system

Abstract

We provide the system. [Solution] Means of collecting information entered by the user, A means of sending user information to the server, A means by which the server analyzes user information and recommends corresponding products, A means of sending recommended product information to the user's terminal, A means of displaying recommended products on the user's terminal, The means by which users select products and proceed with the purchase process, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In online shopping, when a user selects a product suitable for themselves, especially in the case of products that require specialized knowledge (such as snowboards and boots), it is difficult to make the optimal choice for themselves. For this reason, there is a risk that the user will spend time selecting an appropriate product or purchase a wrong product. In addition, since there is no system that automatically recommends the optimal product for the user, there is a problem that it is difficult to improve the purchasing desire.

Means for Solving the Problems

[0005] This invention provides a system in which a user inputs their own information (e.g., age, height, weight, preferences, intended use, etc.), and a server analyzes that information to automatically recommend the most suitable product to the user. Specifically, the system includes means for collecting information entered by the user, means for transmitting that information to a server, means for the server to analyze the user information and recommend corresponding products, means for transmitting and displaying the recommended product information on the user's terminal, and means for the user to select a product and proceed with the purchase procedure. This makes it easier for the user to select an appropriate product and can increase their willingness to purchase.

[0006] A "user" refers to a person who uses the system to search for and purchase products.

[0007] "Input information" refers to personal information provided by the user, such as age, height, weight, preferences, and planned use, as well as information indicating purchasing intent.

[0008] A "server" refers to a computer system that receives and analyzes user input information via a network and recommends the most suitable products.

[0009] "Terminal" refers to a device (such as a smartphone, tablet, or personal computer) used by a user to input information and receive and display recommended product information.

[0010] A "database" refers to a collection of data that stores information about products (such as product name, price, features, and design), which a server uses to perform searches and recommendations.

[0011] "Recommended products" refer to products that the server analyzes based on user input and suggests to the user.

[0012] "Product selection" refers to the act of a user choosing a product they want to purchase from among the recommended products.

[0013] "Purchase process" refers to a series of online procedures that a user undertakes to actually purchase a product they have selected.

[0014] "Purchase information" refers to the details of the product that the user ultimately selected and completed the purchase process for (product ID, quantity, payment method, shipping address, etc.).

[0015] "User interface" refers to a part of software that provides screens and functions for users to interact with the system and input / retrieve information. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the 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 processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0037] ---

[0038] This invention is a system designed to help users select appropriate products and increase their purchasing intent. The system includes a process where it recommends products based on personal information and preferences provided by the user, and the user then selects and purchases a product based on these recommendations.

[0039] Specifically, this system is configured as follows:

[0040] Entering user information

[0041] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user wants to purchase a snowboard, they would enter information such as height 170cm, weight 65kg, and preference for blue and black designs.

[0042] Submitting the entered information

[0043] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS). The input information is encrypted before transmission, ensuring the security of the data during transmission.

[0044] Receiving and analyzing user information

[0045] Server: The server receives user input information sent from the terminal and begins analysis. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[0046] Generate product recommendations

[0047] Server: The server searches the database for corresponding products based on the analyzed user information. It uses machine learning algorithms and filtering techniques to list the products best suited to the user's needs. For example, it might generate a list recommending the optimal snowboard and boots based on the user's height and weight.

[0048] Sending and displaying recommended products

[0049] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[0050] Product selection and purchase

[0051] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[0052] Submission and confirmation of purchase information

[0053] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[0054] This system makes it easier for users to select the right products, even without specialized knowledge. Furthermore, personalized recommendations for each user increase their purchasing intent and enable more efficient online shopping.

[0055] ---

[0056] The following describes the processing flow.

[0057] ---

[0058] Step 1:

[0059] User: The user launches the app and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use.

[0060] Specific actions:

[0061] Launch the app and select a product category (for example, snowboards).

[0062] Enter your age (e.g., 25 years old), height (e.g., 170cm), weight (e.g., 65kg), preferences (e.g., blue or black design), and planned date of use in the input form.

[0063] Step 2:

[0064] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS).

[0065] Specific actions:

[0066] After completing the input, the user presses the "Next" or "Submit" button.

[0067] The input information is encrypted and sent to the server.

[0068] Step 3:

[0069] Server: Receives and analyzes user input information sent from the terminal.

[0070] Specific actions:

[0071] The system decodes the received data and analyzes information such as age, height, weight, preferred design and color, and intended use.

[0072] Based on this information, we extract parameters to narrow down the recommended products.

[0073] Step 4:

[0074] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products.

[0075] Specific actions:

[0076] A query is sent to the database to search for products that match the user's parameters.

[0077] Evaluate search results and create a list to recommend the best products to the user.

[0078] Step 5:

[0079] Server: Sends the generated list of recommended products to the terminal.

[0080] Specific actions:

[0081] Convert the recommended product list into JSON format and send it to the user's terminal.

[0082] The list includes product images, prices, features, and other users' ratings.

[0083] Step 6:

[0084] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[0085] Specific actions:

[0086] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[0087] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[0088] Step 7:

[0089] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[0090] Specific actions:

[0091] The user taps on the product to view details and then presses the "Add to Cart" button.

[0092] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[0093] Step 8:

[0094] Terminal: When a user completes the purchase process, it sends that information to the server.

[0095] Specific actions:

[0096] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[0097] Step 9:

[0098] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[0099] Specific actions:

[0100] It integrates with the inventory system to check the stock of the selected product.

[0101] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[0102] We will notify you of information such as the expected shipping date and tracking number.

[0103] ---

[0104] The above outlines the specific processing steps of this system. This process allows users to easily select the most suitable product and proceed with the purchase smoothly.

[0105] (Example 1)

[0106] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0107] Traditional online shopping systems often made it difficult for users to find suitable products from a large amount of information, leading to decreased purchasing intent. Furthermore, there were security concerns, as users' personal information was sometimes not protected. Additionally, the lack of appropriate recommendation algorithms meant that accurate product recommendations were not possible.

[0108] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0109] In this invention, the server includes means for encrypting user information and transmitting it to the server using a secure communication protocol, means for the server to analyze the user information and recommend corresponding products, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide accurate product recommendations based on user preferences while ensuring the security of user information.

[0110] "User information" refers to personal data that users enter into the system, such as age, height, weight, preferred design and color, and intended use.

[0111] "Encryption" is the process of transforming data according to a specific algorithm in order to ensure the security of data during transmission.

[0112] A "secure communication protocol" is a protocol used to ensure the confidentiality and integrity of data during data communication over the internet, and HTTPS is a representative example.

[0113] A "server" is a computer system that receives data sent by a user, performs various processing steps, and returns the results to the user.

[0114] "Analysis" is the process of examining user information using specific algorithms and methods to extract meaningful data and parameters.

[0115] A "machine learning algorithm" is an algorithm that performs predictions and classifications based on data, and in this context, it is a method used to recommend the most suitable product to a user.

[0116] The "Recommended Products List" is a list of products selected based on user information, tailored to the user's preferences and needs.

[0117] A "user interface" is a means for a user to interact with a computer or system, and in this context, it refers to a screen that displays product information or the progress of the purchase process.

[0118] The "Add to Cart" function allows users to temporarily reserve items they are considering purchasing.

[0119] The "purchase process" refers to a series of operations in which the user enters the payment method and shipping address for the selected product and finally confirms the purchase.

[0120] This invention is a system that helps users select appropriate products and improves their purchasing intent. The system includes a process in which products are recommended based on personal information and preferences provided by the user, and the user selects a product based on these recommendations and proceeds with the purchase.

[0121] Hardware and software to be used

[0122] User terminal: A device used by the user to enter information and view a list of recommended products (e.g., smartphone, tablet, PC).

[0123] Server: A computer system used for receiving, analyzing, recommending products, and transmitting results.

[0124] Software libraries and frameworks:

[0125] Data analysis: Python's pandas and numpy.

[0126] Machine learning algorithm: Scikit-learn.

[0127] Encryption: AES (Advanced Encryption Standard).

[0128] Communication protocol: HTTPS.

[0129] Frontend: React, Vue.js.

[0130] Entering user information

[0131] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user is 170cm tall, weighs 65kg, likes blue and black designs, and wants to purchase a freestyle snowboard, they would enter that information into the input form.

[0132] Submitting the entered information

[0133] Terminal: The terminal encrypts the collected user information and sends it to the server using HTTPS, a secure communication protocol. The encryption technology used is AES, which ensures the security of the data during transmission.

[0134] Receiving and analyzing user information

[0135] Server: The server receives encrypted data sent from the terminal and decrypts it. Next, it analyzes the received data using Python's pandas and numpy libraries to extract parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[0136] Generate product recommendations

[0137] Server: Based on the analyzed user information, the server searches the database for relevant products. The search uses machine learning algorithms (collaborative filtering and content-based filtering utilizing Scikit-learn) to list the products best suited to the user's needs. For example, it might add the optimal snowboard and boots for the user's height and weight to the recommendation list.

[0138] Sending and displaying recommended products

[0139] Server and Terminal: The server converts the generated recommended product list into JSON format and sends it back to the terminal. The terminal parses the received list using a JavaScript framework such as React or Vue.js and displays it in the user interface. The recommended product list includes product images, prices, features, and reviews from other users. For example, it might display images and detailed information of a blue and black snowboard and boots in the appropriate size.

[0140] Product selection and purchase

[0141] User: The user selects the product they wish to purchase from the displayed list of recommended products. They add the product to their cart by clicking the "Add to Cart" button. Then, they follow the on-screen instructions to proceed with the purchase, entering their payment method and shipping information. For example, a user adds a blue snowboard and boots of the appropriate size to their cart, enters their credit card information and shipping address, and confirms the purchase.

[0142] Submission and confirmation of purchase information

[0143] Terminal and Server: Once a user completes the purchase process, the terminal sends the purchase information to the server. The server stores the received purchase information in its database and checks the product's inventory. It then verifies the purchase and sends an email to the user notifying them that the purchase is complete and providing the estimated shipping date. As a specific example, a "Purchase Completion Notification" email is sent to the user's email address, providing the estimated shipping date and tracking number.

[0144] Examples of specific cases and prompt statements

[0145] Specific example

[0146] 1. The user launches the app and expresses a desire to purchase a snowboard.

[0147] 2. The user enters the following information into the input form:

[0148] Age: 25

[0149] Height: 170cm

[0150] Weight: 65kg

[0151] Preferred design: Blue or black

[0152] Usage: Freestyle

[0153] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[0154] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[0155] 5. The recommendation list is sent to the device and displayed in the user interface.

[0156] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[0157] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[0158] Example of a prompt

[0159] For example, the prompt text to be input to the generative AI model would be as follows:

[0160] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[0161] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0162] Step 1: Enter user information

[0163] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use. For example, a user might enter "Height 170cm, Weight 65kg, Likes blue and black designs" into the input form.

[0164] Input: User's personal information (age, height, weight, preferred design, intended use).

[0165] Output: User input data.

[0166] Step 2: Encrypt and send the entered information.

[0167] Terminal: The terminal encrypts the user's input information using AES (Advanced Encryption Standard). It then sends this encrypted data to the server using HTTPS, a secure communication protocol.

[0168] Input: User input data.

[0169] Data processing: Encryption using AES.

[0170] Output: Encrypted user information data.

[0171] Step 3: Receiving and decrypting user information

[0172] Server: The server receives encrypted data sent from the terminal. Next, it decrypts the received data. This decryption uses a key that was shared in advance.

[0173] Input: Encrypted user information data.

[0174] Data processing: Decryption of encrypted data.

[0175] Output: Decoded user information data.

[0176] Step 4: Analyze user information

[0177] Server: Analyzes the decrypted user information. Using Python's pandas and numpy libraries, the server extracts parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[0178] Input: Decrypted user information data.

[0179] Data processing: Data analysis and parameter extraction (pandas, numpy).

[0180] Output: Parameters for recommendation.

[0181] Step 5: Search for products and generate a recommendation list

[0182] Server: Uses machine learning algorithms (e.g., Scikit-learn) to search a database based on user parameters. Generates a list of recommended products. For example, it might list snowboards and boots that match the user's height and weight.

[0183] Input: Parameters for recommendation.

[0184] Data processing: Search and list generation using machine learning algorithms.

[0185] Output: Recommended product list.

[0186] Step 6: Submit your recommended product

[0187] Server: Converts the generated list of recommended products into JSON format and sends it to the user's terminal.

[0188] Input: Recommended product list.

[0189] Data processing: Conversion to JSON format.

[0190] Output: A list of recommended products in JSON format.

[0191] Step 7: Display recommended products

[0192] Terminal: The terminal parses the received list of recommended products and displays it in the user interface. This display uses React or Vue.js. The displayed list includes product images, prices, features, and reviews from other users.

[0193] Input: A list of recommended products in JSON format.

[0194] Data processing: Parsing and displaying lists.

[0195] Output: Recommended product list on the user interface.

[0196] Step 8: Product Selection and Purchase Procedure

[0197] User: The user selects the product they wish to purchase from the displayed recommended products and clicks the "Add to Cart" button. They then enter their payment method and shipping information and proceed with the purchase.

[0198] Input: User's desired purchase items and payment information.

[0199] Data processing: Proceeding with the purchase procedure.

[0200] Output: Purchase confirmation data.

[0201] Step 9: Submit and confirm purchase information

[0202] Terminal and Server: The terminal sends purchase confirmation data to the server. The server stores the received purchase information in its database and checks inventory. After that, it verifies the purchase and notifies the user of the purchase completion and estimated shipping date.

[0203] Input: Purchase confirmation data.

[0204] Data processing: Database storage and inventory checks.

[0205] Output: Purchase completion notification and estimated shipping date.

[0206] The above describes the specific program processing flow in this system.

[0207] Examples of specific cases and prompt statements

[0208] Specific example

[0209] 1. The user launches the app and expresses a desire to purchase a snowboard.

[0210] 2. The user enters the following information into the input form:

[0211] Age: 25

[0212] Height: 170cm

[0213] Weight: 65kg

[0214] Preferred design: Blue or black

[0215] Usage: Freestyle

[0216] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[0217] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[0218] 5. The recommendation list is sent to the device and displayed in the user interface.

[0219] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[0220] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[0221] Example of a prompt

[0222] For example, the prompt text to be input to the generative AI model would be as follows:

[0223] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[0224] (Application Example 1)

[0225] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0226] The aim is to help users who are unsure which products to choose, enabling them to select appropriate products even without specialized knowledge, thereby increasing their purchasing intent. Furthermore, the goal is to improve the user experience by providing personalized recommendations to users during online shopping.

[0227] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0228] In this invention, the server includes means for securely transmitting user input information via the HTTPS protocol, means for recommending the most suitable products from a database using machine learning algorithms, and means for searching for products using prompt sentences with a generative AI model. This allows users to easily find suitable products and enhance their purchasing intent through a personalized experience.

[0229] A "user terminal" refers to a device used by a user, such as a smartphone, tablet, or computer.

[0230] The HTTPS protocol is a secure communication protocol for safely sending and receiving data over the internet.

[0231] A "machine learning algorithm" is a method that analyzes data, recognizes patterns, and derives relevant results.

[0232] A "database" is a system for organizing and managing collections of data, and it stores user information and product information.

[0233] A "generative AI model" is a model used to perform specific tasks using artificial intelligence, making predictions and recommendations based on input data.

[0234] A "prompt message" is text input to a generative AI model, and its role is to give the model instructions for performing a specific task.

[0235] A "recommended product" is a list of products that the server selects and provides based on user information analysis, ensuring the product best suits the user's needs.

[0236] An "interface" is a means by which a user and a system can interact and communicate with each other, and includes graphical user interfaces (GUIs) and command-line interfaces (CLIs).

[0237] This invention is a system that recommends optimal products based on a user's personal information and preferences, thereby increasing their willingness to purchase. The system includes a user terminal, a secure communication protocol, a server, a machine learning algorithm, a generative AI model, and a database.

[0238] Entering user information

[0239] The user launches the application using their smartphone and selects the product category they wish to purchase. Next, they enter information such as age, height, weight, preferred color and design, and intended use into an input form. For example, if a user wants to purchase outdoor equipment, they would enter information such as age 30, height 175cm, weight 70kg, preference for blue, and camping equipment.

[0240] Submitting the entered information

[0241] The user terminal securely transmits the collected input information to the server using the HTTPS protocol. The input information is encrypted before transmission, ensuring the security of the data during communication.

[0242] Receiving and analyzing user information

[0243] The server receives user input information sent from the terminal and begins analysis using a machine learning algorithm. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[0244] Generate product recommendations

[0245] The server searches the database for corresponding products based on the analyzed user information. The search uses a generative AI model and prompt text to list the products best suited to the user's needs. For example, it might generate a list recommending a blue outdoor jacket, a suitable tent, and a sleeping mat.

[0246] Sending and displaying recommended products

[0247] The server sends the generated list of recommended products to the user's terminal, which then analyzes the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings.

[0248] Product selection and purchase

[0249] The user selects the items they wish to purchase from the list of recommended products and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue outdoor jacket and a matching tent to their cart, enter their payment method and shipping address, and then confirm the purchase.

[0250] Submission and confirmation of purchase information

[0251] Once the user completes the purchase process, the purchase information is sent from the device to the server. The server checks the inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[0252] Hardware and software to use

[0253] Smartphone: A device used by users to input information and view results.

[0254] Server: Uses Python, TENSORFLOW®, and Firebase to perform data analysis and recommendation algorithms.

[0255] Communication protocol: Uses HTTPS to securely send and receive data.

[0256] Database: User and product information is managed using Firebase.

[0257] Examples of prompt statements

[0258] Please recommend outdoor gear for camping, for a user who is 175cm tall, weighs 70kg, likes the color blue, and is looking for products suitable for camping.

[0259] As a result, users can easily find the right products, and their purchasing intent can be increased through a personalized experience.

[0260] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0261] Step 1:

[0262] The user launches the application using their smartphone. The user selects the product category they wish to purchase and enters information such as age, height, weight, preferred design and color, and intended use into the input form. This data is user information and is used for the next step.

[0263] Step 2:

[0264] The terminal encrypts the information entered by the user using the HTTPS protocol and securely sends it to the server. Specifically, the application converts the input data into JSON format and sends it to the server using an HTTPS POST request. The output of this step is encrypted user information.

[0265] Step 3:

[0266] The server receives user input information sent from the terminal. It parses the received data and extracts parameters from the JSON format. The data used here is user information, and the parsing results become the input for the next step.

[0267] Step 4:

[0268] The server uses Python and TensorFlow to apply machine learning algorithms and calculate parameters for recommending the most suitable products based on user input. Specifically, it analyzes information such as the user's height, weight, age, preferred design and color, and intended use to extract product characteristics. The output of this step is the characteristic information of the recommended products.

[0269] Step 5:

[0270] The server searches for corresponding products in its database (Firebase) based on the analyzed user information and generates prompt messages using a generative AI model. Specifically, it uses the prompt message to instruct the AI ​​model to search the database and generate an optimal product list. For example, it might generate a prompt message such as, "The user is 175cm tall, weighs 70kg, likes the color blue, and would like recommendations for outdoor equipment to use for camping." The output of this step is a list of recommended products.

[0271] Step 6:

[0272] The server sends the generated list of recommended products to the terminal. Specifically, it converts the generated product list into JSON format and sends it to the terminal using an HTTPS POST request. The output of this step is the list information of recommended products.

[0273] Step 7:

[0274] The terminal analyzes the received list of recommended products and displays it on the user interface. Specifically, it analyzes the JSON data to display product images, prices, features, and other users' ratings. The output of this step is the recommended product information displayed to the user.

[0275] Step 8:

[0276] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. Next, they proceed with the purchase process following the on-screen instructions. Specifically, this involves adding the desired items to the cart and entering payment and shipping information. The input in this step is the user's purchase preferences and payment information, while the output is information about the items added to the cart.

[0277] Step 9:

[0278] When the user completes the purchase procedure, the terminal sends the purchase information to the server. As a specific operation, the purchase information is converted into JSON format and sent to the server using an HTTPS POST request. The output of this step is the sent purchase information.

[0279] Step 10:

[0280] The server checks the inventory based on the received purchase information and confirms the purchase. Next, it notifies the user that the purchase has been completed and the scheduled shipping date. The specific operation is to check the inventory status using an inventory management system and send a purchase confirmation email to the user. The output of this step is the purchase confirmation and the notification of the scheduled shipping date.

[0281] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0282] ---

[0283] The present invention is a system that supports a user in selecting the most suitable product for himself / herself, and further aims to improve the user's purchase experience by combining an emotion engine that recognizes the user's emotion. This system supports from product recommendation to purchase procedure based on the personal information, preferences, and emotional state provided by the user.

[0284] Specifically, this system is configured as follows.

[0285] Input of user information and emotion information

[0286] User: The user launches the application and selects the product category they wish to purchase. Subsequently, the user enters information such as age, height, weight, preferred design and color, and intended usage date. Additionally, the user provides the system with their current emotional state (e.g., joy, excitement, confusion, etc.) through voice or facial expression inputs. For example, if the user wishes to purchase a snowboard, they enter information such as a height of 170 cm, a weight of 65 kg, and a preference for blue or black designs, and further collect real-time facial expressions and voices through the camera and microphone for the emotion engine.

[0287] Transmission of Information

[0288] Terminal: The terminal transmits the user's input information and emotional information to the server using a secure communication protocol (e.g., HTTPS). Since the user information and emotional information are encrypted before transmission, the security of the data during communication is guaranteed.

[0289] Receiving and Analyzing Information

[0290] Server: The server receives the user's information and emotional information transmitted from the terminal and begins analysis. In the analysis, in addition to the user's height, weight, age, preferred design and color, and purpose of use, the emotional state is evaluated to extract parameters for recommending the optimal product based on this information. For example, if the user is excited, recommended products that reflect that emotion are picked out.

[0291] Generating Product Recommendations

[0292] Server: Based on the analysis results, the server searches the database for corresponding products. Machine learning algorithms and filtering techniques are used in the search to list up the products that are most suitable for the user's desires and emotional state. For example, due to the user being in an excited state, brightly colored and highly designed snowboards and boots are recommended with emphasis.

[0293] Transmission and Display of Recommended Products

[0294] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[0295] Product selection and purchase

[0296] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[0297] Submission and confirmation of purchase information

[0298] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date.

[0299] This invention enables product recommendations that reflect user emotions in real time, thereby increasing user purchasing intent. Furthermore, users can select appropriate products without requiring specialized knowledge, providing a seamless online shopping experience.

[0300] ---

[0301] The following describes the processing flow.

[0302] ---

[0303] Step 1:

[0304] User: The user launches the app and selects the product category they wish to purchase. Subsequently, they input information such as age, height, weight, preferred design and color, and intended usage. Additionally, they provide their expressions and voice to the system in real-time through the camera and microphone, and the emotion engine recognizes the user's emotional state.

[0305] Specific operations:

[0306] Launch the app and select a product category (e.g., snowboard).

[0307] Enter age (e.g., 25 years old), height (e.g., 170 cm), weight (e.g., 65 kg), preferences (e.g., blue and black designs), intended usage date, etc. into the input form.

[0308] Enable the camera and microphone and send expressions and voice to the emotion engine in real-time.

[0309] Step 2:

[0310] Terminal: The user's input information and emotion information are sent to the server using a secure communication protocol (e.g., HTTPS).

[0311] Specific operations:

[0312] After entering the information, press the "Next" or "Send" button.

[0313] The input information and emotion information are encrypted and sent to the server.

[0314] Step 3:

[0315] Server: Receive the user information and emotion information sent from the terminal and start analysis.

[0316] Specific operations:

[0317] Decode the received data and analyze age, height, weight, preferred design and color, intended usage, emotional state, etc.

[0318] Based on this information, we extract the parameters used to search for recommended products.

[0319] Step 4:

[0320] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products that reflect the emotional state.

[0321] Specific actions:

[0322] A query is sent to the database to search for products that match the user's parameters and emotional state.

[0323] We generate the optimal recommended product list, taking into account product popularity and ratings.

[0324] For example, if a user is excited, we might recommend products with vibrant designs.

[0325] Step 5:

[0326] Server: Sends the generated list of recommended products to the terminal.

[0327] Specific actions:

[0328] Convert the recommended product list into JSON format and send it to the user's terminal.

[0329] Recommended products include product images, prices, features, and reviews from other users.

[0330] Step 6:

[0331] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[0332] Specific actions:

[0333] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[0334] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[0335] Step 7:

[0336] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[0337] Specific actions:

[0338] The user taps on the product to view details and then presses the "Add to Cart" button.

[0339] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[0340] Step 8:

[0341] Terminal: When a user completes the purchase process, it sends that information to the server.

[0342] Specific actions:

[0343] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[0344] Step 9:

[0345] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[0346] Specific actions:

[0347] It integrates with the inventory system to check the stock of the selected product.

[0348] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[0349] We will notify you of information such as the expected shipping date and tracking number.

[0350] ---

[0351] The above outlines the specific processing steps of the system that incorporates the emotion engine. This process allows users to select the optimal product according to their emotional state and proceed with the purchase smoothly.

[0352] (Example 2)

[0353] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0354] Traditional online shopping systems mostly recommended products based on users' personal information, without considering their emotional state. Therefore, they were unable to provide product recommendations that reflected users' real-time emotional states, failing to adequately enhance user satisfaction and purchasing intent. Furthermore, traditional systems lacked the means to assist users in selecting the optimal product, even if they lacked specialized knowledge.

[0355] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input personal information and emotional information, means for transmitting the user's personal information and emotional information to the server, and means for the server to analyze the user's personal information and emotional information and recommend corresponding products. This makes it possible to recommend products that take into account not only the user's personal information but also real-time emotional information. This is expected to improve user satisfaction and purchasing intent, and enable users to choose appropriate products even without specialized knowledge.

[0356] ---

[0357] A "user" refers to an individual or group that uses this system to search for, select, and purchase products.

[0358] "Personal information" refers to information that can identify a user, such as the user's age, height, weight, preferred design and color, and planned usage date.

[0359] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) obtained by analyzing the user's facial expressions, voice, etc.

[0360] A "server" refers to a central processing unit that receives and analyzes users' personal and emotional information and recommends corresponding products.

[0361] A "terminal" refers to a device used by a user to input personal and emotional information and send it to a server.

[0362] A "database" refers to an information storage system that stores user and product information and allows it to be searched and retrieved as needed.

[0363] An "algorithm" refers to a set of computational procedures and rules used to recommend the most suitable products based on a user's personal and emotional information.

[0364] "Interface" refers to the screens and operating methods that users use to interact with a system, and includes GUIs (Graphical User Interfaces) used for selecting products and proceeding with purchase procedures.

[0365] "Recommended products" refer to products that the system recommends based on the analysis of the user's personal information and emotional data.

[0366] ---

[0367] This invention aims to improve the user's purchasing experience by providing a system that helps users select the most suitable products for them, and by combining it with an emotion engine that recognizes the user's emotions. This system supports users from product recommendations to the purchase process based on personal information, preferences, and emotional states provided by the user.

[0368] Input of user information and sentiment information

[0369] The user launches the application and selects the product category they wish to purchase. They then enter personal information such as age, height, weight, preferred design and color, and intended use date. Furthermore, the user provides real-time emotional states to the system using a camera and microphone. Facial recognition software and speech recognition software are used to input emotional states. Specific software examples include general facial recognition APIs for facial recognition and speech-to-text APIs for speech recognition.

[0370] Information transmission

[0371] The terminal transmits the entered user information and sentiment information to the server using a secure communication protocol. Before transmission, the information is encrypted using protocols such as TLS to ensure the security of the data during transmission.

[0372] Information reception and analysis

[0373] The server receives user information and sentiment information sent from the terminal and stores it in a database. Next, it analyzes this information using an AI engine. Specifically, machine learning platforms and models are used as the AI ​​engine. It analyzes the user's personal information and sentiment state and extracts parameters to recommend the most suitable products based on that analysis.

[0374] Generate product recommendations

[0375] The server uses the analysis results to search for the most suitable product from its product database. This search utilizes machine learning algorithms and filtering techniques. For example, a recommendation engine is used to list products that best suit the user's preferences and emotional state.

[0376] Sending and displaying recommended products

[0377] The server sends the generated list of recommended products to the user's terminal, which receives the list and displays it in the user interface. The user interface is implemented using a graphical user interface framework. This display includes product images, prices, features, and other users' ratings.

[0378] Product selection and purchase

[0379] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. A screen then appears to proceed with the purchase. This screen includes fields for entering payment method and shipping address. A payment service API is used for payment processing.

[0380] Submission and confirmation of purchase information

[0381] Once a user completes the purchase process, the purchase information is sent from the device to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date. Notifications are sent via email or in-app notifications.

[0382] Specific example

[0383] Below are specific scenarios and examples of input prompts for the generated AI model, designed to help visualize how this system works.

[0384] Example Scenario

[0385] User A has decided to go snowboarding this weekend and wants to buy a new snowboard. User A has decided to use the system to purchase a snowboard.

[0386] 1. Person A launches the app and selects "Snowboarding".

[0387] 2. Enter the required personal information (height 170cm, weight 65kg, preferred colors are blue and black).

[0388] 3. Smile at the camera and tell the microphone, "I'm excited."

[0389] 4. The device sends this information to the server.

[0390] 5. The server analyzes the information and searches for the most suitable product.

[0391] 6. A list of recommended snowboards and boots with blue or black designs will be displayed on the device.

[0392] 7. Person A selects their favorite item, adds it to their cart, and confirms the purchase.

[0393] Input prompts for the generative AI model

[0394] "If a user launches the app to buy a snowboard, enters their height as 170cm, weight as 65kg, preferred colors as blue and black, smiles at the camera, and says into the microphone that they are excited, what products would be recommended?"

[0395] This system enables product recommendations that reflect user emotions in real time, which is expected to improve the purchasing experience. Users will be able to easily select the right product even without specialized knowledge.

[0396] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0397] ---

[0398] Step 1: Enter user information and sentiment information.

[0399] User: Launch the application and select the product category you wish to purchase. Next, enter personal information such as age, height, weight, preferred design and color, and intended use date. You will also input facial expressions and voice via camera and microphone to provide your current emotional state (e.g., joy, excitement, confusion, etc.).

[0400] Input: Age, height, weight, preferred design and color, planned date of use, facial expression, voice

[0401] Output: Personal and emotional data

[0402] Specific actions: If a user wants to buy a snowboard, they will enter information such as their height (170cm), weight (65kg), and preference for blue and black designs. They will also smile in front of the camera and say into the microphone, "I'm excited."

[0403] Step 2: Sending Information

[0404] Terminal: Sends entered personal and emotional information to the server using a secure communication protocol (e.g., HTTPS). The information is encrypted before transmission.

[0405] Input: Personal information and emotional information

[0406] Output: Data packets containing encrypted personal and emotional information

[0407] Specific operation: The entered information is encrypted using the TLS protocol and sent to the server via HTTPS.

[0408] Step 3: Information reception and analysis

[0409] Server: Receives personal and emotional information sent from terminals and stores it in a database. Then, an AI engine is used to analyze this data.

[0410] Input: Encrypted personal information and emotional information

[0411] Output: Data showing the analyzed user's preferences and emotional state.

[0412] Specific operation: Received information is stored in a database, and an AI engine (e.g., a machine learning platform) is used to analyze the user's height, weight, age, preferred designs and colors, and emotional state.

[0413] Step 4: Generating Product Recommendations

[0414] Server: Based on the analysis results, it searches the database for the most suitable product. It uses machine learning algorithms and filtering techniques.

[0415] Input: Analyzed user preferences and emotional state

[0416] Output: List of recommended products

[0417] Specific operation: Use a recommendation engine to list the most suitable products, taking into account the user's age, preferences, and emotional state. For example, select products with brightly colored designs for an excited user.

[0418] Step 5: Submit and display recommended products

[0419] Server and Terminal: The server sends the generated list of recommended products to the terminal, and the terminal displays the received list in the user interface.

[0420] Input: List of recommended contests

[0421] Output: Product information displayed to the user

[0422] Specific operation: The list includes product images, prices, features, and other users' ratings, and is displayed on the user's device.

[0423] Step 6: Product Selection and Purchase

[0424] User: Select the product you want to purchase from the list of recommended products and click the "Add to Cart" button. Then, proceed with the purchase process, enter your payment method and shipping address, and confirm your purchase.

[0425] Input: Selected product information, payment information, shipping address information

[0426] Output: Final purchase information

[0427] Specific action: The user adds a blue snowboard and matching boots to their cart, enters payment information and shipping address, and confirms the purchase.

[0428] Step 7: Submit and confirm purchase information

[0429] Terminal and Server: Purchase information is sent from the terminal to the server, which checks inventory and confirms the purchase. The user is notified of the purchase completion and the estimated shipping date.

[0430] Input: Purchase Information

[0431] Output: Purchase confirmation information, estimated shipping date

[0432] Specific operation: The server checks inventory and sends a notification along with purchase confirmation. The user is notified of the expected shipping date via email or in-app notification.

[0433] ---

[0434] This system's processing steps enable product recommendations based on personal and emotional information, thereby improving the user experience.

[0435] (Application Example 2)

[0436] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0437] Current online shopping systems commonly recommend products based on users' personal information and purchase history. However, these systems recommend products based on uniform criteria without considering the user's emotional state, resulting in a limited user experience. There is a problem in providing products that users are actually interested in or that match their mood at that moment. This can increase the effort required for users to find what they truly want, potentially diminishing their desire to purchase.

[0438] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing personal information and emotional information entered by the user, means including a generative AI model that searches a database based on the analysis results and recommends the most suitable product, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide personalized product recommendations that take into account the user's emotional state.

[0439] "User personal information" refers to information that can be used to identify or distinguish a user, such as age, height, weight, preferred design and color, and planned usage date.

[0440] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) based on the user's voice, facial expressions, and actions, and is collected in real time.

[0441] A "server" is a centralized computing system that receives and analyzes information sent by users and recommends products.

[0442] "Means of analysis" refers to the process by which the server uses an analysis algorithm to decipher the user's personal information and emotional information received, and selects the most suitable product for the user.

[0443] A "generative AI model" is a computational model that uses machine learning techniques to recommend the most suitable products based on a user's personal information and emotional data.

[0444] A "user terminal" refers to an electronic device, such as a smartphone, tablet, or PC, that a user uses to communicate with a server.

[0445] "Recommended methods" refer to a mechanism that uses analysis results and generated AI models to appropriately select products based on user information.

[0446] "Means of transmission" refers to the communication protocols and technologies used to deliver product information analyzed and selected by the server to the user's terminal.

[0447] Modes for carrying out the invention

[0448] This invention relates to a system that recommends optimal products based on personal information and emotional information entered by the user. Specifically, it aims to improve the purchasing experience by analyzing the user's emotional state in real time and providing personalized product recommendations based on the user's current mood and preferences. The following describes in detail a specific embodiment of this system.

[0449] composition

[0450] hardware

[0451] User terminal: An electronic device such as a smartphone, tablet, or PC that a user uses to input information and communicate with a server.

[0452] Server: A centralized computing system that receives, analyzes, and recommends products based on information sent from user terminals.

[0453] software

[0454] Frontend: We will utilize React Native (a mobile application development framework) to provide an interface that makes it easy for users to input information.

[0455] Backend: Node.js (server-side environment), Express (web framework), MongoDB (database), and TensorFlow.js (machine learning library) are used to analyze incoming data.

[0456] Operation overview

[0457] Collection of user information and sentiment information

[0458] Users launch the application using their smartphones or tablets and input their personal information, preferred designs, colors, and intended use, as well as real-time facial expressions and voice recordings via the camera and microphone. This allows the system to collect data on the user's emotional state.

[0459] Information transmission

[0460] Personal and emotional information transmitted from the user's terminal is securely sent to the server via the HTTPS protocol. This ensures the security of the data during transmission.

[0461] Information analysis

[0462] The server uses TensorFlow.js to analyze the personal and emotional information of the users it receives. The analyzed emotional data represents the user's emotional state (e.g., joy, excitement, confusion) as numerical data.

[0463] Generate product recommendations

[0464] Based on the analysis results, the server searches the MongoDB database for products that match the user's preferences and emotional state. Using a generative AI model, the most suitable products are recommended.

[0465] Sending and displaying recommended products

[0466] The server generates a list of recommended products, which is then sent to the user's terminal. The user's terminal then displays images, prices, features, and user reviews of the recommended products.

[0467] Product selection and purchase

[0468] The user selects the desired product from the displayed recommended items, adds it to their cart, and proceeds with the purchase. Following the user interface, they enter their payment method and shipping address and confirm the purchase.

[0469] Specific example

[0470] For example, if a user wants to buy a "snowboard," they might enter the following information:

[0471] Example of a prompt

[0472] Age: 28

[0473] Height: 170cm

[0474] Weight: 65kg

[0475] Favorite colors: Blue, Black

[0476] Preferred design: Simple

[0477] Usage: Snowboarding

[0478] Emotional information: Joy, Trust level 90%

[0479] Based on this input information, the system analyzes the user's emotional state and recommends snowboards with bright colors and stylish designs, taking into account that the user is experiencing "joy." In this way, personalized product recommendations that reflect the user's emotional state are achieved.

[0480] The above describes the detailed configuration for carrying out the invention. This system allows users to select the optimal product without specialized knowledge and obtain a seamless purchasing experience.

[0481] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0482] Program processing flow

[0483] Step 1:

[0484] Users launch the application using their smartphones or tablets and enter personal and emotional information. They enter their age, height, weight, preferred design and color, and intended use into a form. Simultaneously, they use their camera and microphone to input real-time facial expressions and voice into the application. This entered information is then passed directly to the next processing step.

[0485] Step 2:

[0486] The user terminal transmits entered personal and emotional information to the server using the HTTPS protocol. Before transmission, this data is encrypted to ensure the security of the data during transmission. Input data consists of personal and emotional information, and output data is encrypted.

[0487] Step 3:

[0488] The server analyzes personal and emotional information received from the user's terminal. The received data is fed into an analysis algorithm to evaluate the user's height, weight, preferences, and emotional state. Emotional analysis is performed using TensorFlow.js, extracting the type of emotion (e.g., joy, excitement, confusion) and its confidence level as numerical values. The input data consists of received personal and emotional information, while the output data is the analysis results (numerical data of the user's preferences and emotional state).

[0489] Step 4:

[0490] The server performs a database search based on the analysis results. It queries the MongoDB database to find products that match the user's preferences and emotional state. Using a generative AI model, it further filters the search results to identify the most suitable products. The input data consists of the analysis results and product data in the database, and the output data is a list of recommended products.

[0491] Step 5:

[0492] The server generates a list of optimal products and sends it back to the user's terminal via HTTPS. The data sent is a list of recommended products, and the user's terminal receives this data.

[0493] Step 6:

[0494] The user terminal analyzes the received list of recommended products and displays it on the user interface. It displays product images, prices, features, and other users' ratings to make selection easier for the user. The input data is the received list of recommended products, and the output data is the product information displayed on the screen.

[0495] Step 7:

[0496] The user selects the desired product from the recommended products displayed on the screen and adds it to their cart. They then proceed to the checkout screen, where they enter their payment method and shipping address, and click the "Confirm Purchase" button. The input data consists of the user's selected products and purchase information, while the output data is the purchase confirmation information sent to the server.

[0497] Step 8:

[0498] The server receives information that the user has completed the purchase process and checks the inventory. If inventory is available, it confirms the purchase and begins preparing for shipment. The user is notified that the purchase is complete and the estimated shipping date. The input data is the confirmed purchase information, and the output data is the purchase confirmation and estimated shipping date notification.

[0499] In this way, personalized product recommendations and a seamless purchasing experience are realized based on the user's personal and emotional information.

[0500] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0501] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0502] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0503] [Second Embodiment]

[0504] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0505] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0506] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0507] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0508] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0509] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0510] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0511] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0512] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0513] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0514] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0515] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0516] ---

[0517] This invention is a system designed to help users select appropriate products and increase their purchasing intent. The system includes a process where it recommends products based on personal information and preferences provided by the user, and the user then selects and purchases a product based on these recommendations.

[0518] Specifically, this system is configured as follows:

[0519] Entering user information

[0520] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user wants to purchase a snowboard, they would enter information such as height 170cm, weight 65kg, and preference for blue and black designs.

[0521] Submitting the entered information

[0522] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS). The input information is encrypted before transmission, ensuring the security of the data during transmission.

[0523] Receiving and analyzing user information

[0524] Server: The server receives user input information sent from the terminal and begins analysis. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[0525] Generate product recommendations

[0526] Server: The server searches the database for corresponding products based on the analyzed user information. It uses machine learning algorithms and filtering techniques to list the products best suited to the user's needs. For example, it might generate a list recommending the optimal snowboard and boots based on the user's height and weight.

[0527] Sending and displaying recommended products

[0528] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[0529] Product selection and purchase

[0530] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[0531] Submission and confirmation of purchase information

[0532] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[0533] This system makes it easier for users to select the right products, even without specialized knowledge. Furthermore, personalized recommendations for each user increase their purchasing intent and enable more efficient online shopping.

[0534] ---

[0535] The following describes the processing flow.

[0536] ---

[0537] Step 1:

[0538] User: The user launches the app and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use.

[0539] Specific actions:

[0540] Launch the app and select a product category (for example, snowboards).

[0541] Enter your age (e.g., 25 years old), height (e.g., 170cm), weight (e.g., 65kg), preferences (e.g., blue or black design), and planned date of use in the input form.

[0542] Step 2:

[0543] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS).

[0544] Specific actions:

[0545] After completing the input, the user presses the "Next" or "Submit" button.

[0546] The input information is encrypted and sent to the server.

[0547] Step 3:

[0548] Server: Receives and analyzes user input information sent from the terminal.

[0549] Specific actions:

[0550] The system decodes the received data and analyzes information such as age, height, weight, preferred design and color, and intended use.

[0551] Based on this information, we extract parameters to narrow down the recommended products.

[0552] Step 4:

[0553] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products.

[0554] Specific actions:

[0555] A query is sent to the database to search for products that match the user's parameters.

[0556] Evaluate search results and create a list to recommend the best products to the user.

[0557] Step 5:

[0558] Server: Sends the generated list of recommended products to the terminal.

[0559] Specific actions:

[0560] Convert the recommended product list into JSON format and send it to the user's terminal.

[0561] The list includes product images, prices, features, and other users' ratings.

[0562] Step 6:

[0563] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[0564] Specific actions:

[0565] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[0566] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[0567] Step 7:

[0568] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[0569] Specific actions:

[0570] The user taps on the product to view details and then presses the "Add to Cart" button.

[0571] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[0572] Step 8:

[0573] Terminal: When a user completes the purchase process, it sends that information to the server.

[0574] Specific actions:

[0575] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[0576] Step 9:

[0577] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[0578] Specific actions:

[0579] It integrates with the inventory system to check the stock of the selected product.

[0580] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[0581] We will notify you of information such as the expected shipping date and tracking number.

[0582] ---

[0583] The above outlines the specific processing steps of this system. This process allows users to easily select the most suitable product and proceed with the purchase smoothly.

[0584] (Example 1)

[0585] Next, we will describe Example 1. 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."

[0586] Traditional online shopping systems often made it difficult for users to find suitable products from a large amount of information, leading to decreased purchasing intent. Furthermore, there were security concerns, as users' personal information was sometimes not protected. Additionally, the lack of appropriate recommendation algorithms meant that accurate product recommendations were not possible.

[0587] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0588] In this invention, the server includes means for encrypting user information and transmitting it to the server using a secure communication protocol, means for the server to analyze the user information and recommend corresponding products, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide accurate product recommendations based on user preferences while ensuring the security of user information.

[0589] "User information" refers to personal data that users enter into the system, such as age, height, weight, preferred design and color, and intended use.

[0590] "Encryption" is the process of transforming data according to a specific algorithm in order to ensure the security of data during transmission.

[0591] A "secure communication protocol" is a protocol used to ensure the confidentiality and integrity of data during data communication over the internet, and HTTPS is a representative example.

[0592] A "server" is a computer system that receives data sent by a user, performs various processing steps, and returns the results to the user.

[0593] "Analysis" is the process of examining user information using specific algorithms and methods to extract meaningful data and parameters.

[0594] A "machine learning algorithm" is an algorithm that performs predictions and classifications based on data, and in this context, it is a method used to recommend the most suitable product to a user.

[0595] The "Recommended Products List" is a list of products selected based on user information, tailored to the user's preferences and needs.

[0596] A "user interface" is a means for a user to interact with a computer or system, and in this context, it refers to a screen that displays product information or the progress of the purchase process.

[0597] The "Add to Cart" function allows users to temporarily reserve items they are considering purchasing.

[0598] The "purchase process" refers to a series of operations in which the user enters the payment method and shipping address for the selected product and finally confirms the purchase.

[0599] This invention is a system that helps users select appropriate products and improves their purchasing intent. The system includes a process in which products are recommended based on personal information and preferences provided by the user, and the user selects a product based on these recommendations and proceeds with the purchase.

[0600] Hardware and software to be used

[0601] User terminal: A device used by the user to enter information and view a list of recommended products (e.g., smartphone, tablet, PC).

[0602] Server: A computer system used for receiving, analyzing, recommending products, and transmitting results.

[0603] Software libraries and frameworks:

[0604] Data analysis: Python's pandas and numpy.

[0605] Machine learning algorithm: Scikit-learn.

[0606] Encryption: AES (Advanced Encryption Standard).

[0607] Communication protocol: HTTPS.

[0608] Frontend: React, Vue.js.

[0609] Entering user information

[0610] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user is 170cm tall, weighs 65kg, likes blue and black designs, and wants to purchase a freestyle snowboard, they would enter that information into the input form.

[0611] Submitting the entered information

[0612] Terminal: The terminal encrypts the collected user information and sends it to the server using HTTPS, a secure communication protocol. The encryption technology used is AES, which ensures the security of the data during transmission.

[0613] Receiving and analyzing user information

[0614] Server: The server receives encrypted data sent from the terminal and decrypts it. Next, it analyzes the received data using Python's pandas and numpy libraries to extract parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[0615] Generate product recommendations

[0616] Server: Based on the analyzed user information, the server searches the database for relevant products. The search uses machine learning algorithms (collaborative filtering and content-based filtering utilizing Scikit-learn) to list the products best suited to the user's needs. For example, it might add the optimal snowboard and boots for the user's height and weight to the recommendation list.

[0617] Sending and displaying recommended products

[0618] Server and Terminal: The server converts the generated recommended product list into JSON format and sends it back to the terminal. The terminal parses the received list using a JavaScript framework such as React or Vue.js and displays it in the user interface. The recommended product list includes product images, prices, features, and reviews from other users. For example, it might display images and detailed information of a blue and black snowboard and boots in the appropriate size.

[0619] Product selection and purchase

[0620] User: The user selects the product they wish to purchase from the displayed list of recommended products. They add the product to their cart by clicking the "Add to Cart" button. Then, they follow the on-screen instructions to proceed with the purchase, entering their payment method and shipping information. For example, a user adds a blue snowboard and boots of the appropriate size to their cart, enters their credit card information and shipping address, and confirms the purchase.

[0621] Submission and confirmation of purchase information

[0622] Terminal and Server: Once a user completes the purchase process, the terminal sends the purchase information to the server. The server stores the received purchase information in its database and checks the product's inventory. It then verifies the purchase and sends an email to the user notifying them that the purchase is complete and providing the estimated shipping date. As a specific example, a "Purchase Completion Notification" email is sent to the user's email address, providing the estimated shipping date and tracking number.

[0623] Examples of specific cases and prompt statements

[0624] Specific example

[0625] 1. The user launches the app and expresses a desire to purchase a snowboard.

[0626] 2. The user enters the following information into the input form:

[0627] Age: 25

[0628] Height: 170cm

[0629] Weight: 65kg

[0630] Preferred design: Blue or black

[0631] Usage: Freestyle

[0632] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[0633] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[0634] 5. The recommendation list is sent to the device and displayed in the user interface.

[0635] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[0636] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[0637] Example of a prompt

[0638] For example, the prompt text to be input to the generative AI model would be as follows:

[0639] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1: Enter user information

[0642] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use. For example, a user might enter "Height 170cm, Weight 65kg, Likes blue and black designs" into the input form.

[0643] Input: User's personal information (age, height, weight, preferred design, intended use).

[0644] Output: User input data.

[0645] Step 2: Encrypt and send the entered information.

[0646] Terminal: The terminal encrypts the user's input information using AES (Advanced Encryption Standard). It then sends this encrypted data to the server using HTTPS, a secure communication protocol.

[0647] Input: User input data.

[0648] Data processing: Encryption using AES.

[0649] Output: Encrypted user information data.

[0650] Step 3: Receiving and decrypting user information

[0651] Server: The server receives encrypted data sent from the terminal. Next, it decrypts the received data. This decryption uses a key that was shared in advance.

[0652] Input: Encrypted user information data.

[0653] Data processing: Decryption of encrypted data.

[0654] Output: Decoded user information data.

[0655] Step 4: Analyze user information

[0656] Server: Analyzes the decrypted user information. Using Python's pandas and numpy libraries, the server extracts parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[0657] Input: Decrypted user information data.

[0658] Data processing: Data analysis and parameter extraction (pandas, numpy).

[0659] Output: Parameters for recommendation.

[0660] Step 5: Search for products and generate a recommendation list

[0661] Server: Uses machine learning algorithms (e.g., Scikit-learn) to search a database based on user parameters. Generates a list of recommended products. For example, it might list snowboards and boots that match the user's height and weight.

[0662] Input: Parameters for recommendation.

[0663] Data processing: Search and list generation using machine learning algorithms.

[0664] Output: Recommended product list.

[0665] Step 6: Submit your recommended product

[0666] Server: Converts the generated list of recommended products into JSON format and sends it to the user's terminal.

[0667] Input: Recommended product list.

[0668] Data processing: Conversion to JSON format.

[0669] Output: A list of recommended products in JSON format.

[0670] Step 7: Display recommended products

[0671] Terminal: The terminal parses the received list of recommended products and displays it in the user interface. This display uses React or Vue.js. The displayed list includes product images, prices, features, and reviews from other users.

[0672] Input: A list of recommended products in JSON format.

[0673] Data processing: Parsing and displaying lists.

[0674] Output: Recommended product list on the user interface.

[0675] Step 8: Product Selection and Purchase Procedure

[0676] User: The user selects the product they wish to purchase from the displayed recommended products and clicks the "Add to Cart" button. They then enter their payment method and shipping information and proceed with the purchase.

[0677] Input: User's desired purchase items and payment information.

[0678] Data processing: Proceeding with the purchase procedure.

[0679] Output: Purchase confirmation data.

[0680] Step 9: Submit and confirm purchase information

[0681] Terminal and Server: The terminal sends purchase confirmation data to the server. The server stores the received purchase information in its database and checks inventory. After that, it verifies the purchase and notifies the user of the purchase completion and estimated shipping date.

[0682] Input: Purchase confirmation data.

[0683] Data processing: Database storage and inventory checks.

[0684] Output: Purchase completion notification and estimated shipping date.

[0685] The above describes the specific program processing flow in this system.

[0686] Examples of specific cases and prompt statements

[0687] Specific example

[0688] 1. The user launches the app and expresses a desire to purchase a snowboard.

[0689] 2. The user enters the following information into the input form:

[0690] Age: 25

[0691] Height: 170cm

[0692] Weight: 65kg

[0693] Preferred design: Blue or black

[0694] Usage: Freestyle

[0695] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[0696] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[0697] 5. The recommendation list is sent to the device and displayed in the user interface.

[0698] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[0699] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[0700] Example of a prompt

[0701] For example, the prompt text to be input to the generative AI model would be as follows:

[0702] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[0703] (Application Example 1)

[0704] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0705] The aim is to help users who are unsure which products to choose, enabling them to select appropriate products even without specialized knowledge, thereby increasing their purchasing intent. Furthermore, the goal is to improve the user experience by providing personalized recommendations to users during online shopping.

[0706] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0707] In this invention, the server includes means for securely transmitting user input information via the HTTPS protocol, means for recommending the most suitable products from a database using machine learning algorithms, and means for searching for products using prompt sentences with a generative AI model. This allows users to easily find suitable products and enhance their purchasing intent through a personalized experience.

[0708] A "user terminal" refers to a device used by a user, such as a smartphone, tablet, or computer.

[0709] The HTTPS protocol is a secure communication protocol for safely sending and receiving data over the internet.

[0710] A "machine learning algorithm" is a method that analyzes data, recognizes patterns, and derives relevant results.

[0711] A "database" is a system for organizing and managing collections of data, and it stores user information and product information.

[0712] A "generative AI model" is a model used to perform specific tasks using artificial intelligence, making predictions and recommendations based on input data.

[0713] A "prompt message" is text input to a generative AI model, and its role is to give the model instructions for performing a specific task.

[0714] A "recommended product" is a list of products that the server selects and provides based on user information analysis, ensuring the product best suits the user's needs.

[0715] An "interface" is a means by which a user and a system can interact and communicate with each other, and includes graphical user interfaces (GUIs) and command-line interfaces (CLIs).

[0716] This invention is a system that recommends optimal products based on a user's personal information and preferences, thereby increasing their willingness to purchase. The system includes a user terminal, a secure communication protocol, a server, a machine learning algorithm, a generative AI model, and a database.

[0717] Entering user information

[0718] The user launches the application using their smartphone and selects the product category they wish to purchase. Next, they enter information such as age, height, weight, preferred color and design, and intended use into an input form. For example, if a user wants to purchase outdoor equipment, they would enter information such as age 30, height 175cm, weight 70kg, preference for blue, and camping equipment.

[0719] Submitting the entered information

[0720] The user terminal securely transmits the collected input information to the server using the HTTPS protocol. The input information is encrypted before transmission, ensuring the security of the data during communication.

[0721] Receiving and analyzing user information

[0722] The server receives user input information sent from the terminal and begins analysis using a machine learning algorithm. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[0723] Generate product recommendations

[0724] The server searches the database for corresponding products based on the analyzed user information. The search uses a generative AI model and prompt text to list the products best suited to the user's needs. For example, it might generate a list recommending a blue outdoor jacket, a suitable tent, and a sleeping mat.

[0725] Sending and displaying recommended products

[0726] The server sends the generated list of recommended products to the user's terminal, which then analyzes the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings.

[0727] Product selection and purchase

[0728] The user selects the items they wish to purchase from the list of recommended products and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue outdoor jacket and a matching tent to their cart, enter their payment method and shipping address, and then confirm the purchase.

[0729] Submission and confirmation of purchase information

[0730] Once the user completes the purchase process, the purchase information is sent from the device to the server. The server checks the inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[0731] Hardware and software to use

[0732] Smartphone: A device used by users to input information and view results.

[0733] Server: Uses Python, TensorFlow, and Firebase to perform data analysis and recommendation algorithms.

[0734] Communication protocol: Uses HTTPS to securely send and receive data.

[0735] Database: User and product information is managed using Firebase.

[0736] Examples of prompt statements

[0737] Please recommend outdoor gear for camping, for a user who is 175cm tall, weighs 70kg, likes the color blue, and is looking for products suitable for camping.

[0738] As a result, users can easily find the right products, and their purchasing intent can be increased through a personalized experience.

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] The user launches the application using their smartphone. The user selects the product category they wish to purchase and enters information such as age, height, weight, preferred design and color, and intended use into the input form. This data is user information and is used for the next step.

[0742] Step 2:

[0743] The terminal encrypts the information entered by the user using the HTTPS protocol and securely sends it to the server. Specifically, the application converts the input data into JSON format and sends it to the server using an HTTPS POST request. The output of this step is encrypted user information.

[0744] Step 3:

[0745] The server receives user input information sent from the terminal. It parses the received data and extracts parameters from the JSON format. The data used here is user information, and the parsing results become the input for the next step.

[0746] Step 4:

[0747] The server uses Python and TensorFlow to apply machine learning algorithms and calculate parameters for recommending the most suitable products based on user input. Specifically, it analyzes information such as the user's height, weight, age, preferred design and color, and intended use to extract product characteristics. The output of this step is the characteristic information of the recommended products.

[0748] Step 5:

[0749] The server searches for corresponding products in its database (Firebase) based on the analyzed user information and generates prompt messages using a generative AI model. Specifically, it uses the prompt message to instruct the AI ​​model to search the database and generate an optimal product list. For example, it might generate a prompt message such as, "The user is 175cm tall, weighs 70kg, likes the color blue, and would like recommendations for outdoor equipment to use for camping." The output of this step is a list of recommended products.

[0750] Step 6:

[0751] The server sends the generated list of recommended products to the terminal. Specifically, it converts the generated product list into JSON format and sends it to the terminal using an HTTPS POST request. The output of this step is the list information of recommended products.

[0752] Step 7:

[0753] The terminal analyzes the received list of recommended products and displays it on the user interface. Specifically, it analyzes the JSON data to display product images, prices, features, and other users' ratings. The output of this step is the recommended product information displayed to the user.

[0754] Step 8:

[0755] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. Next, they proceed with the purchase process following the on-screen instructions. Specifically, this involves adding the desired items to the cart and entering payment and shipping information. The input in this step is the user's purchase preferences and payment information, while the output is information about the items added to the cart.

[0756] Step 9:

[0757] The terminal sends purchase information to the server once the user completes the purchase process. Specifically, it converts the purchase information into JSON format and sends it to the server using an HTTPS POST request. The output of this step is the sent purchase information.

[0758] Step 10:

[0759] The server checks inventory based on the received purchase information and confirms the purchase. Next, it notifies the user that the purchase is complete and the expected shipping date. Specifically, it uses the inventory management system to check the inventory status and sends a purchase confirmation email to the user. The output of this step is the purchase confirmation and the notification of the expected shipping date.

[0760] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0761] ---

[0762] This invention aims to improve the user's purchasing experience by providing a system that helps users select the most suitable products for them, and by combining it with an emotion engine that recognizes the user's emotions. This system supports users from product recommendations to the purchase process based on personal information, preferences, and emotional states provided by the user.

[0763] Specifically, this system is configured as follows:

[0764] Input of user information and sentiment information

[0765] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use date. The user also provides the system with their current emotional state (e.g., joy, excitement, confusion) through voice and facial expressions. For example, if a user wants to buy a snowboard, they would enter information such as height 170cm, weight 65kg, and preference for blue and black designs. Furthermore, their facial expressions and voice would be collected in real-time by the emotion engine via camera and microphone.

[0766] Information transmission

[0767] Terminal: User input information and sentiment information are sent to the server using a secure communication protocol (e.g., HTTPS). User information and sentiment information are encrypted before transmission, ensuring the security of the data during transmission.

[0768] Information reception and analysis

[0769] Server: The server receives user information and emotional information sent from the terminal and begins analysis. The analysis evaluates the user's height, weight, age, preferred design and color, intended use, and emotional state, and extracts parameters to recommend the most suitable product based on this information. For example, if the user is excited, the server will select recommended products that reflect that emotion.

[0770] Generate product recommendations

[0771] Server: Based on the analysis results, it searches the database for corresponding products. The search uses machine learning algorithms and filtering techniques to list products that best suit the user's needs and emotional state. For example, if the user is excited, it might focus on recommending colorful and stylish snowboards and boots.

[0772] Sending and displaying recommended products

[0773] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[0774] Product selection and purchase

[0775] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[0776] Submission and confirmation of purchase information

[0777] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date.

[0778] This invention enables product recommendations that reflect user emotions in real time, thereby increasing user purchasing intent. Furthermore, users can select appropriate products without requiring specialized knowledge, providing a seamless online shopping experience.

[0779] ---

[0780] The following describes the processing flow.

[0781] ---

[0782] Step 1:

[0783] User: The user launches the app and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use. Additionally, they provide facial expressions and voice to the system in real time via camera and microphone, allowing the emotion engine to recognize the user's emotional state.

[0784] Specific actions:

[0785] Launch the app and select a product category (for example, snowboards).

[0786] Enter your age (e.g., 25 years old), height (e.g., 170cm), weight (e.g., 65kg), preferences (e.g., blue or black design), and planned date of use in the input form.

[0787] Enable the camera and microphone to send facial expressions and voices to the emotion engine in real time.

[0788] Step 2:

[0789] Terminal: Sends user input information and sentiment information to the server using a secure communication protocol (e.g., HTTPS).

[0790] Specific actions:

[0791] After entering the information, press the "Next" or "Submit" button.

[0792] Input information and emotional information are encrypted and sent to the server.

[0793] Step 3:

[0794] Server: Receives user information and sentiment information sent from the terminal and begins analysis.

[0795] Specific actions:

[0796] The system decodes the received data and analyzes it to determine age, height, weight, preferred designs and colors, intended use, emotional state, and more.

[0797] Based on this information, we extract the parameters used to search for recommended products.

[0798] Step 4:

[0799] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products that reflect the emotional state.

[0800] Specific actions:

[0801] A query is sent to the database to search for products that match the user's parameters and emotional state.

[0802] We generate the optimal recommended product list, taking into account product popularity and ratings.

[0803] For example, if a user is excited, we might recommend products with vibrant designs.

[0804] Step 5:

[0805] Server: Sends the generated list of recommended products to the terminal.

[0806] Specific actions:

[0807] Convert the recommended product list into JSON format and send it to the user's terminal.

[0808] Recommended products include product images, prices, features, and reviews from other users.

[0809] Step 6:

[0810] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[0811] Specific actions:

[0812] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[0813] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[0814] Step 7:

[0815] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[0816] Specific actions:

[0817] The user taps on the product to view details and then presses the "Add to Cart" button.

[0818] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[0819] Step 8:

[0820] Terminal: When a user completes the purchase process, it sends that information to the server.

[0821] Specific actions:

[0822] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[0823] Step 9:

[0824] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[0825] Specific actions:

[0826] It integrates with the inventory system to check the stock of the selected product.

[0827] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[0828] We will notify you of information such as the expected shipping date and tracking number.

[0829] ---

[0830] The above outlines the specific processing steps of the system that incorporates the emotion engine. This process allows users to select the optimal product according to their emotional state and proceed with the purchase smoothly.

[0831] (Example 2)

[0832] Next, we will describe Example 2. 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".

[0833] Traditional online shopping systems mostly recommended products based on users' personal information, without considering their emotional state. Therefore, they were unable to provide product recommendations that reflected users' real-time emotional states, failing to adequately enhance user satisfaction and purchasing intent. Furthermore, traditional systems lacked the means to assist users in selecting the optimal product, even if they lacked specialized knowledge.

[0834] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input personal information and emotional information, means for transmitting the user's personal information and emotional information to the server, and means for the server to analyze the user's personal information and emotional information and recommend corresponding products. This makes it possible to recommend products that take into account not only the user's personal information but also real-time emotional information. This is expected to improve user satisfaction and purchasing intent, and enable users to choose appropriate products even without specialized knowledge.

[0835] ---

[0836] A "user" refers to an individual or group that uses this system to search for, select, and purchase products.

[0837] "Personal information" refers to information that can identify a user, such as the user's age, height, weight, preferred design and color, and planned usage date.

[0838] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) obtained by analyzing the user's facial expressions, voice, etc.

[0839] A "server" refers to a central processing unit that receives and analyzes users' personal and emotional information and recommends corresponding products.

[0840] A "terminal" refers to a device used by a user to input personal and emotional information and send it to a server.

[0841] A "database" refers to an information storage system that stores user and product information and allows it to be searched and retrieved as needed.

[0842] An "algorithm" refers to a set of computational procedures and rules used to recommend the most suitable products based on a user's personal and emotional information.

[0843] "Interface" refers to the screens and operating methods that users use to interact with a system, and includes GUIs (Graphical User Interfaces) used for selecting products and proceeding with purchase procedures.

[0844] "Recommended products" refer to products that the system recommends based on the analysis of the user's personal information and emotional data.

[0845] ---

[0846] This invention aims to improve the user's purchasing experience by providing a system that helps users select the most suitable products for them, and by combining it with an emotion engine that recognizes the user's emotions. This system supports users from product recommendations to the purchase process based on personal information, preferences, and emotional states provided by the user.

[0847] Input of user information and sentiment information

[0848] The user launches the application and selects the product category they wish to purchase. They then enter personal information such as age, height, weight, preferred design and color, and intended use date. Furthermore, the user provides real-time emotional states to the system using a camera and microphone. Facial recognition software and speech recognition software are used to input emotional states. Specific software examples include general facial recognition APIs for facial recognition and speech-to-text APIs for speech recognition.

[0849] Information transmission

[0850] The terminal transmits the entered user information and sentiment information to the server using a secure communication protocol. Before transmission, the information is encrypted using protocols such as TLS to ensure the security of the data during transmission.

[0851] Information reception and analysis

[0852] The server receives user information and sentiment information sent from the terminal and stores it in a database. Next, it analyzes this information using an AI engine. Specifically, machine learning platforms and models are used as the AI ​​engine. It analyzes the user's personal information and sentiment state and extracts parameters to recommend the most suitable products based on that analysis.

[0853] Generate product recommendations

[0854] The server uses the analysis results to search for the most suitable product from its product database. This search utilizes machine learning algorithms and filtering techniques. For example, a recommendation engine is used to list products that best suit the user's preferences and emotional state.

[0855] Sending and displaying recommended products

[0856] The server sends the generated list of recommended products to the user's terminal, which receives the list and displays it in the user interface. The user interface is implemented using a graphical user interface framework. This display includes product images, prices, features, and other users' ratings.

[0857] Product selection and purchase

[0858] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. A screen then appears to proceed with the purchase. This screen includes fields for entering payment method and shipping address. A payment service API is used for payment processing.

[0859] Submission and confirmation of purchase information

[0860] Once a user completes the purchase process, the purchase information is sent from the device to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date. Notifications are sent via email or in-app notifications.

[0861] Specific example

[0862] Below are specific scenarios and examples of input prompts for the generated AI model, designed to help visualize how this system works.

[0863] Example Scenario

[0864] User A has decided to go snowboarding this weekend and wants to buy a new snowboard. User A has decided to use the system to purchase a snowboard.

[0865] 1. Person A launches the app and selects "Snowboarding".

[0866] 2. Enter the required personal information (height 170cm, weight 65kg, preferred colors are blue and black).

[0867] 3. Smile at the camera and tell the microphone, "I'm excited."

[0868] 4. The device sends this information to the server.

[0869] 5. The server analyzes the information and searches for the most suitable product.

[0870] 6. A list of recommended snowboards and boots with blue or black designs will be displayed on the device.

[0871] 7. Person A selects their favorite item, adds it to their cart, and confirms the purchase.

[0872] Input prompts for the generative AI model

[0873] "If a user launches the app to buy a snowboard, enters their height as 170cm, weight as 65kg, preferred colors as blue and black, smiles at the camera, and says into the microphone that they are excited, what products would be recommended?"

[0874] This system enables product recommendations that reflect user emotions in real time, which is expected to improve the purchasing experience. Users will be able to easily select the right product even without specialized knowledge.

[0875] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0876] ---

[0877] Step 1: Enter user information and sentiment information.

[0878] User: Launch the application and select the product category you wish to purchase. Next, enter personal information such as age, height, weight, preferred design and color, and intended use date. You will also input facial expressions and voice via camera and microphone to provide your current emotional state (e.g., joy, excitement, confusion, etc.).

[0879] Input: Age, height, weight, preferred design and color, planned date of use, facial expression, voice

[0880] Output: Personal and emotional data

[0881] Specific actions: If a user wants to buy a snowboard, they will enter information such as their height (170cm), weight (65kg), and preference for blue and black designs. They will also smile in front of the camera and say into the microphone, "I'm excited."

[0882] Step 2: Sending Information

[0883] Terminal: Sends entered personal and emotional information to the server using a secure communication protocol (e.g., HTTPS). The information is encrypted before transmission.

[0884] Input: Personal information and emotional information

[0885] Output: Data packets containing encrypted personal and emotional information

[0886] Specific operation: The entered information is encrypted using the TLS protocol and sent to the server via HTTPS.

[0887] Step 3: Information reception and analysis

[0888] Server: Receives personal and emotional information sent from terminals and stores it in a database. Then, an AI engine is used to analyze this data.

[0889] Input: Encrypted personal information and emotional information

[0890] Output: Data showing the analyzed user's preferences and emotional state.

[0891] Specific operation: Received information is stored in a database, and an AI engine (e.g., a machine learning platform) is used to analyze the user's height, weight, age, preferred designs and colors, and emotional state.

[0892] Step 4: Generating Product Recommendations

[0893] Server: Based on the analysis results, it searches the database for the most suitable product. It uses machine learning algorithms and filtering techniques.

[0894] Input: Analyzed user preferences and emotional state

[0895] Output: List of recommended products

[0896] Specific operation: Use a recommendation engine to list the most suitable products, taking into account the user's age, preferences, and emotional state. For example, select products with brightly colored designs for an excited user.

[0897] Step 5: Submit and display recommended products

[0898] Server and Terminal: The server sends the generated list of recommended products to the terminal, and the terminal displays the received list in the user interface.

[0899] Input: List of recommended contests

[0900] Output: Product information displayed to the user

[0901] Specific operation: The list includes product images, prices, features, and other users' ratings, and is displayed on the user's device.

[0902] Step 6: Product Selection and Purchase

[0903] User: Select the product you want to purchase from the list of recommended products and click the "Add to Cart" button. Then, proceed with the purchase process, enter your payment method and shipping address, and confirm your purchase.

[0904] Input: Selected product information, payment information, shipping address information

[0905] Output: Final purchase information

[0906] Specific action: The user adds a blue snowboard and matching boots to their cart, enters payment information and shipping address, and confirms the purchase.

[0907] Step 7: Submit and confirm purchase information

[0908] Terminal and Server: Purchase information is sent from the terminal to the server, which checks inventory and confirms the purchase. The user is notified of the purchase completion and the estimated shipping date.

[0909] Input: Purchase Information

[0910] Output: Purchase confirmation information, estimated shipping date

[0911] Specific operation: The server checks inventory and sends a notification along with purchase confirmation. The user is notified of the expected shipping date via email or in-app notification.

[0912] ---

[0913] This system's processing steps enable product recommendations based on personal and emotional information, thereby improving the user experience.

[0914] (Application Example 2)

[0915] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0916] Current online shopping systems commonly recommend products based on users' personal information and purchase history. However, these systems recommend products based on uniform criteria without considering the user's emotional state, resulting in a limited user experience. There is a problem in providing products that users are actually interested in or that match their mood at that moment. This can increase the effort required for users to find what they truly want, potentially diminishing their desire to purchase.

[0917] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing personal information and emotional information entered by the user, means including a generative AI model that searches a database based on the analysis results and recommends the most suitable product, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide personalized product recommendations that take into account the user's emotional state.

[0918] "User personal information" refers to information that can be used to identify or distinguish a user, such as age, height, weight, preferred design and color, and planned usage date.

[0919] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) based on the user's voice, facial expressions, and actions, and is collected in real time.

[0920] A "server" is a centralized computing system that receives and analyzes information sent by users and recommends products.

[0921] "Means of analysis" refers to the process by which the server uses an analysis algorithm to decipher the user's personal information and emotional information received, and selects the most suitable product for the user.

[0922] A "generative AI model" is a computational model that uses machine learning techniques to recommend the most suitable products based on a user's personal information and emotional data.

[0923] A "user terminal" refers to an electronic device, such as a smartphone, tablet, or PC, that a user uses to communicate with a server.

[0924] "Recommended methods" refer to a mechanism that uses analysis results and generated AI models to appropriately select products based on user information.

[0925] "Means of transmission" refers to the communication protocols and technologies used to deliver product information analyzed and selected by the server to the user's terminal.

[0926] Modes for carrying out the invention

[0927] This invention relates to a system that recommends optimal products based on personal information and emotional information entered by the user. Specifically, it aims to improve the purchasing experience by analyzing the user's emotional state in real time and providing personalized product recommendations based on the user's current mood and preferences. The following describes in detail a specific embodiment of this system.

[0928] composition

[0929] hardware

[0930] User terminal: An electronic device such as a smartphone, tablet, or PC that a user uses to input information and communicate with a server.

[0931] Server: A centralized computing system that receives, analyzes, and recommends products based on information sent from user terminals.

[0932] software

[0933] Frontend: We will utilize React Native (a mobile application development framework) to provide an interface that makes it easy for users to input information.

[0934] Backend: Node.js (server-side environment), Express (web framework), MongoDB (database), and TensorFlow.js (machine learning library) are used to analyze incoming data.

[0935] Operation overview

[0936] Collection of user information and sentiment information

[0937] Users launch the application using their smartphones or tablets and input their personal information, preferred designs, colors, and intended use, as well as real-time facial expressions and voice recordings via the camera and microphone. This allows the system to collect data on the user's emotional state.

[0938] Information transmission

[0939] Personal and emotional information transmitted from the user's terminal is securely sent to the server via the HTTPS protocol. This ensures the security of the data during transmission.

[0940] Information analysis

[0941] The server uses TensorFlow.js to analyze the personal and emotional information of the users it receives. The analyzed emotional data represents the user's emotional state (e.g., joy, excitement, confusion) as numerical data.

[0942] Generate product recommendations

[0943] Based on the analysis results, the server searches the MongoDB database for products that match the user's preferences and emotional state. Using a generative AI model, the most suitable products are recommended.

[0944] Sending and displaying recommended products

[0945] The server generates a list of recommended products, which is then sent to the user's terminal. The user's terminal then displays images, prices, features, and user reviews of the recommended products.

[0946] Product selection and purchase

[0947] The user selects the desired product from the displayed recommended items, adds it to their cart, and proceeds with the purchase. Following the user interface, they enter their payment method and shipping address and confirm the purchase.

[0948] Specific example

[0949] For example, if a user wants to buy a "snowboard," they might enter the following information:

[0950] Example of a prompt

[0951] Age: 28

[0952] Height: 170cm

[0953] Weight: 65kg

[0954] Favorite colors: Blue, Black

[0955] Preferred design: Simple

[0956] Usage: Snowboarding

[0957] Emotional information: Joy, Trust level 90%

[0958] Based on this input information, the system analyzes the user's emotional state and recommends snowboards with bright colors and stylish designs, taking into account that the user is experiencing "joy." In this way, personalized product recommendations that reflect the user's emotional state are achieved.

[0959] The above describes the detailed configuration for carrying out the invention. This system allows users to select the optimal product without specialized knowledge and obtain a seamless purchasing experience.

[0960] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0961] Program processing flow

[0962] Step 1:

[0963] Users launch the application using their smartphones or tablets and enter personal and emotional information. They enter their age, height, weight, preferred design and color, and intended use into a form. Simultaneously, they use their camera and microphone to input real-time facial expressions and voice into the application. This entered information is then passed directly to the next processing step.

[0964] Step 2:

[0965] The user terminal transmits entered personal and emotional information to the server using the HTTPS protocol. Before transmission, this data is encrypted to ensure the security of the data during transmission. Input data consists of personal and emotional information, and output data is encrypted.

[0966] Step 3:

[0967] The server analyzes personal and emotional information received from the user's terminal. The received data is fed into an analysis algorithm to evaluate the user's height, weight, preferences, and emotional state. Emotional analysis is performed using TensorFlow.js, extracting the type of emotion (e.g., joy, excitement, confusion) and its confidence level as numerical values. The input data consists of received personal and emotional information, while the output data is the analysis results (numerical data of the user's preferences and emotional state).

[0968] Step 4:

[0969] The server performs a database search based on the analysis results. It queries the MongoDB database to find products that match the user's preferences and emotional state. Using a generative AI model, it further filters the search results to identify the most suitable products. The input data consists of the analysis results and product data in the database, and the output data is a list of recommended products.

[0970] Step 5:

[0971] The server generates a list of optimal products and sends it back to the user's terminal via HTTPS. The data sent is a list of recommended products, and the user's terminal receives this data.

[0972] Step 6:

[0973] The user terminal analyzes the received list of recommended products and displays it on the user interface. It displays product images, prices, features, and other users' ratings to make selection easier for the user. The input data is the received list of recommended products, and the output data is the product information displayed on the screen.

[0974] Step 7:

[0975] The user selects the desired product from the recommended products displayed on the screen and adds it to their cart. They then proceed to the checkout screen, where they enter their payment method and shipping address, and click the "Confirm Purchase" button. The input data consists of the user's selected products and purchase information, while the output data is the purchase confirmation information sent to the server.

[0976] Step 8:

[0977] The server receives information that the user has completed the purchase process and checks the inventory. If inventory is available, it confirms the purchase and begins preparing for shipment. The user is notified that the purchase is complete and the estimated shipping date. The input data is the confirmed purchase information, and the output data is the purchase confirmation and estimated shipping date notification.

[0978] In this way, personalized product recommendations and a seamless purchasing experience are realized based on the user's personal and emotional information.

[0979] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0980] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0981] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0982] [Third Embodiment]

[0983] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0984] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0985] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0986] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0987] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0988] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0989] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0990] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0991] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0992] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0993] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0994] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0995] ---

[0996] This invention is a system designed to help users select appropriate products and increase their purchasing intent. The system includes a process where it recommends products based on personal information and preferences provided by the user, and the user then selects and purchases a product based on these recommendations.

[0997] Specifically, this system is configured as follows:

[0998] Entering user information

[0999] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user wants to purchase a snowboard, they would enter information such as height 170cm, weight 65kg, and preference for blue and black designs.

[1000] Submitting the entered information

[1001] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS). The input information is encrypted before transmission, ensuring the security of the data during transmission.

[1002] Receiving and analyzing user information

[1003] Server: The server receives user input information sent from the terminal and begins analysis. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[1004] Generate product recommendations

[1005] Server: The server searches the database for corresponding products based on the analyzed user information. It uses machine learning algorithms and filtering techniques to list the products best suited to the user's needs. For example, it might generate a list recommending the optimal snowboard and boots based on the user's height and weight.

[1006] Sending and displaying recommended products

[1007] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[1008] Product selection and purchase

[1009] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[1010] Submission and confirmation of purchase information

[1011] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[1012] This system makes it easier for users to select the right products, even without specialized knowledge. Furthermore, personalized recommendations for each user increase their purchasing intent and enable more efficient online shopping.

[1013] ---

[1014] The following describes the processing flow.

[1015] ---

[1016] Step 1:

[1017] User: The user launches the app and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use.

[1018] Specific actions:

[1019] Launch the app and select a product category (for example, snowboards).

[1020] Enter your age (e.g., 25 years old), height (e.g., 170cm), weight (e.g., 65kg), preferences (e.g., blue or black design), and planned date of use in the input form.

[1021] Step 2:

[1022] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS).

[1023] Specific actions:

[1024] After completing the input, the user presses the "Next" or "Submit" button.

[1025] The input information is encrypted and sent to the server.

[1026] Step 3:

[1027] Server: Receives and analyzes user input information sent from the terminal.

[1028] Specific actions:

[1029] The system decodes the received data and analyzes information such as age, height, weight, preferred design and color, and intended use.

[1030] Based on this information, we extract parameters to narrow down the recommended products.

[1031] Step 4:

[1032] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products.

[1033] Specific actions:

[1034] A query is sent to the database to search for products that match the user's parameters.

[1035] Evaluate search results and create a list to recommend the best products to the user.

[1036] Step 5:

[1037] Server: Sends the generated list of recommended products to the terminal.

[1038] Specific actions:

[1039] Convert the recommended product list into JSON format and send it to the user's terminal.

[1040] The list includes product images, prices, features, and other users' ratings.

[1041] Step 6:

[1042] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[1043] Specific actions:

[1044] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[1045] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[1046] Step 7:

[1047] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[1048] Specific actions:

[1049] The user taps on the product to view details and then presses the "Add to Cart" button.

[1050] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[1051] Step 8:

[1052] Terminal: When a user completes the purchase process, it sends that information to the server.

[1053] Specific actions:

[1054] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[1055] Step 9:

[1056] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[1057] Specific actions:

[1058] It integrates with the inventory system to check the stock of the selected product.

[1059] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[1060] We will notify you of information such as the expected shipping date and tracking number.

[1061] ---

[1062] The above outlines the specific processing steps of this system. This process allows users to easily select the most suitable product and proceed with the purchase smoothly.

[1063] (Example 1)

[1064] Next, we will describe Example 1. 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."

[1065] Traditional online shopping systems often made it difficult for users to find suitable products from a large amount of information, leading to decreased purchasing intent. Furthermore, there were security concerns, as users' personal information was sometimes not protected. Additionally, the lack of appropriate recommendation algorithms meant that accurate product recommendations were not possible.

[1066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1067] In this invention, the server includes means for encrypting user information and transmitting it to the server using a secure communication protocol, means for the server to analyze the user information and recommend corresponding products, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide accurate product recommendations based on user preferences while ensuring the security of user information.

[1068] "User information" refers to personal data that users enter into the system, such as age, height, weight, preferred design and color, and intended use.

[1069] "Encryption" is the process of transforming data according to a specific algorithm in order to ensure the security of data during transmission.

[1070] A "secure communication protocol" is a protocol used to ensure the confidentiality and integrity of data during data communication over the internet, and HTTPS is a representative example.

[1071] A "server" is a computer system that receives data sent by a user, performs various processing steps, and returns the results to the user.

[1072] "Analysis" is the process of examining user information using specific algorithms and methods to extract meaningful data and parameters.

[1073] A "machine learning algorithm" is an algorithm that performs predictions and classifications based on data, and in this context, it is a method used to recommend the most suitable product to a user.

[1074] The "Recommended Products List" is a list of products selected based on user information, tailored to the user's preferences and needs.

[1075] A "user interface" is a means for a user to interact with a computer or system, and in this context, it refers to a screen that displays product information or the progress of the purchase process.

[1076] The "Add to Cart" function allows users to temporarily reserve items they are considering purchasing.

[1077] The "purchase process" refers to a series of operations in which the user enters the payment method and shipping address for the selected product and finally confirms the purchase.

[1078] This invention is a system that helps users select appropriate products and improves their purchasing intent. The system includes a process in which products are recommended based on personal information and preferences provided by the user, and the user selects a product based on these recommendations and proceeds with the purchase.

[1079] Hardware and software to be used

[1080] User terminal: A device used by the user to enter information and view a list of recommended products (e.g., smartphone, tablet, PC).

[1081] Server: A computer system used for receiving, analyzing, recommending products, and transmitting results.

[1082] Software libraries and frameworks:

[1083] Data analysis: Python's pandas and numpy.

[1084] Machine learning algorithm: Scikit-learn.

[1085] Encryption: AES (Advanced Encryption Standard).

[1086] Communication protocol: HTTPS.

[1087] Frontend: React, Vue.js.

[1088] Entering user information

[1089] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user is 170cm tall, weighs 65kg, likes blue and black designs, and wants to purchase a freestyle snowboard, they would enter that information into the input form.

[1090] Submitting the entered information

[1091] Terminal: The terminal encrypts the collected user information and sends it to the server using HTTPS, a secure communication protocol. The encryption technology used is AES, which ensures the security of the data during transmission.

[1092] Receiving and analyzing user information

[1093] Server: The server receives encrypted data sent from the terminal and decrypts it. Next, it analyzes the received data using Python's pandas and numpy libraries to extract parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[1094] Generate product recommendations

[1095] Server: Based on the analyzed user information, the server searches the database for relevant products. The search uses machine learning algorithms (collaborative filtering and content-based filtering utilizing Scikit-learn) to list the products best suited to the user's needs. For example, it might add the optimal snowboard and boots for the user's height and weight to the recommendation list.

[1096] Sending and displaying recommended products

[1097] Server and Terminal: The server converts the generated recommended product list into JSON format and sends it back to the terminal. The terminal parses the received list using a JavaScript framework such as React or Vue.js and displays it in the user interface. The recommended product list includes product images, prices, features, and reviews from other users. For example, it might display images and detailed information of a blue and black snowboard and boots in the appropriate size.

[1098] Product selection and purchase

[1099] User: The user selects the product they wish to purchase from the displayed list of recommended products. They add the product to their cart by clicking the "Add to Cart" button. Then, they follow the on-screen instructions to proceed with the purchase, entering their payment method and shipping information. For example, a user adds a blue snowboard and boots of the appropriate size to their cart, enters their credit card information and shipping address, and confirms the purchase.

[1100] Submission and confirmation of purchase information

[1101] Terminal and Server: Once a user completes the purchase process, the terminal sends the purchase information to the server. The server stores the received purchase information in its database and checks the product's inventory. It then verifies the purchase and sends an email to the user notifying them that the purchase is complete and providing the estimated shipping date. As a specific example, a "Purchase Completion Notification" email is sent to the user's email address, providing the estimated shipping date and tracking number.

[1102] Examples of specific cases and prompt statements

[1103] Specific example

[1104] 1. The user launches the app and expresses a desire to purchase a snowboard.

[1105] 2. The user enters the following information into the input form:

[1106] Age: 25

[1107] Height: 170cm

[1108] Weight: 65kg

[1109] Preferred design: Blue or black

[1110] Usage: Freestyle

[1111] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[1112] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[1113] 5. The recommendation list is sent to the device and displayed in the user interface.

[1114] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[1115] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[1116] Example of a prompt

[1117] For example, the prompt text to be input to the generative AI model would be as follows:

[1118] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[1119] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1120] Step 1: Enter user information

[1121] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use. For example, a user might enter "Height 170cm, Weight 65kg, Likes blue and black designs" into the input form.

[1122] Input: User's personal information (age, height, weight, preferred design, intended use).

[1123] Output: User input data.

[1124] Step 2: Encrypt and send the entered information.

[1125] Terminal: The terminal encrypts the user's input information using AES (Advanced Encryption Standard). It then sends this encrypted data to the server using HTTPS, a secure communication protocol.

[1126] Input: User input data.

[1127] Data processing: Encryption using AES.

[1128] Output: Encrypted user information data.

[1129] Step 3: Receiving and decrypting user information

[1130] Server: The server receives encrypted data sent from the terminal. Next, it decrypts the received data. This decryption uses a key that was shared in advance.

[1131] Input: Encrypted user information data.

[1132] Data processing: Decryption of encrypted data.

[1133] Output: Decoded user information data.

[1134] Step 4: Analyze user information

[1135] Server: Analyzes the decrypted user information. Using Python's pandas and numpy libraries, the server extracts parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[1136] Input: Decrypted user information data.

[1137] Data processing: Data analysis and parameter extraction (pandas, numpy).

[1138] Output: Parameters for recommendation.

[1139] Step 5: Search for products and generate a recommendation list

[1140] Server: Uses machine learning algorithms (e.g., Scikit-learn) to search a database based on user parameters. Generates a list of recommended products. For example, it might list snowboards and boots that match the user's height and weight.

[1141] Input: Parameters for recommendation.

[1142] Data processing: Search and list generation using machine learning algorithms.

[1143] Output: Recommended product list.

[1144] Step 6: Submit your recommended product

[1145] Server: Converts the generated list of recommended products into JSON format and sends it to the user's terminal.

[1146] Input: Recommended product list.

[1147] Data processing: Conversion to JSON format.

[1148] Output: A list of recommended products in JSON format.

[1149] Step 7: Display recommended products

[1150] Terminal: The terminal parses the received list of recommended products and displays it in the user interface. This display uses React or Vue.js. The displayed list includes product images, prices, features, and reviews from other users.

[1151] Input: A list of recommended products in JSON format.

[1152] Data processing: Parsing and displaying lists.

[1153] Output: Recommended product list on the user interface.

[1154] Step 8: Product Selection and Purchase Procedure

[1155] User: The user selects the product they wish to purchase from the displayed recommended products and clicks the "Add to Cart" button. They then enter their payment method and shipping information and proceed with the purchase.

[1156] Input: User's desired purchase items and payment information.

[1157] Data processing: Proceeding with the purchase procedure.

[1158] Output: Purchase confirmation data.

[1159] Step 9: Submit and confirm purchase information

[1160] Terminal and Server: The terminal sends purchase confirmation data to the server. The server stores the received purchase information in its database and checks inventory. After that, it verifies the purchase and notifies the user of the purchase completion and estimated shipping date.

[1161] Input: Purchase confirmation data.

[1162] Data processing: Database storage and inventory checks.

[1163] Output: Purchase completion notification and estimated shipping date.

[1164] The above describes the specific program processing flow in this system.

[1165] Examples of specific cases and prompt statements

[1166] Specific example

[1167] 1. The user launches the app and expresses a desire to purchase a snowboard.

[1168] 2. The user enters the following information into the input form:

[1169] Age: 25

[1170] Height: 170cm

[1171] Weight: 65kg

[1172] Preferred design: Blue or black

[1173] Usage: Freestyle

[1174] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[1175] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[1176] 5. The recommendation list is sent to the device and displayed in the user interface.

[1177] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[1178] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[1179] Example of a prompt

[1180] For example, the prompt text to be input to the generative AI model would be as follows:

[1181] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[1182] (Application Example 1)

[1183] Next, we will explain Application Example 1. In the following explanation, 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."

[1184] The aim is to help users who are unsure which products to choose, enabling them to select appropriate products even without specialized knowledge, thereby increasing their purchasing intent. Furthermore, the goal is to improve the user experience by providing personalized recommendations to users during online shopping.

[1185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1186] In this invention, the server includes means for securely transmitting user input information via the HTTPS protocol, means for recommending the most suitable products from a database using machine learning algorithms, and means for searching for products using prompt sentences with a generative AI model. This allows users to easily find suitable products and enhance their purchasing intent through a personalized experience.

[1187] A "user terminal" refers to a device used by a user, such as a smartphone, tablet, or computer.

[1188] The HTTPS protocol is a secure communication protocol for safely sending and receiving data over the internet.

[1189] A "machine learning algorithm" is a method that analyzes data, recognizes patterns, and derives relevant results.

[1190] A "database" is a system for organizing and managing collections of data, and it stores user information and product information.

[1191] A "generative AI model" is a model used to perform specific tasks using artificial intelligence, making predictions and recommendations based on input data.

[1192] A "prompt message" is text input to a generative AI model, and its role is to give the model instructions for performing a specific task.

[1193] A "recommended product" is a list of products that the server selects and provides based on user information analysis, ensuring the product best suits the user's needs.

[1194] An "interface" is a means by which a user and a system can interact and communicate with each other, and includes graphical user interfaces (GUIs) and command-line interfaces (CLIs).

[1195] This invention is a system that recommends optimal products based on a user's personal information and preferences, thereby increasing their willingness to purchase. The system includes a user terminal, a secure communication protocol, a server, a machine learning algorithm, a generative AI model, and a database.

[1196] Entering user information

[1197] The user launches the application using their smartphone and selects the product category they wish to purchase. Next, they enter information such as age, height, weight, preferred color and design, and intended use into an input form. For example, if a user wants to purchase outdoor equipment, they would enter information such as age 30, height 175cm, weight 70kg, preference for blue, and camping equipment.

[1198] Submitting the entered information

[1199] The user terminal securely transmits the collected input information to the server using the HTTPS protocol. The input information is encrypted before transmission, ensuring the security of the data during communication.

[1200] Receiving and analyzing user information

[1201] The server receives user input information sent from the terminal and begins analysis using a machine learning algorithm. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[1202] Generate product recommendations

[1203] The server searches the database for corresponding products based on the analyzed user information. The search uses a generative AI model and prompt text to list the products best suited to the user's needs. For example, it might generate a list recommending a blue outdoor jacket, a suitable tent, and a sleeping mat.

[1204] Sending and displaying recommended products

[1205] The server sends the generated list of recommended products to the user's terminal, which then analyzes the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings.

[1206] Product selection and purchase

[1207] The user selects the items they wish to purchase from the list of recommended products and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue outdoor jacket and a matching tent to their cart, enter their payment method and shipping address, and then confirm the purchase.

[1208] Submission and confirmation of purchase information

[1209] Once the user completes the purchase process, the purchase information is sent from the device to the server. The server checks the inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[1210] Hardware and software to use

[1211] Smartphone: A device used by users to input information and view results.

[1212] Server: Uses Python, TensorFlow, and Firebase to perform data analysis and recommendation algorithms.

[1213] Communication protocol: Uses HTTPS to securely send and receive data.

[1214] Database: User and product information is managed using Firebase.

[1215] Examples of prompt statements

[1216] Please recommend outdoor gear for camping, for a user who is 175cm tall, weighs 70kg, likes the color blue, and is looking for products suitable for camping.

[1217] As a result, users can easily find the right products, and their purchasing intent can be increased through a personalized experience.

[1218] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1219] Step 1:

[1220] The user launches the application using their smartphone. The user selects the product category they wish to purchase and enters information such as age, height, weight, preferred design and color, and intended use into the input form. This data is user information and is used for the next step.

[1221] Step 2:

[1222] The terminal encrypts the information entered by the user using the HTTPS protocol and securely sends it to the server. Specifically, the application converts the input data into JSON format and sends it to the server using an HTTPS POST request. The output of this step is encrypted user information.

[1223] Step 3:

[1224] The server receives user input information sent from the terminal. It parses the received data and extracts parameters from the JSON format. The data used here is user information, and the parsing results become the input for the next step.

[1225] Step 4:

[1226] The server uses Python and TensorFlow to apply machine learning algorithms and calculate parameters for recommending the most suitable products based on user input. Specifically, it analyzes information such as the user's height, weight, age, preferred design and color, and intended use to extract product characteristics. The output of this step is the characteristic information of the recommended products.

[1227] Step 5:

[1228] The server searches for corresponding products in its database (Firebase) based on the analyzed user information and generates prompt messages using a generative AI model. Specifically, it uses the prompt message to instruct the AI ​​model to search the database and generate an optimal product list. For example, it might generate a prompt message such as, "The user is 175cm tall, weighs 70kg, likes the color blue, and would like recommendations for outdoor equipment to use for camping." The output of this step is a list of recommended products.

[1229] Step 6:

[1230] The server sends the generated list of recommended products to the terminal. Specifically, it converts the generated product list into JSON format and sends it to the terminal using an HTTPS POST request. The output of this step is the list information of recommended products.

[1231] Step 7:

[1232] The terminal analyzes the received list of recommended products and displays it on the user interface. Specifically, it analyzes the JSON data to display product images, prices, features, and other users' ratings. The output of this step is the recommended product information displayed to the user.

[1233] Step 8:

[1234] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. Next, they proceed with the purchase process following the on-screen instructions. Specifically, this involves adding the desired items to the cart and entering payment and shipping information. The input in this step is the user's purchase preferences and payment information, while the output is information about the items added to the cart.

[1235] Step 9:

[1236] The terminal sends purchase information to the server once the user completes the purchase process. Specifically, it converts the purchase information into JSON format and sends it to the server using an HTTPS POST request. The output of this step is the sent purchase information.

[1237] Step 10:

[1238] The server checks inventory based on the received purchase information and confirms the purchase. Next, it notifies the user that the purchase is complete and the expected shipping date. Specifically, it uses the inventory management system to check the inventory status and sends a purchase confirmation email to the user. The output of this step is the purchase confirmation and the notification of the expected shipping date.

[1239] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1240] ---

[1241] This invention aims to improve the user's purchasing experience by providing a system that helps users select the most suitable products for them, and by combining it with an emotion engine that recognizes the user's emotions. This system supports users from product recommendations to the purchase process based on personal information, preferences, and emotional states provided by the user.

[1242] Specifically, this system is configured as follows:

[1243] Input of user information and sentiment information

[1244] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use date. The user also provides the system with their current emotional state (e.g., joy, excitement, confusion) through voice and facial expressions. For example, if a user wants to buy a snowboard, they would enter information such as height 170cm, weight 65kg, and preference for blue and black designs. Furthermore, their facial expressions and voice would be collected in real-time by the emotion engine via camera and microphone.

[1245] Information transmission

[1246] Terminal: User input information and sentiment information are sent to the server using a secure communication protocol (e.g., HTTPS). User information and sentiment information are encrypted before transmission, ensuring the security of the data during transmission.

[1247] Information reception and analysis

[1248] Server: The server receives user information and emotional information sent from the terminal and begins analysis. The analysis evaluates the user's height, weight, age, preferred design and color, intended use, and emotional state, and extracts parameters to recommend the most suitable product based on this information. For example, if the user is excited, the server will select recommended products that reflect that emotion.

[1249] Generate product recommendations

[1250] Server: Based on the analysis results, it searches the database for corresponding products. The search uses machine learning algorithms and filtering techniques to list products that best suit the user's needs and emotional state. For example, if the user is excited, it might focus on recommending colorful and stylish snowboards and boots.

[1251] Sending and displaying recommended products

[1252] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[1253] Product selection and purchase

[1254] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[1255] Submission and confirmation of purchase information

[1256] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date.

[1257] This invention enables product recommendations that reflect user emotions in real time, thereby increasing user purchasing intent. Furthermore, users can select appropriate products without requiring specialized knowledge, providing a seamless online shopping experience.

[1258] ---

[1259] The following describes the processing flow.

[1260] ---

[1261] Step 1:

[1262] User: The user launches the app and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use. Additionally, they provide facial expressions and voice to the system in real time via camera and microphone, allowing the emotion engine to recognize the user's emotional state.

[1263] Specific actions:

[1264] Launch the app and select a product category (for example, snowboards).

[1265] Enter your age (e.g., 25 years old), height (e.g., 170cm), weight (e.g., 65kg), preferences (e.g., blue or black design), and planned date of use in the input form.

[1266] Enable the camera and microphone to send facial expressions and voices to the emotion engine in real time.

[1267] Step 2:

[1268] Terminal: Sends user input information and sentiment information to the server using a secure communication protocol (e.g., HTTPS).

[1269] Specific actions:

[1270] After entering the information, press the "Next" or "Submit" button.

[1271] Input information and emotional information are encrypted and sent to the server.

[1272] Step 3:

[1273] Server: Receives user information and sentiment information sent from the terminal and begins analysis.

[1274] Specific actions:

[1275] The system decodes the received data and analyzes it to determine age, height, weight, preferred designs and colors, intended use, emotional state, and more.

[1276] Based on this information, we extract the parameters used to search for recommended products.

[1277] Step 4:

[1278] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products that reflect the emotional state.

[1279] Specific actions:

[1280] A query is sent to the database to search for products that match the user's parameters and emotional state.

[1281] We generate the optimal recommended product list, taking into account product popularity and ratings.

[1282] For example, if a user is excited, we might recommend products with vibrant designs.

[1283] Step 5:

[1284] Server: Sends the generated list of recommended products to the terminal.

[1285] Specific actions:

[1286] Convert the recommended product list into JSON format and send it to the user's terminal.

[1287] Recommended products include product images, prices, features, and reviews from other users.

[1288] Step 6:

[1289] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[1290] Specific actions:

[1291] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[1292] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[1293] Step 7:

[1294] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[1295] Specific actions:

[1296] The user taps on the product to view details and then presses the "Add to Cart" button.

[1297] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[1298] Step 8:

[1299] Terminal: When a user completes the purchase process, it sends that information to the server.

[1300] Specific actions:

[1301] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[1302] Step 9:

[1303] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[1304] Specific actions:

[1305] It integrates with the inventory system to check the stock of the selected product.

[1306] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[1307] We will notify you of information such as the expected shipping date and tracking number.

[1308] ---

[1309] The above outlines the specific processing steps of the system that incorporates the emotion engine. This process allows users to select the optimal product according to their emotional state and proceed with the purchase smoothly.

[1310] (Example 2)

[1311] Next, we will describe Example 2. 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."

[1312] Traditional online shopping systems mostly recommended products based on users' personal information, without considering their emotional state. Therefore, they were unable to provide product recommendations that reflected users' real-time emotional states, failing to adequately enhance user satisfaction and purchasing intent. Furthermore, traditional systems lacked the means to assist users in selecting the optimal product, even if they lacked specialized knowledge.

[1313] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input personal information and emotional information, means for transmitting the user's personal information and emotional information to the server, and means for the server to analyze the user's personal information and emotional information and recommend corresponding products. This makes it possible to recommend products that take into account not only the user's personal information but also real-time emotional information. This is expected to improve user satisfaction and purchasing intent, and enable users to choose appropriate products even without specialized knowledge.

[1314] ---

[1315] A "user" refers to an individual or group that uses this system to search for, select, and purchase products.

[1316] "Personal information" refers to information that can identify a user, such as the user's age, height, weight, preferred design and color, and planned usage date.

[1317] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) obtained by analyzing the user's facial expressions, voice, etc.

[1318] A "server" refers to a central processing unit that receives and analyzes users' personal and emotional information and recommends corresponding products.

[1319] A "terminal" refers to a device used by a user to input personal and emotional information and send it to a server.

[1320] A "database" refers to an information storage system that stores user and product information and allows it to be searched and retrieved as needed.

[1321] An "algorithm" refers to a set of computational procedures and rules used to recommend the most suitable products based on a user's personal and emotional information.

[1322] "Interface" refers to the screens and operating methods that users use to interact with a system, and includes GUIs (Graphical User Interfaces) used for selecting products and proceeding with purchase procedures.

[1323] "Recommended products" refer to products that the system recommends based on the analysis of the user's personal information and emotional data.

[1324] ---

[1325] This invention aims to improve the user's purchasing experience by providing a system that helps users select the most suitable products for them, and by combining it with an emotion engine that recognizes the user's emotions. This system supports users from product recommendations to the purchase process based on personal information, preferences, and emotional states provided by the user.

[1326] Input of user information and sentiment information

[1327] The user launches the application and selects the product category they wish to purchase. They then enter personal information such as age, height, weight, preferred design and color, and intended use date. Furthermore, the user provides real-time emotional states to the system using a camera and microphone. Facial recognition software and speech recognition software are used to input emotional states. Specific software examples include general facial recognition APIs for facial recognition and speech-to-text APIs for speech recognition.

[1328] Information transmission

[1329] The terminal transmits the entered user information and sentiment information to the server using a secure communication protocol. Before transmission, the information is encrypted using protocols such as TLS to ensure the security of the data during transmission.

[1330] Information reception and analysis

[1331] The server receives user information and sentiment information sent from the terminal and stores it in a database. Next, it analyzes this information using an AI engine. Specifically, machine learning platforms and models are used as the AI ​​engine. It analyzes the user's personal information and sentiment state and extracts parameters to recommend the most suitable products based on that analysis.

[1332] Generate product recommendations

[1333] The server uses the analysis results to search for the most suitable product from its product database. This search utilizes machine learning algorithms and filtering techniques. For example, a recommendation engine is used to list products that best suit the user's preferences and emotional state.

[1334] Sending and displaying recommended products

[1335] The server sends the generated list of recommended products to the user's terminal, which receives the list and displays it in the user interface. The user interface is implemented using a graphical user interface framework. This display includes product images, prices, features, and other users' ratings.

[1336] Product selection and purchase

[1337] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. A screen then appears to proceed with the purchase. This screen includes fields for entering payment method and shipping address. A payment service API is used for payment processing.

[1338] Submission and confirmation of purchase information

[1339] Once a user completes the purchase process, the purchase information is sent from the device to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date. Notifications are sent via email or in-app notifications.

[1340] Specific example

[1341] Below are specific scenarios and examples of input prompts for the generated AI model, designed to help visualize how this system works.

[1342] Example Scenario

[1343] User A has decided to go snowboarding this weekend and wants to buy a new snowboard. User A has decided to use the system to purchase a snowboard.

[1344] 1. Person A launches the app and selects "Snowboarding".

[1345] 2. Enter the required personal information (height 170cm, weight 65kg, preferred colors are blue and black).

[1346] 3. Smile at the camera and tell the microphone, "I'm excited."

[1347] 4. The device sends this information to the server.

[1348] 5. The server analyzes the information and searches for the most suitable product.

[1349] 6. A list of recommended snowboards and boots with blue or black designs will be displayed on the device.

[1350] 7. Person A selects their favorite item, adds it to their cart, and confirms the purchase.

[1351] Input prompts for the generative AI model

[1352] "If a user launches the app to buy a snowboard, enters their height as 170cm, weight as 65kg, preferred colors as blue and black, smiles at the camera, and says into the microphone that they are excited, what products would be recommended?"

[1353] This system enables product recommendations that reflect user emotions in real time, which is expected to improve the purchasing experience. Users will be able to easily select the right product even without specialized knowledge.

[1354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1355] ---

[1356] Step 1: Enter user information and sentiment information.

[1357] User: Launch the application and select the product category you wish to purchase. Next, enter personal information such as age, height, weight, preferred design and color, and intended use date. You will also input facial expressions and voice via camera and microphone to provide your current emotional state (e.g., joy, excitement, confusion, etc.).

[1358] Input: Age, height, weight, preferred design and color, planned date of use, facial expression, voice

[1359] Output: Personal and emotional data

[1360] Specific actions: If a user wants to buy a snowboard, they will enter information such as their height (170cm), weight (65kg), and preference for blue and black designs. They will also smile in front of the camera and say into the microphone, "I'm excited."

[1361] Step 2: Sending Information

[1362] Terminal: Sends entered personal and emotional information to the server using a secure communication protocol (e.g., HTTPS). The information is encrypted before transmission.

[1363] Input: Personal information and emotional information

[1364] Output: Data packets containing encrypted personal and emotional information

[1365] Specific operation: The entered information is encrypted using the TLS protocol and sent to the server via HTTPS.

[1366] Step 3: Information reception and analysis

[1367] Server: Receives personal and emotional information sent from terminals and stores it in a database. Then, an AI engine is used to analyze this data.

[1368] Input: Encrypted personal information and emotional information

[1369] Output: Data showing the analyzed user's preferences and emotional state.

[1370] Specific operation: Received information is stored in a database, and an AI engine (e.g., a machine learning platform) is used to analyze the user's height, weight, age, preferred designs and colors, and emotional state.

[1371] Step 4: Generating Product Recommendations

[1372] Server: Based on the analysis results, it searches the database for the most suitable product. It uses machine learning algorithms and filtering techniques.

[1373] Input: Analyzed user preferences and emotional state

[1374] Output: List of recommended products

[1375] Specific operation: Use a recommendation engine to list the most suitable products, taking into account the user's age, preferences, and emotional state. For example, select products with brightly colored designs for an excited user.

[1376] Step 5: Submit and display recommended products

[1377] Server and Terminal: The server sends the generated list of recommended products to the terminal, and the terminal displays the received list in the user interface.

[1378] Input: List of recommended contests

[1379] Output: Product information displayed to the user

[1380] Specific operation: The list includes product images, prices, features, and other users' ratings, and is displayed on the user's device.

[1381] Step 6: Product Selection and Purchase

[1382] User: Select the product you want to purchase from the list of recommended products and click the "Add to Cart" button. Then, proceed with the purchase process, enter your payment method and shipping address, and confirm your purchase.

[1383] Input: Selected product information, payment information, shipping address information

[1384] Output: Final purchase information

[1385] Specific action: The user adds a blue snowboard and matching boots to their cart, enters payment information and shipping address, and confirms the purchase.

[1386] Step 7: Submit and confirm purchase information

[1387] Terminal and Server: Purchase information is sent from the terminal to the server, which checks inventory and confirms the purchase. The user is notified of the purchase completion and the estimated shipping date.

[1388] Input: Purchase Information

[1389] Output: Purchase confirmation information, estimated shipping date

[1390] Specific operation: The server checks inventory and sends a notification along with purchase confirmation. The user is notified of the expected shipping date via email or in-app notification.

[1391] ---

[1392] This system's processing steps enable product recommendations based on personal and emotional information, thereby improving the user experience.

[1393] (Application Example 2)

[1394] Next, we will explain application example 2. In the following explanation, 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."

[1395] Current online shopping systems commonly recommend products based on users' personal information and purchase history. However, these systems recommend products based on uniform criteria without considering the user's emotional state, resulting in a limited user experience. There is a problem in providing products that users are actually interested in or that match their mood at that moment. This can increase the effort required for users to find what they truly want, potentially diminishing their desire to purchase.

[1396] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing personal information and emotional information entered by the user, means including a generative AI model that searches a database based on the analysis results and recommends the most suitable product, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide personalized product recommendations that take into account the user's emotional state.

[1397] "User personal information" refers to information that can be used to identify or distinguish a user, such as age, height, weight, preferred design and color, and planned usage date.

[1398] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) based on the user's voice, facial expressions, and actions, and is collected in real time.

[1399] A "server" is a centralized computing system that receives and analyzes information sent by users and recommends products.

[1400] "Means of analysis" refers to the process by which the server uses an analysis algorithm to decipher the user's personal information and emotional information received, and selects the most suitable product for the user.

[1401] A "generative AI model" is a computational model that uses machine learning techniques to recommend the most suitable products based on a user's personal information and emotional data.

[1402] A "user terminal" refers to an electronic device, such as a smartphone, tablet, or PC, that a user uses to communicate with a server.

[1403] "Recommended methods" refer to a mechanism that uses analysis results and generated AI models to appropriately select products based on user information.

[1404] "Means of transmission" refers to the communication protocols and technologies used to deliver product information analyzed and selected by the server to the user's terminal.

[1405] Modes for carrying out the invention

[1406] This invention relates to a system that recommends optimal products based on personal information and emotional information entered by the user. Specifically, it aims to improve the purchasing experience by analyzing the user's emotional state in real time and providing personalized product recommendations based on the user's current mood and preferences. The following describes in detail a specific embodiment of this system.

[1407] composition

[1408] hardware

[1409] User terminal: An electronic device such as a smartphone, tablet, or PC that a user uses to input information and communicate with a server.

[1410] Server: A centralized computing system that receives, analyzes, and recommends products based on information sent from user terminals.

[1411] software

[1412] Frontend: We will utilize React Native (a mobile application development framework) to provide an interface that makes it easy for users to input information.

[1413] Backend: Node.js (server-side environment), Express (web framework), MongoDB (database), and TensorFlow.js (machine learning library) are used to analyze incoming data.

[1414] Operation overview

[1415] Collection of user information and sentiment information

[1416] Users launch the application using their smartphones or tablets and input their personal information, preferred designs, colors, and intended use, as well as real-time facial expressions and voice recordings via the camera and microphone. This allows the system to collect data on the user's emotional state.

[1417] Information transmission

[1418] Personal and emotional information transmitted from the user's terminal is securely sent to the server via the HTTPS protocol. This ensures the security of the data during transmission.

[1419] Information analysis

[1420] The server uses TensorFlow.js to analyze the personal and emotional information of the users it receives. The analyzed emotional data represents the user's emotional state (e.g., joy, excitement, confusion) as numerical data.

[1421] Generate product recommendations

[1422] Based on the analysis results, the server searches the MongoDB database for products that match the user's preferences and emotional state. Using a generative AI model, the most suitable products are recommended.

[1423] Sending and displaying recommended products

[1424] The server generates a list of recommended products, which is then sent to the user's terminal. The user's terminal then displays images, prices, features, and user reviews of the recommended products.

[1425] Product selection and purchase

[1426] The user selects the desired product from the displayed recommended items, adds it to their cart, and proceeds with the purchase. Following the user interface, they enter their payment method and shipping address and confirm the purchase.

[1427] Specific example

[1428] For example, if a user wants to buy a "snowboard," they might enter the following information:

[1429] Example of a prompt

[1430] Age: 28

[1431] Height: 170cm

[1432] Weight: 65kg

[1433] Favorite colors: Blue, Black

[1434] Preferred design: Simple

[1435] Usage: Snowboarding

[1436] Emotional information: Joy, Trust level 90%

[1437] Based on this input information, the system analyzes the user's emotional state and recommends snowboards with bright colors and stylish designs, taking into account that the user is experiencing "joy." In this way, personalized product recommendations that reflect the user's emotional state are achieved.

[1438] The above describes the detailed configuration for carrying out the invention. This system allows users to select the optimal product without specialized knowledge and obtain a seamless purchasing experience.

[1439] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1440] Program processing flow

[1441] Step 1:

[1442] Users launch the application using their smartphones or tablets and enter personal and emotional information. They enter their age, height, weight, preferred design and color, and intended use into a form. Simultaneously, they use their camera and microphone to input real-time facial expressions and voice into the application. This entered information is then passed directly to the next processing step.

[1443] Step 2:

[1444] The user terminal transmits entered personal and emotional information to the server using the HTTPS protocol. Before transmission, this data is encrypted to ensure the security of the data during transmission. Input data consists of personal and emotional information, and output data is encrypted.

[1445] Step 3:

[1446] The server analyzes personal and emotional information received from the user's terminal. The received data is fed into an analysis algorithm to evaluate the user's height, weight, preferences, and emotional state. Emotional analysis is performed using TensorFlow.js, extracting the type of emotion (e.g., joy, excitement, confusion) and its confidence level as numerical values. The input data consists of received personal and emotional information, while the output data is the analysis results (numerical data of the user's preferences and emotional state).

[1447] Step 4:

[1448] The server performs a database search based on the analysis results. It queries the MongoDB database to find products that match the user's preferences and emotional state. Using a generative AI model, it further filters the search results to identify the most suitable products. The input data consists of the analysis results and product data in the database, and the output data is a list of recommended products.

[1449] Step 5:

[1450] The server generates a list of optimal products and sends it back to the user's terminal via HTTPS. The data sent is a list of recommended products, and the user's terminal receives this data.

[1451] Step 6:

[1452] The user terminal analyzes the received list of recommended products and displays it on the user interface. It displays product images, prices, features, and other users' ratings to make selection easier for the user. The input data is the received list of recommended products, and the output data is the product information displayed on the screen.

[1453] Step 7:

[1454] The user selects the desired product from the recommended products displayed on the screen and adds it to their cart. They then proceed to the checkout screen, where they enter their payment method and shipping address, and click the "Confirm Purchase" button. The input data consists of the user's selected products and purchase information, while the output data is the purchase confirmation information sent to the server.

[1455] Step 8:

[1456] The server receives information that the user has completed the purchase process and checks the inventory. If inventory is available, it confirms the purchase and begins preparing for shipment. The user is notified that the purchase is complete and the estimated shipping date. The input data is the confirmed purchase information, and the output data is the purchase confirmation and estimated shipping date notification.

[1457] In this way, personalized product recommendations and a seamless purchasing experience are realized based on the user's personal and emotional information.

[1458] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1459] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1460] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1461] [Fourth Embodiment]

[1462] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1463] As shown in Figure 7, the 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.

[1464] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1465] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1466] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1467] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1468] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1469] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1470] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1471] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1472] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1473] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1474] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1475] ---

[1476] This invention is a system designed to help users select appropriate products and increase their purchasing intent. The system includes a process where it recommends products based on personal information and preferences provided by the user, and the user then selects and purchases a product based on these recommendations.

[1477] Specifically, this system is configured as follows:

[1478] Entering user information

[1479] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user wants to purchase a snowboard, they would enter information such as height 170cm, weight 65kg, and preference for blue and black designs.

[1480] Submitting the entered information

[1481] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS). The input information is encrypted before transmission, ensuring the security of the data during transmission.

[1482] Receiving and analyzing user information

[1483] Server: The server receives user input information sent from the terminal and begins analysis. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[1484] Generate product recommendations

[1485] Server: The server searches the database for corresponding products based on the analyzed user information. It uses machine learning algorithms and filtering techniques to list the products best suited to the user's needs. For example, it might generate a list recommending the optimal snowboard and boots based on the user's height and weight.

[1486] Sending and displaying recommended products

[1487] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[1488] Product selection and purchase

[1489] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[1490] Submission and confirmation of purchase information

[1491] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[1492] This system makes it easier for users to select the right products, even without specialized knowledge. Furthermore, personalized recommendations for each user increase their purchasing intent and enable more efficient online shopping.

[1493] ---

[1494] The following describes the processing flow.

[1495] ---

[1496] Step 1:

[1497] User: The user launches the app and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use.

[1498] Specific actions:

[1499] Launch the app and select a product category (for example, snowboards).

[1500] Enter your age (e.g., 25 years old), height (e.g., 170cm), weight (e.g., 65kg), preferences (e.g., blue or black design), and planned date of use in the input form.

[1501] Step 2:

[1502] Terminal: Sends user input information to the server using a secure communication protocol (e.g., HTTPS).

[1503] Specific actions:

[1504] After completing the input, the user presses the "Next" or "Submit" button.

[1505] The input information is encrypted and sent to the server.

[1506] Step 3:

[1507] Server: Receives and analyzes user input information sent from the terminal.

[1508] Specific actions:

[1509] The system decodes the received data and analyzes information such as age, height, weight, preferred design and color, and intended use.

[1510] Based on this information, we extract parameters to narrow down the recommended products.

[1511] Step 4:

[1512] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products.

[1513] Specific actions:

[1514] A query is sent to the database to search for products that match the user's parameters.

[1515] Evaluate search results and create a list to recommend the best products to the user.

[1516] Step 5:

[1517] Server: Sends the generated list of recommended products to the terminal.

[1518] Specific actions:

[1519] Convert the recommended product list into JSON format and send it to the user's terminal.

[1520] The list includes product images, prices, features, and other users' ratings.

[1521] Step 6:

[1522] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[1523] Specific actions:

[1524] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[1525] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[1526] Step 7:

[1527] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[1528] Specific actions:

[1529] The user taps on the product to view details and then presses the "Add to Cart" button.

[1530] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[1531] Step 8:

[1532] Terminal: When a user completes the purchase process, it sends that information to the server.

[1533] Specific actions:

[1534] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[1535] Step 9:

[1536] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[1537] Specific actions:

[1538] It integrates with the inventory system to check the stock of the selected product.

[1539] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[1540] We will notify you of information such as the expected shipping date and tracking number.

[1541] ---

[1542] The above outlines the specific processing steps of this system. This process allows users to easily select the most suitable product and proceed with the purchase smoothly.

[1543] (Example 1)

[1544] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1545] Traditional online shopping systems often made it difficult for users to find suitable products from a large amount of information, leading to decreased purchasing intent. Furthermore, there were security concerns, as users' personal information was sometimes not protected. Additionally, the lack of appropriate recommendation algorithms meant that accurate product recommendations were not possible.

[1546] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1547] In this invention, the server includes means for encrypting user information and transmitting it to the server using a secure communication protocol, means for the server to analyze the user information and recommend corresponding products, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide accurate product recommendations based on user preferences while ensuring the security of user information.

[1548] "User information" refers to personal data that users enter into the system, such as age, height, weight, preferred design and color, and intended use.

[1549] "Encryption" is the process of transforming data according to a specific algorithm in order to ensure the security of data during transmission.

[1550] A "secure communication protocol" is a protocol used to ensure the confidentiality and integrity of data during data communication over the internet, and HTTPS is a representative example.

[1551] A "server" is a computer system that receives data sent by a user, performs various processing steps, and returns the results to the user.

[1552] "Analysis" is the process of examining user information using specific algorithms and methods to extract meaningful data and parameters.

[1553] A "machine learning algorithm" is an algorithm that performs predictions and classifications based on data, and in this context, it is a method used to recommend the most suitable product to a user.

[1554] The "Recommended Products List" is a list of products selected based on user information, tailored to the user's preferences and needs.

[1555] A "user interface" is a means for a user to interact with a computer or system, and in this context, it refers to a screen that displays product information or the progress of the purchase process.

[1556] The "Add to Cart" function allows users to temporarily reserve items they are considering purchasing.

[1557] The "purchase process" refers to a series of operations in which the user enters the payment method and shipping address for the selected product and finally confirms the purchase.

[1558] This invention is a system that helps users select appropriate products and improves their purchasing intent. The system includes a process in which products are recommended based on personal information and preferences provided by the user, and the user selects a product based on these recommendations and proceeds with the purchase.

[1559] Hardware and software to be used

[1560] User terminal: A device used by the user to enter information and view a list of recommended products (e.g., smartphone, tablet, PC).

[1561] Server: A computer system used for receiving, analyzing, recommending products, and transmitting results.

[1562] Software libraries and frameworks:

[1563] Data analysis: Python's pandas and numpy.

[1564] Machine learning algorithm: Scikit-learn.

[1565] Encryption: AES (Advanced Encryption Standard).

[1566] Communication protocol: HTTPS.

[1567] Frontend: React, Vue.js.

[1568] Entering user information

[1569] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use into the input form. For example, if a user is 170cm tall, weighs 65kg, likes blue and black designs, and wants to purchase a freestyle snowboard, they would enter that information into the input form.

[1570] Submitting the entered information

[1571] Terminal: The terminal encrypts the collected user information and sends it to the server using HTTPS, a secure communication protocol. The encryption technology used is AES, which ensures the security of the data during transmission.

[1572] Receiving and analyzing user information

[1573] Server: The server receives encrypted data sent from the terminal and decrypts it. Next, it analyzes the received data using Python's pandas and numpy libraries to extract parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[1574] Generate product recommendations

[1575] Server: Based on the analyzed user information, the server searches the database for relevant products. The search uses machine learning algorithms (collaborative filtering and content-based filtering utilizing Scikit-learn) to list the products best suited to the user's needs. For example, it might add the optimal snowboard and boots for the user's height and weight to the recommendation list.

[1576] Sending and displaying recommended products

[1577] Server and Terminal: The server converts the generated recommended product list into JSON format and sends it back to the terminal. The terminal parses the received list using a JavaScript framework such as React or Vue.js and displays it in the user interface. The recommended product list includes product images, prices, features, and reviews from other users. For example, it might display images and detailed information of a blue and black snowboard and boots in the appropriate size.

[1578] Product selection and purchase

[1579] User: The user selects the product they wish to purchase from the displayed list of recommended products. They add the product to their cart by clicking the "Add to Cart" button. Then, they follow the on-screen instructions to proceed with the purchase, entering their payment method and shipping information. For example, a user adds a blue snowboard and boots of the appropriate size to their cart, enters their credit card information and shipping address, and confirms the purchase.

[1580] Submission and confirmation of purchase information

[1581] Terminal and Server: Once a user completes the purchase process, the terminal sends the purchase information to the server. The server stores the received purchase information in its database and checks the product's inventory. It then verifies the purchase and sends an email to the user notifying them that the purchase is complete and providing the estimated shipping date. As a specific example, a "Purchase Completion Notification" email is sent to the user's email address, providing the estimated shipping date and tracking number.

[1582] Examples of specific cases and prompt statements

[1583] Specific example

[1584] 1. The user launches the app and expresses a desire to purchase a snowboard.

[1585] 2. The user enters the following information into the input form:

[1586] Age: 25

[1587] Height: 170cm

[1588] Weight: 65kg

[1589] Preferred design: Blue or black

[1590] Usage: Freestyle

[1591] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[1592] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[1593] 5. The recommendation list is sent to the device and displayed in the user interface.

[1594] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[1595] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[1596] Example of a prompt

[1597] For example, the prompt text to be input to the generative AI model would be as follows:

[1598] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[1599] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1600] Step 1: Enter user information

[1601] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use. For example, a user might enter "Height 170cm, Weight 65kg, Likes blue and black designs" into the input form.

[1602] Input: User's personal information (age, height, weight, preferred design, intended use).

[1603] Output: User input data.

[1604] Step 2: Encrypt and send the entered information.

[1605] Terminal: The terminal encrypts the user's input information using AES (Advanced Encryption Standard). It then sends this encrypted data to the server using HTTPS, a secure communication protocol.

[1606] Input: User input data.

[1607] Data processing: Encryption using AES.

[1608] Output: Encrypted user information data.

[1609] Step 3: Receiving and decrypting user information

[1610] Server: The server receives encrypted data sent from the terminal. Next, it decrypts the received data. This decryption uses a key that was shared in advance.

[1611] Input: Encrypted user information data.

[1612] Data processing: Decryption of encrypted data.

[1613] Output: Decoded user information data.

[1614] Step 4: Analyze user information

[1615] Server: Analyzes the decrypted user information. Using Python's pandas and numpy libraries, the server extracts parameters necessary for product recommendations based on the user's age, height, weight, preferred design and color, and intended use.

[1616] Input: Decrypted user information data.

[1617] Data processing: Data analysis and parameter extraction (pandas, numpy).

[1618] Output: Parameters for recommendation.

[1619] Step 5: Search for products and generate a recommendation list

[1620] Server: Uses machine learning algorithms (e.g., Scikit-learn) to search a database based on user parameters. Generates a list of recommended products. For example, it might list snowboards and boots that match the user's height and weight.

[1621] Input: Parameters for recommendation.

[1622] Data processing: Search and list generation using machine learning algorithms.

[1623] Output: Recommended product list.

[1624] Step 6: Submit your recommended product

[1625] Server: Converts the generated list of recommended products into JSON format and sends it to the user's terminal.

[1626] Input: Recommended product list.

[1627] Data processing: Conversion to JSON format.

[1628] Output: A list of recommended products in JSON format.

[1629] Step 7: Display recommended products

[1630] Terminal: The terminal parses the received list of recommended products and displays it in the user interface. This display uses React or Vue.js. The displayed list includes product images, prices, features, and reviews from other users.

[1631] Input: A list of recommended products in JSON format.

[1632] Data processing: Parsing and displaying lists.

[1633] Output: Recommended product list on the user interface.

[1634] Step 8: Product Selection and Purchase Procedure

[1635] User: The user selects the product they wish to purchase from the displayed recommended products and clicks the "Add to Cart" button. They then enter their payment method and shipping information and proceed with the purchase.

[1636] Input: User's desired purchase items and payment information.

[1637] Data processing: Proceeding with the purchase procedure.

[1638] Output: Purchase confirmation data.

[1639] Step 9: Submit and confirm purchase information

[1640] Terminal and Server: The terminal sends purchase confirmation data to the server. The server stores the received purchase information in its database and checks inventory. After that, it verifies the purchase and notifies the user of the purchase completion and estimated shipping date.

[1641] Input: Purchase confirmation data.

[1642] Data processing: Database storage and inventory checks.

[1643] Output: Purchase completion notification and estimated shipping date.

[1644] The above describes the specific program processing flow in this system.

[1645] Examples of specific cases and prompt statements

[1646] Specific example

[1647] 1. The user launches the app and expresses a desire to purchase a snowboard.

[1648] 2. The user enters the following information into the input form:

[1649] Age: 25

[1650] Height: 170cm

[1651] Weight: 65kg

[1652] Preferred design: Blue or black

[1653] Usage: Freestyle

[1654] 3. The terminal encrypts the input information and sends it to the server using the HTTPS protocol.

[1655] 4. The server performs analysis to recommend appropriate snowboards and boots and generates a recommendation list.

[1656] 5. The recommendation list is sent to the device and displayed in the user interface.

[1657] 6. The user selects the product they wish to purchase from the recommended products and completes the purchase process.

[1658] 7. The server saves the purchase information, checks inventory, and notifies the user that the purchase is complete and the estimated shipping date.

[1659] Example of a prompt

[1660] For example, the prompt text to be input to the generative AI model would be as follows:

[1661] "Please recommend the best product for a user who is 170cm tall, weighs 65kg, is 25 years old, and wants a blue or black snowboard."

[1662] (Application Example 1)

[1663] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1664] The aim is to help users who are unsure which products to choose, enabling them to select appropriate products even without specialized knowledge, thereby increasing their purchasing intent. Furthermore, the goal is to improve the user experience by providing personalized recommendations to users during online shopping.

[1665] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1666] In this invention, the server includes means for securely transmitting user input information via the HTTPS protocol, means for recommending the most suitable products from a database using machine learning algorithms, and means for searching for products using prompt sentences with a generative AI model. This allows users to easily find suitable products and enhance their purchasing intent through a personalized experience.

[1667] A "user terminal" refers to a device used by a user, such as a smartphone, tablet, or computer.

[1668] The HTTPS protocol is a secure communication protocol for safely sending and receiving data over the internet.

[1669] A "machine learning algorithm" is a method that analyzes data, recognizes patterns, and derives relevant results.

[1670] A "database" is a system for organizing and managing collections of data, and it stores user information and product information.

[1671] A "generative AI model" is a model used to perform specific tasks using artificial intelligence, making predictions and recommendations based on input data.

[1672] A "prompt message" is text input to a generative AI model, and its role is to give the model instructions for performing a specific task.

[1673] A "recommended product" is a list of products that the server selects and provides based on user information analysis, ensuring the product best suits the user's needs.

[1674] An "interface" is a means by which a user and a system can interact and communicate with each other, and includes graphical user interfaces (GUIs) and command-line interfaces (CLIs).

[1675] This invention is a system that recommends optimal products based on a user's personal information and preferences, thereby increasing their willingness to purchase. The system includes a user terminal, a secure communication protocol, a server, a machine learning algorithm, a generative AI model, and a database.

[1676] Entering user information

[1677] The user launches the application using their smartphone and selects the product category they wish to purchase. Next, they enter information such as age, height, weight, preferred color and design, and intended use into an input form. For example, if a user wants to purchase outdoor equipment, they would enter information such as age 30, height 175cm, weight 70kg, preference for blue, and camping equipment.

[1678] Submitting the entered information

[1679] The user terminal securely transmits the collected input information to the server using the HTTPS protocol. The input information is encrypted before transmission, ensuring the security of the data during communication.

[1680] Receiving and analyzing user information

[1681] The server receives user input information sent from the terminal and begins analysis using a machine learning algorithm. The analysis extracts parameters to recommend the most suitable product based on information such as the user's height, weight, age, preferred design and color, and intended use.

[1682] Generate product recommendations

[1683] The server searches the database for corresponding products based on the analyzed user information. The search uses a generative AI model and prompt text to list the products best suited to the user's needs. For example, it might generate a list recommending a blue outdoor jacket, a suitable tent, and a sleeping mat.

[1684] Sending and displaying recommended products

[1685] The server sends the generated list of recommended products to the user's terminal, which then analyzes the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings.

[1686] Product selection and purchase

[1687] The user selects the items they wish to purchase from the list of recommended products and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue outdoor jacket and a matching tent to their cart, enter their payment method and shipping address, and then confirm the purchase.

[1688] Submission and confirmation of purchase information

[1689] Once the user completes the purchase process, the purchase information is sent from the device to the server. The server checks the inventory and confirms the purchase based on the received purchase information. The user is notified that the purchase is complete and the estimated shipping date.

[1690] Hardware and software to use

[1691] Smartphone: A device used by users to input information and view results.

[1692] Server: Uses Python, TensorFlow, and Firebase to perform data analysis and recommendation algorithms.

[1693] Communication protocol: Uses HTTPS to securely send and receive data.

[1694] Database: User and product information is managed using Firebase.

[1695] Examples of prompt statements

[1696] Please recommend outdoor gear for camping, for a user who is 175cm tall, weighs 70kg, likes the color blue, and is looking for products suitable for camping.

[1697] As a result, users can easily find the right products, and their purchasing intent can be increased through a personalized experience.

[1698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1699] Step 1:

[1700] The user launches the application using their smartphone. The user selects the product category they wish to purchase and enters information such as age, height, weight, preferred design and color, and intended use into the input form. This data is user information and is used for the next step.

[1701] Step 2:

[1702] The terminal encrypts the information entered by the user using the HTTPS protocol and securely sends it to the server. Specifically, the application converts the input data into JSON format and sends it to the server using an HTTPS POST request. The output of this step is encrypted user information.

[1703] Step 3:

[1704] The server receives user input information sent from the terminal. It parses the received data and extracts parameters from the JSON format. The data used here is user information, and the parsing results become the input for the next step.

[1705] Step 4:

[1706] The server uses Python and TensorFlow to apply machine learning algorithms and calculate parameters for recommending the most suitable products based on user input. Specifically, it analyzes information such as the user's height, weight, age, preferred design and color, and intended use to extract product characteristics. The output of this step is the characteristic information of the recommended products.

[1707] Step 5:

[1708] The server searches for corresponding products in its database (Firebase) based on the analyzed user information and generates prompt messages using a generative AI model. Specifically, it uses the prompt message to instruct the AI ​​model to search the database and generate an optimal product list. For example, it might generate a prompt message such as, "The user is 175cm tall, weighs 70kg, likes the color blue, and would like recommendations for outdoor equipment to use for camping." The output of this step is a list of recommended products.

[1709] Step 6:

[1710] The server sends the generated list of recommended products to the terminal. Specifically, it converts the generated product list into JSON format and sends it to the terminal using an HTTPS POST request. The output of this step is the list information of recommended products.

[1711] Step 7:

[1712] The terminal analyzes the received list of recommended products and displays it on the user interface. Specifically, it analyzes the JSON data to display product images, prices, features, and other users' ratings. The output of this step is the recommended product information displayed to the user.

[1713] Step 8:

[1714] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. Next, they proceed with the purchase process following the on-screen instructions. Specifically, this involves adding the desired items to the cart and entering payment and shipping information. The input in this step is the user's purchase preferences and payment information, while the output is information about the items added to the cart.

[1715] Step 9:

[1716] The terminal sends purchase information to the server once the user completes the purchase process. Specifically, it converts the purchase information into JSON format and sends it to the server using an HTTPS POST request. The output of this step is the sent purchase information.

[1717] Step 10:

[1718] The server checks inventory based on the received purchase information and confirms the purchase. Next, it notifies the user that the purchase is complete and the expected shipping date. Specifically, it uses the inventory management system to check the inventory status and sends a purchase confirmation email to the user. The output of this step is the purchase confirmation and the notification of the expected shipping date.

[1719] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1720] ---

[1721] This invention aims to improve the user's purchasing experience by providing a system that helps users select the most suitable products for them, and by combining it with an emotion engine that recognizes the user's emotions. This system supports users from product recommendations to the purchase process based on personal information, preferences, and emotional states provided by the user.

[1722] Specifically, this system is configured as follows:

[1723] Input of user information and sentiment information

[1724] User: The user launches the application and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use date. The user also provides the system with their current emotional state (e.g., joy, excitement, confusion) through voice and facial expressions. For example, if a user wants to buy a snowboard, they would enter information such as height 170cm, weight 65kg, and preference for blue and black designs. Furthermore, their facial expressions and voice would be collected in real-time by the emotion engine via camera and microphone.

[1725] Information transmission

[1726] Terminal: User input information and sentiment information are sent to the server using a secure communication protocol (e.g., HTTPS). User information and sentiment information are encrypted before transmission, ensuring the security of the data during transmission.

[1727] Information reception and analysis

[1728] Server: The server receives user information and emotional information sent from the terminal and begins analysis. The analysis evaluates the user's height, weight, age, preferred design and color, intended use, and emotional state, and extracts parameters to recommend the most suitable product based on this information. For example, if the user is excited, the server will select recommended products that reflect that emotion.

[1729] Generate product recommendations

[1730] Server: Based on the analysis results, it searches the database for corresponding products. The search uses machine learning algorithms and filtering techniques to list products that best suit the user's needs and emotional state. For example, if the user is excited, it might focus on recommending colorful and stylish snowboards and boots.

[1731] Sending and displaying recommended products

[1732] Server and Terminal: The server sends the generated list of recommended products to the terminal, which then parses the received list and displays it on the user interface. The displayed list includes product images, prices, features, and other users' ratings. For example, images and detailed information of a blue or black snowboard and boots in the appropriate size might be displayed on the user's terminal.

[1733] Product selection and purchase

[1734] User: The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. They then proceed with the purchase process following the on-screen instructions. For example, a user might add a blue snowboard and matching boots to their cart, enter their payment method and shipping address, and then confirm the purchase.

[1735] Submission and confirmation of purchase information

[1736] Terminal and Server: Once the user completes the purchase process, the purchase information is sent from the terminal to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date.

[1737] This invention enables product recommendations that reflect user emotions in real time, thereby increasing user purchasing intent. Furthermore, users can select appropriate products without requiring specialized knowledge, providing a seamless online shopping experience.

[1738] ---

[1739] The following describes the processing flow.

[1740] ---

[1741] Step 1:

[1742] User: The user launches the app and selects the product category they wish to purchase. They then enter information such as age, height, weight, preferred design and color, and intended use. Additionally, they provide facial expressions and voice to the system in real time via camera and microphone, allowing the emotion engine to recognize the user's emotional state.

[1743] Specific actions:

[1744] Launch the app and select a product category (for example, snowboards).

[1745] Enter your age (e.g., 25 years old), height (e.g., 170cm), weight (e.g., 65kg), preferences (e.g., blue or black design), and planned date of use in the input form.

[1746] Enable the camera and microphone to send facial expressions and voices to the emotion engine in real time.

[1747] Step 2:

[1748] Terminal: Sends user input information and sentiment information to the server using a secure communication protocol (e.g., HTTPS).

[1749] Specific actions:

[1750] After entering the information, press the "Next" or "Submit" button.

[1751] Input information and emotional information are encrypted and sent to the server.

[1752] Step 3:

[1753] Server: Receives user information and sentiment information sent from the terminal and begins analysis.

[1754] Specific actions:

[1755] The system decodes the received data and analyzes it to determine age, height, weight, preferred designs and colors, intended use, emotional state, and more.

[1756] Based on this information, we extract the parameters used to search for recommended products.

[1757] Step 4:

[1758] Server: Based on the analysis results, it searches the database for the most suitable products and generates a list of recommended products that reflect the emotional state.

[1759] Specific actions:

[1760] A query is sent to the database to search for products that match the user's parameters and emotional state.

[1761] We generate the optimal recommended product list, taking into account product popularity and ratings.

[1762] For example, if a user is excited, we might recommend products with vibrant designs.

[1763] Step 5:

[1764] Server: Sends the generated list of recommended products to the terminal.

[1765] Specific actions:

[1766] Convert the recommended product list into JSON format and send it to the user's terminal.

[1767] Recommended products include product images, prices, features, and reviews from other users.

[1768] Step 6:

[1769] Terminal: Analyzes the received list of recommended products and displays it on the user interface.

[1770] Specific actions:

[1771] The system parses JSON data and converts it into a layout to display product images, prices, and features.

[1772] The product details will be displayed, and each product will have buttons such as "View Details" and "Add to Cart."

[1773] Step 7:

[1774] User: Select the product you wish to purchase from the list of recommended products and proceed with the purchase process.

[1775] Specific actions:

[1776] The user taps on the product to view details and then presses the "Add to Cart" button.

[1777] Proceed to the checkout screen and enter your shipping address, payment method, and other information.

[1778] Step 8:

[1779] Terminal: When a user completes the purchase process, it sends that information to the server.

[1780] Specific actions:

[1781] When you press the purchase confirmation button, your purchase information (product ID, quantity, shipping address, payment information, etc.) will be encrypted and sent to the server.

[1782] Step 9:

[1783] Server: Based on the received purchase information, the server checks inventory and confirms the purchase, then initiates the shipping process.

[1784] Specific actions:

[1785] It integrates with the inventory system to check the stock of the selected product.

[1786] Once the purchase is confirmed, we begin preparing the item for shipment and send a notification to the user stating, "Your purchase is complete."

[1787] We will notify you of information such as the expected shipping date and tracking number.

[1788] ---

[1789] The above outlines the specific processing steps of the system that incorporates the emotion engine. This process allows users to select the optimal product according to their emotional state and proceed with the purchase smoothly.

[1790] (Example 2)

[1791] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1792] Traditional online shopping systems mostly recommended products based on users' personal information, without considering their emotional state. Therefore, they were unable to provide product recommendations that reflected users' real-time emotional states, failing to adequately enhance user satisfaction and purchasing intent. Furthermore, traditional systems lacked the means to assist users in selecting the optimal product, even if they lacked specialized knowledge.

[1793] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input personal information and emotional information, means for transmitting the user's personal information and emotional information to the server, and means for the server to analyze the user's personal information and emotional information and recommend corresponding products. This makes it possible to recommend products that take into account not only the user's personal information but also real-time emotional information. This is expected to improve user satisfaction and purchasing intent, and enable users to choose appropriate products even without specialized knowledge.

[1794] ---

[1795] A "user" refers to an individual or group that uses this system to search for, select, and purchase products.

[1796] "Personal information" refers to information that can identify a user, such as the user's age, height, weight, preferred design and color, and planned usage date.

[1797] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) obtained by analyzing the user's facial expressions, voice, etc.

[1798] A "server" refers to a central processing unit that receives and analyzes users' personal and emotional information and recommends corresponding products.

[1799] A "terminal" refers to a device used by a user to input personal and emotional information and send it to a server.

[1800] A "database" refers to an information storage system that stores user and product information and allows it to be searched and retrieved as needed.

[1801] An "algorithm" refers to a set of computational procedures and rules used to recommend the most suitable products based on a user's personal and emotional information.

[1802] "Interface" refers to the screens and operating methods that users use to interact with a system, and includes GUIs (Graphical User Interfaces) used for selecting products and proceeding with purchase procedures.

[1803] "Recommended products" refer to products that the system recommends based on the analysis of the user's personal information and emotional data.

[1804] ---

[1805] This invention aims to improve the user's purchasing experience by providing a system that helps users select the most suitable products for them, and by combining it with an emotion engine that recognizes the user's emotions. This system supports users from product recommendations to the purchase process based on personal information, preferences, and emotional states provided by the user.

[1806] Input of user information and sentiment information

[1807] The user launches the application and selects the product category they wish to purchase. They then enter personal information such as age, height, weight, preferred design and color, and intended use date. Furthermore, the user provides real-time emotional states to the system using a camera and microphone. Facial recognition software and speech recognition software are used to input emotional states. Specific software examples include general facial recognition APIs for facial recognition and speech-to-text APIs for speech recognition.

[1808] Information transmission

[1809] The terminal transmits the entered user information and sentiment information to the server using a secure communication protocol. Before transmission, the information is encrypted using protocols such as TLS to ensure the security of the data during transmission.

[1810] Information reception and analysis

[1811] The server receives user information and sentiment information sent from the terminal and stores it in a database. Next, it analyzes this information using an AI engine. Specifically, machine learning platforms and models are used as the AI ​​engine. It analyzes the user's personal information and sentiment state and extracts parameters to recommend the most suitable products based on that analysis.

[1812] Generate product recommendations

[1813] The server uses the analysis results to search for the most suitable product from its product database. This search utilizes machine learning algorithms and filtering techniques. For example, a recommendation engine is used to list products that best suit the user's preferences and emotional state.

[1814] Sending and displaying recommended products

[1815] The server sends the generated list of recommended products to the user's terminal, which receives the list and displays it in the user interface. The user interface is implemented using a graphical user interface framework. This display includes product images, prices, features, and other users' ratings.

[1816] Product selection and purchase

[1817] The user selects the desired product from the list of recommended items and clicks the "Add to Cart" button. A screen then appears to proceed with the purchase. This screen includes fields for entering payment method and shipping address. A payment service API is used for payment processing.

[1818] Submission and confirmation of purchase information

[1819] Once a user completes the purchase process, the purchase information is sent from the device to the server. The server checks inventory based on the received purchase information and confirms the purchase. The user is notified that the purchase is complete and the estimated shipping date. Notifications are sent via email or in-app notifications.

[1820] Specific example

[1821] Below are specific scenarios and examples of input prompts for the generated AI model, designed to help visualize how this system works.

[1822] Example Scenario

[1823] User A has decided to go snowboarding this weekend and wants to buy a new snowboard. User A has decided to use the system to purchase a snowboard.

[1824] 1. Person A launches the app and selects "Snowboarding".

[1825] 2. Enter the required personal information (height 170cm, weight 65kg, preferred colors are blue and black).

[1826] 3. Smile at the camera and tell the microphone, "I'm excited."

[1827] 4. The device sends this information to the server.

[1828] 5. The server analyzes the information and searches for the most suitable product.

[1829] 6. A list of recommended snowboards and boots with blue or black designs will be displayed on the device.

[1830] 7. Person A selects their favorite item, adds it to their cart, and confirms the purchase.

[1831] Input prompts for the generative AI model

[1832] "If a user launches the app to buy a snowboard, enters their height as 170cm, weight as 65kg, preferred colors as blue and black, smiles at the camera, and says into the microphone that they are excited, what products would be recommended?"

[1833] This system enables product recommendations that reflect user emotions in real time, which is expected to improve the purchasing experience. Users will be able to easily select the right product even without specialized knowledge.

[1834] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1835] ---

[1836] Step 1: Enter user information and sentiment information.

[1837] User: Launch the application and select the product category you wish to purchase. Next, enter personal information such as age, height, weight, preferred design and color, and intended use date. You will also input facial expressions and voice via camera and microphone to provide your current emotional state (e.g., joy, excitement, confusion, etc.).

[1838] Input: Age, height, weight, preferred design and color, planned date of use, facial expression, voice

[1839] Output: Personal and emotional data

[1840] Specific actions: If a user wants to buy a snowboard, they will enter information such as their height (170cm), weight (65kg), and preference for blue and black designs. They will also smile in front of the camera and say into the microphone, "I'm excited."

[1841] Step 2: Sending Information

[1842] Terminal: Sends entered personal and emotional information to the server using a secure communication protocol (e.g., HTTPS). The information is encrypted before transmission.

[1843] Input: Personal information and emotional information

[1844] Output: Data packets containing encrypted personal and emotional information

[1845] Specific operation: The entered information is encrypted using the TLS protocol and sent to the server via HTTPS.

[1846] Step 3: Information reception and analysis

[1847] Server: Receives personal and emotional information sent from terminals and stores it in a database. Then, an AI engine is used to analyze this data.

[1848] Input: Encrypted personal information and emotional information

[1849] Output: Data showing the analyzed user's preferences and emotional state.

[1850] Specific operation: Received information is stored in a database, and an AI engine (e.g., a machine learning platform) is used to analyze the user's height, weight, age, preferred designs and colors, and emotional state.

[1851] Step 4: Generating Product Recommendations

[1852] Server: Based on the analysis results, it searches the database for the most suitable product. It uses machine learning algorithms and filtering techniques.

[1853] Input: Analyzed user preferences and emotional state

[1854] Output: List of recommended products

[1855] Specific operation: Use a recommendation engine to list the most suitable products, taking into account the user's age, preferences, and emotional state. For example, select products with brightly colored designs for an excited user.

[1856] Step 5: Submit and display recommended products

[1857] Server and Terminal: The server sends the generated list of recommended products to the terminal, and the terminal displays the received list in the user interface.

[1858] Input: List of recommended contests

[1859] Output: Product information displayed to the user

[1860] Specific operation: The list includes product images, prices, features, and other users' ratings, and is displayed on the user's device.

[1861] Step 6: Product Selection and Purchase

[1862] User: Select the product you want to purchase from the list of recommended products and click the "Add to Cart" button. Then, proceed with the purchase process, enter your payment method and shipping address, and confirm your purchase.

[1863] Input: Selected product information, payment information, shipping address information

[1864] Output: Final purchase information

[1865] Specific action: The user adds a blue snowboard and matching boots to their cart, enters payment information and shipping address, and confirms the purchase.

[1866] Step 7: Submit and confirm purchase information

[1867] Terminal and Server: Purchase information is sent from the terminal to the server, which checks inventory and confirms the purchase. The user is notified of the purchase completion and the estimated shipping date.

[1868] Input: Purchase Information

[1869] Output: Purchase confirmation information, estimated shipping date

[1870] Specific operation: The server checks inventory and sends a notification along with purchase confirmation. The user is notified of the expected shipping date via email or in-app notification.

[1871] ---

[1872] This system's processing steps enable product recommendations based on personal and emotional information, thereby improving the user experience.

[1873] (Application Example 2)

[1874] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1875] Current online shopping systems commonly recommend products based on users' personal information and purchase history. However, these systems recommend products based on uniform criteria without considering the user's emotional state, resulting in a limited user experience. There is a problem in providing products that users are actually interested in or that match their mood at that moment. This can increase the effort required for users to find what they truly want, potentially diminishing their desire to purchase.

[1876] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing personal information and emotional information entered by the user, means including a generative AI model that searches a database based on the analysis results and recommends the most suitable product, and means for transmitting the recommended product information to the user terminal. This makes it possible to provide personalized product recommendations that take into account the user's emotional state.

[1877] "User personal information" refers to information that can be used to identify or distinguish a user, such as age, height, weight, preferred design and color, and planned usage date.

[1878] "Emotional information" refers to information that indicates the user's emotional state (e.g., joy, excitement, confusion) based on the user's voice, facial expressions, and actions, and is collected in real time.

[1879] A "server" is a centralized computing system that receives and analyzes information sent by users and recommends products.

[1880] "Means of analysis" refers to the process by which the server uses an analysis algorithm to decipher the user's personal information and emotional information received, and selects the most suitable product for the user.

[1881] A "generative AI model" is a computational model that uses machine learning techniques to recommend the most suitable products based on a user's personal information and emotional data.

[1882] A "user terminal" refers to an electronic device, such as a smartphone, tablet, or PC, that a user uses to communicate with a server.

[1883] "Recommended methods" refer to a mechanism that uses analysis results and generated AI models to appropriately select products based on user information.

[1884] "Means of transmission" refers to the communication protocols and technologies used to deliver product information analyzed and selected by the server to the user's terminal.

[1885] Modes for carrying out the invention

[1886] This invention relates to a system that recommends optimal products based on personal information and emotional information entered by the user. Specifically, it aims to improve the purchasing experience by analyzing the user's emotional state in real time and providing personalized product recommendations based on the user's current mood and preferences. The following describes in detail a specific embodiment of this system.

[1887] composition

[1888] hardware

[1889] User terminal: An electronic device such as a smartphone, tablet, or PC that a user uses to input information and communicate with a server.

[1890] Server: A centralized computing system that receives, analyzes, and recommends products based on information sent from user terminals.

[1891] software

[1892] Frontend: We will utilize React Native (a mobile application development framework) to provide an interface that makes it easy for users to input information.

[1893] Backend: Node.js (server-side environment), Express (web framework), MongoDB (database), and TensorFlow.js (machine learning library) are used to analyze incoming data.

[1894] Operation overview

[1895] Collection of user information and sentiment information

[1896] Users launch the application using their smartphones or tablets and input their personal information, preferred designs, colors, and intended use, as well as real-time facial expressions and voice recordings via the camera and microphone. This allows the system to collect data on the user's emotional state.

[1897] Information transmission

[1898] Personal and emotional information transmitted from the user's terminal is securely sent to the server via the HTTPS protocol. This ensures the security of the data during transmission.

[1899] Information analysis

[1900] The server uses TensorFlow.js to analyze the personal and emotional information of the users it receives. The analyzed emotional data represents the user's emotional state (e.g., joy, excitement, confusion) as numerical data.

[1901] Generate product recommendations

[1902] Based on the analysis results, the server searches the MongoDB database for products that match the user's preferences and emotional state. Using a generative AI model, the most suitable products are recommended.

[1903] Sending and displaying recommended products

[1904] The server generates a list of recommended products, which is then sent to the user's terminal. The user's terminal then displays images, prices, features, and user reviews of the recommended products.

[1905] Product selection and purchase

[1906] The user selects the desired product from the displayed recommended items, adds it to their cart, and proceeds with the purchase. Following the user interface, they enter their payment method and shipping address and confirm the purchase.

[1907] Specific example

[1908] For example, if a user wants to buy a "snowboard," they might enter the following information:

[1909] Example of a prompt

[1910] Age: 28

[1911] Height: 170cm

[1912] Weight: 65kg

[1913] Favorite colors: Blue, Black

[1914] Preferred design: Simple

[1915] Usage: Snowboarding

[1916] Emotional information: Joy, Trust level 90%

[1917] Based on this input information, the system analyzes the user's emotional state and recommends snowboards with bright colors and stylish designs, taking into account that the user is experiencing "joy." In this way, personalized product recommendations that reflect the user's emotional state are achieved.

[1918] The above describes the detailed configuration for carrying out the invention. This system allows users to select the optimal product without specialized knowledge and obtain a seamless purchasing experience.

[1919] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1920] Program processing flow

[1921] Step 1:

[1922] Users launch the application using their smartphones or tablets and enter personal and emotional information. They enter their age, height, weight, preferred design and color, and intended use into a form. Simultaneously, they use their camera and microphone to input real-time facial expressions and voice into the application. This entered information is then passed directly to the next processing step.

[1923] Step 2:

[1924] The user terminal transmits entered personal and emotional information to the server using the HTTPS protocol. Before transmission, this data is encrypted to ensure the security of the data during transmission. Input data consists of personal and emotional information, and output data is encrypted.

[1925] Step 3:

[1926] The server analyzes personal and emotional information received from the user's terminal. The received data is fed into an analysis algorithm to evaluate the user's height, weight, preferences, and emotional state. Emotional analysis is performed using TensorFlow.js, extracting the type of emotion (e.g., joy, excitement, confusion) and its confidence level as numerical values. The input data consists of received personal and emotional information, while the output data is the analysis results (numerical data of the user's preferences and emotional state).

[1927] Step 4:

[1928] The server performs a database search based on the analysis results. It queries the MongoDB database to find products that match the user's preferences and emotional state. Using a generative AI model, it further filters the search results to identify the most suitable products. The input data consists of the analysis results and product data in the database, and the output data is a list of recommended products.

[1929] Step 5:

[1930] The server generates a list of optimal products and sends it back to the user's terminal via HTTPS. The data sent is a list of recommended products, and the user's terminal receives this data.

[1931] Step 6:

[1932] The user terminal analyzes the received list of recommended products and displays it on the user interface. It displays product images, prices, features, and other users' ratings to make selection easier for the user. The input data is the received list of recommended products, and the output data is the product information displayed on the screen.

[1933] Step 7:

[1934] The user selects the desired product from the recommended products displayed on the screen and adds it to their cart. They then proceed to the checkout screen, where they enter their payment method and shipping address, and click the "Confirm Purchase" button. The input data consists of the user's selected products and purchase information, while the output data is the purchase confirmation information sent to the server.

[1935] Step 8:

[1936] The server receives information that the user has completed the purchase process and checks the inventory. If inventory is available, it confirms the purchase and begins preparing for shipment. The user is notified that the purchase is complete and the estimated shipping date. The input data is the confirmed purchase information, and the output data is the purchase confirmation and estimated shipping date notification.

[1937] In this way, personalized product recommendations and a seamless purchasing experience are realized based on the user's personal and emotional information.

[1938] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1939] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1940] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1941] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1942] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1943] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1944] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1945] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1946] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1947] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1948] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1949] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1950] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1951] 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.

[1952] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1953] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1954] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1955] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1956] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1957] The descriptions and illustrations presented above are detailed explanations of the technical asp...

Claims

1. Means of collecting information entered by the user, A means of sending user information to the server, A means by which the server analyzes user information and recommends corresponding products, A means of sending recommended product information to the user's terminal, A means of displaying recommended products on the user's terminal, The means by which users select products and proceed with the purchase process, A system that includes this.

2. The system according to claim 1, wherein the server includes means for an algorithm that searches a database based on user information and recommends the most suitable product.

3. The system according to claim 1, wherein the user terminal displays detailed information of recommended products and provides an interface for the user to add products to a cart and proceed with the purchase.

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