username and password and clicks the login button.

[1960] Input: Username and Password

[1961] Output: After successful login, the user ID is obtained.

[1962] The server accesses an external database and obtains the user's attribute information (gender, age, interest tags).

[1963] Specific operation: The server executes an SQL query and saves the retrieved attribute data of User A ("Male", "30 years old", "Sports", "Technology") as session data.

[1964] Input: User ID

[1965] Output: User demographic information (gender, age, interest tags)

[1966] Step 2:

[1967] Enter your budget

[1968] Access a form where users enter budget information.

[1969] Specific behavior: User A opens the budget input form.

[1970] Input: Budget input form URL

[1971] Output: The budget entry page is displayed.

[1972] The user enters budget information and clicks the submit button.

[1973] Specific actions: User A enters "5,000 yen" and presses the send button.

[1974] Input: Budget information (5000 yen)

[1975] Output: The form data is sent to the server.

[1976] The terminal transmits the input budget information to the server.

[1977] What happens: The form sends an HTTP POST request to the server, and the budget information arrives at the server.

[1978] Input: Budget information (5000 yen)

[1979] Output: Budget information is saved on the server.

[1980] Step 3:

[1981] Acquiring emotional information

[1982] The server calls an emotion analysis engine that analyzes the user's online behavior and input data to obtain emotional information.

[1983] Specific operation: The server sends an API request to send user A's browsing history and form input data to the sentiment analysis engine.

[1984] Input: Online behavioral data, input data

[1985] Output: Emotional information (e.g., "excitement," "anticipation")

[1986] The emotion information acquired by the server is saved as session data.

[1987] Specific operation: The server stores the emotion information received from the emotion analysis engine in the session database.

[1988] Input: Emotion information

[1989] Output: Emotion information is added to the session data.

[1990] Step 4:

[1991] Product selection process

[1992] The server calls a generative AI module based on the user's attribute information, budget information, emotional information, online behavior history, and trend information to select the most suitable product.

[1993] Specific operation: The server generates a prompt and sends it to the generation AI. Prompt text:

[1994] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[1995] Budget: 5,000 yen

[1996] Emotional information: excitement, anticipation

[1997] Online behavior history: browsing sports-related sites, reading gadget review articles

[1998] Latest trends: new sportswear, hot gadget accessories

[1999] Input: Attribute information, budget information, sentiment information, behavioral history, trend information

[2000] Output: Selected product candidate list

[2001] The generative AI performs a comprehensive analysis of each data set, generates a list of product candidates that are best suited to the user, and evaluates them to select the optimal combination.

[2002] How it works: Generative AI scores the candidate list and selects a combination of sportswear and gadget accessories.

[2003] Input: prompt statement

[2004] Output: Optimal product mix

[2005] Step 5:

[2006] Creation and shipping of lucky bags

[2007] The server transmits the selected product list to the shipping business system as a lucky bag list.

[2008] Specific operation: The server sends an API request to send the product list to the packaging and delivery system.

[2009] Input: Selected product list

[2010] Output: Lucky bag list

[2011] Based on the lucky bag list, the shipping system will pick up each item from inventory, package it, and hand it over to the delivery company.

[2012] Specific operation: The warehouse system generates a picking list, and staff pack the items and hand them over to the delivery company.

[2013] Input: Lucky Bag List

[2014] Output: Packaged lucky bags

[2015] Step 6:

[2016] User Notification

[2017] The server will then notify the user's contacts of the lucky bag delivery information via email or messaging service.

[2018] Specific operation: The server sends a message "Your lucky bag has been shipped" using email or a messaging service.

[2019] Input: Lucky bag shipping information

[2020] Output: Information message

[2021] The device will notify the user.

[2022] Specific behavior: A notification will pop up on User A's smartphone, allowing them to check the status.

[2023] Input: Notification message

[2024] Output: Notification display on smartphone

[2025] (Application example 2)

[2026] 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."

[2027] Conventional online shopping systems have mechanisms for suggesting products based on user attribute information and budget information, but they are unable to select products that take into account the user's emotional state, and therefore are unable to sufficiently increase user satisfaction. Furthermore, there is a need to provide a personalized shopping experience by proposing products that incorporate emotional information. Furthermore, offering selected products as lucky bags is also a challenge, as it provides a new shopping experience for users and increases their satisfaction.

[2028] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2029] In this invention, the server includes means for acquiring user attribute information, means for receiving budget information entered by the user, means for analyzing the user's online behavior and input data and acquiring emotional information, means including a generation AI for selecting optimal products based on the user's attribute information, budget information, online behavior history, emotional information, and trend information, means for packaging and shipping the selected products as a lucky bag, and means for notifying the user of lucky bag shipping information. This makes it possible to select optimal products based on the user's attribute information, emotional information, and budget information, providing a personalized purchasing experience and high satisfaction.

[2030] "User attribute information" refers to basic information related to individual users, such as their gender, age, and interest tags.

[2031] "Budget information" is information indicating the upper limit of the amount set by the user for purchases.

[2032] "Online behavior" is a record of all actions a user takes on a website or application, including clicks, views, and purchases.

[2033] "Emotional information" is information that indicates a user's emotional state analyzed from their online behavior and input data.

[2034] "Generative AI" is artificial intelligence that generates optimal products based on user information.

[2035] "Trend information" is information about currently popular products and services.

[2036] A "lucky bag" is a package containing multiple selected products.

[2037] "Packaging" is the process of assembling the selected products into lucky bags.

[2038] "Shipping" is the process of sending the packaged lucky bag to the user.

[2039] "Notification" refers to the act of informing users of the shipping information for lucky bags.

[2040] This invention is a system that utilizes user attribute information, budget information, online behavior history, trend information, and emotional information to select the most suitable products for each user using generative AI and provide them as lucky bags. A specific embodiment of this system is shown below.

[2041] When a user logs in to the system, the server first obtains the user's attribute information from an external database. This information includes gender, age, and interest tags. For example, when a 30-year-old male user logs in, the server obtains that the user's gender is male, age is 30, and interest tags are sports and technology.

[2042] Next, when the user inputs budget information through the terminal, the information is sent to the server. For example, if the user inputs a budget of 5,000 yen, this information is saved on the server.

[2043] The server then calls an emotion engine that analyzes the user's online behavior and input data to obtain the user's emotion information and save it as session data. For example, it may be analyzed to determine whether the user is feeling excitement or anticipation.

[2044] Based on this information, the server calls the AI ​​generator, which then comprehensively analyzes the user's attributes, budget, online behavior, trends, and emotional information to select the most suitable products. For example, the AI ​​generator can select a combination of sportswear and the latest gadget accessories for the user.

[2045] The server sends the selected product list as a lucky bag list to the shipping system, which then picks and packages the items from the inventory. For example, a lucky bag containing sportswear and gadget accessories is created and shipped to the user's address.

[2046] The server then notifies the user's contacts of the delivery of the lucky bag. Notifications are sent via email or messaging services, and the user can check the status on their own device. For example, the user might receive a message on their smartphone saying, "Your lucky bag has been shipped. It will arrive within a few days, so look forward to it!"

[2047] This system allows users to receive lucky bags that match their attributes, emotional state, and interests, and online shops can expect to increase user satisfaction and sales by providing products that meet the needs and emotions of each user.

[2048] Prompt Sentence Examples

[2049] User demographic information: Gender - Male, Age - 30, Interests - Sports, Technology

[2050] Budget: 5,000 yen

[2051] Emotional information: excitement, anticipation

[2052] Use this information to help you choose the best product for your users."

[2053] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2054] Step 1:

[2055] Get user attribute information

[2056] Subject: Server

[2057] Input: User logged into the system

[2058] Data processing and calculation: The server sends an API request to an external database to obtain the user's gender, age, and interest tags.

[2059] Output: User demographic information (e.g., gender - male, age - 30, interest tags - sports, technology)

[2060] Step 2:

[2061] Entering budget information

[2062] Subject: User

[2063] Input: The user inputs budget information through the device (e.g., 5,000 yen)

[2064] Data transmission: Budget information is sent from the device to the server.

[2065] Output: User's budget information saved on the server (e.g., 5,000 yen)

[2066] Step 3:

[2067] Acquiring emotional information

[2068] Subject: Server

[2069] Input: Your online behavior and input data

[2070] Data processing and calculation: The server calls the emotion analysis engine, analyzes the input data, and extracts the user's emotional information (e.g., excitement, anticipation).

[2071] Output: Obtained user sentiment information

[2072] Step 4:

[2073] Product selection process

[2074] Subject: Server

[2075] Input: User attribute information, budget information, online behavior history, trend information, emotional information

[2076] Data processing and calculation: The server inputs this information into the generation AI, which then selects the most suitable product based on the prompt (e.g., sportswear, the latest gadget accessories).

[2077] Output: List of selected products

[2078] Step 5:

[2079] Creation and shipping of lucky bags

[2080] Subject: Server and shipping system

[2081] Input: Selected product list

[2082] Data transmission and physical operation: The server sends the selected product list to the shipping system, picks up each product from the inventory, and packages it into a lucky bag.

[2083] Output: Packaged lucky bags are generated and handed over to the delivery company.

[2084] Step 6:

[2085] User Notification

[2086] Subject: Server

[2087] Input: Lucky bag shipping information

[2088] Data transmission and notification behavior: The server notifies the user's contacts (via email or messaging services)

[2089] Output: A notification message that arrives on the user's device (e.g., "Your lucky bag has been shipped. It will arrive in a few days!")

[2090] 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.

[2091] 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.

[2092] 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.

[2093] 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.

[2094] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2095] 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.

[2096] 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).

[2097] 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.

[2098] 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."

[2099] 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.

[2100] 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).

[2101] 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.

[2102] 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.

[2103] 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.

[2104] 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.

[2105] 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.

[2106] 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.

[2107] 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.

[2108] 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.

[2109] 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.

[2110] 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.

[2111] The following is further disclosed regarding the above embodiment.

[2112] (Claim 1)

[2113] A means for acquiring user attribute information;

[2114] a means for receiving user-entered budget information;

[2115] A means including a generation AI that selects optimal products based on user attribute information, budget information, online behavior history, and trend information;

[2116] The selected products will be packaged and shipped as lucky bags,

[2117] A system including a means for notifying users of lucky bag shipping information.

[2118] (Claim 2)

[2119] 2. The system according to claim 1, wherein the user attribute information comprises gender and age.

[2120] (Claim 3)

[2121] 2. The system of claim 1, wherein the online behavior history and trend information comprises a purchase history and interest tags.

[2122] "Example 1"

[2123] (Claim 1)

[2124] A means for acquiring user attribute information;

[2125] a means for receiving user-entered budget information;

[2126] A means including a generation AI that selects optimal products based on user attribute information, budget information, online behavior history, and trend information;

[2127] A means for generating a list of selected products as a lucky bag list and transmitting the list to a shipping system;

[2128] A method to pick up products from inventory based on the lucky bag list, package them, and hand them over to a delivery company;

[2129] A system including a means for notifying a user's contact information of lucky bag shipping information.

[2130] (Claim 2)

[2131] 2. The system according to claim 1, wherein the user attribute information comprises gender, age, and interest tags.

[2132] (Claim 3)

[2133] 2. The system of claim 1, wherein the online behavior history and trend information comprises a purchase history, interest tags, and a trending product list.

[2134] "Application Example 1"

[2135] (Claim 1)

[2136] A means for acquiring user attribute information;

[2137] a means for receiving user-entered budget information;

[2138] A means including a generation AI that selects optimal products based on user attribute information, budget information, online behavior history, and trend information;

[2139] The selected products will be packaged and shipped as lucky bags,

[2140] A means of notifying users of lucky bag shipping information,

[2141] A means for displaying using the generation AI, the means including an application for a smartphone;

[2142] a means for providing users with shipping information and notifications through said smartphone application;

[2143] A means for providing prompts to the generating AI based on the above information and trend information;

[2144] A system including:

[2145] (Claim 2)

[2146] 2. The system according to claim 1, wherein the user attribute information comprises gender and age.

[2147] (Claim 3)

[2148] 2. The system of claim 1, wherein the online behavior history and trend information comprises a purchase history and interest tags.

[2149] "Example 2: Combining Emotion Engines"

[2150] (Claim 1)

[2151] A means for acquiring user attribute information;

[2152] a means for receiving user-entered budget information;

[2153] A method for selecting optimal products based on the user's attribute information, budget information, online behavior history, analysis results by the generation AI, and trend information, taking into account the user's emotional state;

[2154] The selected products will be packaged and shipped as lucky bags,

[2155] A system including a means for notifying users of lucky bag shipping information.

[2156] (Claim 2)

[2157] 2. The system according to claim 1, wherein the user attribute information comprises gender and age.

[2158] (Claim 3)

[2159] 2. The system of claim 1, wherein the online behavior history and trend information comprises a purchase history and interest tags.

[2160] "Application example 2 when combining emotion engines"

[2161] (Claim 1)

[2162] A means for acquiring user attribute information;

[2163] a means for receiving user-entered budget information;

[2164] A means of analyzing users' online behavior and input data to obtain emotional information;

[2165] A means including a generation AI that selects optimal products based on user attribute information, budget information, online behavior history, emotional information, and trend information;

[2166] The selected products will be packaged and shipped as lucky bags,

[2167] A system including a means for notifying users of lucky bag shipping information.

[2168] (Claim 2)

[2169] 2. The system according to claim 1, wherein the user attribute information comprises gender and age.

[2170] (Claim 3)

[2171] 2. The system of claim 1, wherein the online behavior history and trend information comprises a purchase history and interest tags. [Explanation of symbols]

[2172] 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 acquiring user attribute information; a means for receiving user-entered budget information; A means including a generation AI that selects optimal products based on user attribute information, budget information, online behavior history, and trend information; The selected products will be packaged and shipped as lucky bags, A system including a means for notifying users of lucky bag shipping information.

2. 2. The system according to claim 1, wherein the user attribute information comprises gender and age.

3. The system of claim 1 , wherein the online behavior history and trend information comprises a purchase history and interest tags.

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