system

Abstract

Provide a system. A means for a user to enter authentication information to log into an account; means for transmitting the authentication information to a server to perform user authentication; A way for authenticated users to order products in a natural conversational way, natural language processing means for analyzing the user's order and generating an appropriate response; A means of making product suggestions to users; means for displaying a list of suggested products on a screen; A means for the user to order and finalize the selected products; The system includes a means for transmitting the order details after final confirmation to the mail order company and completing the order.

Description

[Technical Field]

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

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

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

[0004] While online shopping is a highly convenient option for many people, the ordering process is often complicated and difficult to understand, especially for elderly people and users with low digital literacy. As a result, these users tend to avoid online shopping and may not be able to enjoy its convenience. The objective of this invention is to improve convenience by providing a system that allows such users to easily order products while interacting with AI in a natural conversational format. [Means for solving the problem]

[0005] The present invention is a system that includes the following means: a means for a user to input authentication information to log in to an account, a means for transmitting the authentication information to a server to authenticate the user, a means for prompting the authenticated user to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying a list of suggested products on a screen, a means for the user to order the products selected by the user and make a final confirmation, and a means for transmitting the order details after final confirmation to a mail-order company to complete the order. This system enables users, especially elderly people and those who are not good at online shopping, to order products intuitively and easily.

[0006] "User authentication" is the process of using the authentication information (such as username and password) entered by a user to verify that the user is a legitimate person authorized to access a system.

[0007] A "natural conversational style" is an interface in which interactions are conducted in a format similar to normal human conversation, allowing users to post questions and instructions without feeling awkward.

[0008] "Natural language processing" is a technology that allows computers to understand, analyze, and generate responses to human language (natural language).

[0009] "Product suggestion" is the process of selecting and presenting available products to users based on their requests and past purchase history.

[0010] "Final confirmation" is a process for the user to confirm the order details for the products selected by the user and finally confirm the order.

[0011] "Mail Order Company" means an external service provider that receives order information confirmed by the User and delivers the specified products and performs other services.

[0012] "Terminal" means a device used by a user to interact with the system, including a smartphone, tablet, or PC.

[0013] "Server" refers to the central computer system that performs the main processing of the system, such as user authentication, natural language processing, product suggestion generation, final order confirmation, and order data transmission.

[0014] The "order process" refers to the series of operations and processes that a user goes through from selecting a product to finalizing the purchase. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0037] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, and a means for the user to confirm the products selected by the user and send them to the mail order company.

[0038] User authentication

[0039] The device first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0040] Natural conversation generation

[0041] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once the entry is complete, the device sends the input to the server.

[0042] Input analysis and response generation

[0043] The server analyzes the received text and uses natural language processing to generate an appropriate response, such as a specific question or suggestion like "What brand of milk would you like?" This response is then sent back to the device and used as the display for the user.

[0044] Generate product suggestions

[0045] The server generates a list of products based on the user's request, taking into account their purchasing history and preferences. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the terminal and displayed visually to the user.

[0046] View product suggestions

[0047] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0048] Final confirmation and submission of order

[0049] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0050] This system allows even elderly people and users with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the natural conversational interface allows users to operate the system without any discomfort.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[0054] Step 2:

[0055] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[0056] Step 3:

[0057] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[0058] Step 4:

[0059] The terminal sends the user's input to the server, which receives the input and analyzes it using natural language processing means.

[0060] Step 5:

[0061] The server analyzes the input and generates an appropriate response, such as a question like "What brand of milk would you like?", and sends the response to the device.

[0062] Step 6:

[0063] The device displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further input.

[0064] Step 7:

[0065] The server generates product suggestions based on detailed input from the user, taking into account the user's past purchase history and preferences to create an appropriate product list.

[0066] Step 8:

[0067] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[0068] Step 9:

[0069] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[0070] Step 10:

[0071] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[0072] Step 11:

[0073] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[0074] Step 12:

[0075] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] For elderly people and users with low digital literacy, online shopping is often complicated and difficult to operate. In particular, if appropriate options are not provided during the product selection and ordering process, there is a high risk of selecting the wrong product. Conventional online shopping systems have complex interfaces that make them difficult for users to use.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server and performing user authentication; means for prompting the authenticated user to order products in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and for final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for suggesting products based on the user's past purchase history and preferences; means for displaying the response generated by the natural language processing means to the user; and means for analyzing the user's input and generating prompt sentences using a generative AI model. This allows users to operate the system intuitively and easily, making online shopping smooth for even elderly people and users with low digital literacy.

[0081] "Authentication Information" refers to the username and password you enter to log into your account.

[0082] "Server" refers to a computer system for processing and storing data.

[0083] "Terminal" refers to devices used by users, such as smartphones, tablets, and computers.

[0084] "User" refers to a person who uses this system to shop online.

[0085] "Natural language processing" refers to the technology of using a computer to analyze human language and generate appropriate responses.

[0086] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate and analyze natural language.

[0087] "Prompt sentence" refers to the text data input into a generative AI model.

[0088] "User authentication" refers to the process of verifying that a user has a valid account.

[0089] "Order Details" refers to the list of products that the User wishes to purchase and their detailed information.

[0090] A "mail order company" refers to a company that receives orders from users and sells products.

[0091] "Product Suggestions" refers to a list of products suggested to users based on their purchasing history and preferences.

[0092] "Final confirmation" refers to the process of final confirmation of the product selected by the user.

[0093] "Purchase history" refers to a record of products a user has purchased in the past.

[0094] "Preferences" refers to a user's tendency to prefer certain products or brands.

[0095] "Text input" refers to character data that a user inputs using a keyboard or touch panel.

[0096] "Response" refers to the appropriate response that a generative AI model gives in response to user input.

[0097] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0098] Hardware and Software

[0099] The hardware used includes the devices operated by users (smartphones, tablets, PCs, etc.) and the servers (cloud servers or physical servers) that process and store data. The software includes applications that make up the user interface, generative AI models for natural language processing (e.g., GPT-3 (registered trademark)), and APIs for linking with mail-order companies.

[0100] User authentication

[0101] The terminal first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the authentication information to the server. For example, if the username "user123" and password "password123" are entered, the server compares this information with its database for authentication. If authentication is successful, the server returns a "Login successful" status code and a session ID to the terminal.

[0102] Natural conversation generation

[0103] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once this input is complete, the device sends the input to the server.

[0104] Input analysis and response generation

[0105] The server analyzes the received text using natural language processing tools. It uses a generative AI model (e.g., GPT-3) to understand the user's intent and generate an appropriate response. For example, it might generate a question like, "What brand of milk would you like?" and send it to the device.

[0106] Generate and display product suggestions

[0107] The server takes into account the user's purchase history and preferences to suggest appropriate products. For example, it generates a suggestion such as, "We have this brand of milk. Would you like it?" and sends it to the device. The device then displays a list of suggested products on the screen so that the user can visually check them. The user can then select the desired product from the displayed products.

[0108] Final confirmation and submission of order

[0109] After the user selects the product they wish to purchase, a screen for final confirmation will be displayed on the terminal. This screen displays details of the selected product and a "Confirm Order" button. When the user makes their final confirmation and presses the "Confirm Order" button, the terminal will send the order details to the server. The server will then send this order details to the mail order company and complete the order process.

[0110] Use of concrete examples and prompts

[0111] Specific examples of prompt sentences are shown below.

[0112] User input: "I want to order milk."

[0113] Prompt the AI ​​model: "The user has typed 'I would like to order milk.' Next, ask related questions to confirm the specific brand and quantity."

[0114] The prompt allows the generative AI model to ask appropriate questions or make suggestions based on the user's input, such as "What brand of milk would you like?" or "How much do you need?", and display these responses to the user.

[0115] As described above, this system is designed to enable users to shop online with an intuitive and natural interface, and is particularly notable for being easy to operate even for elderly people and users with low digital literacy.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1: User authentication

[0118] The terminal displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the entered authentication information (username "user123" and password "password123") to the server. The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it generates a "login successful" status code and a session ID and sends them back to the terminal.

[0119] Input: Username, Password

[0120] Data processing: Comparison with database

[0121] Output: "Login successful" status code and session ID

[0122] Step 2: Start a conversation

[0123] After authentication is complete, the device displays a text entry field and allows the user to begin the product ordering conversation. The entry field appears with the message "Please enter the product you would like to order." The user enters the specific order details in text format, such as "I would like to order milk." Once the entry is complete, the device sends the text content to the server.

[0124] Input: User's order text (e.g. "I'd like to order milk")

[0125] Data processing: Sending text data

[0126] Output: Send text data to the server

[0127] Step 3: Parsing input and generating response

[0128] The server analyzes the received text using natural language processing. A generative AI model (e.g., GPT-3) is used to understand the intent of the user's input and generate an appropriate response. In this process, a prompt sentence is input into the AI ​​model for analysis. For example, a prompt sentence could be, "The user inputs 'I would like to order milk.' Please ask related questions next to confirm the specific brand and quantity." The server generates a generated response, "What brand of milk would you like?", and sends it to the device.

[0129] Input: User text input, prompt

[0130] Data processing: natural language processing, intent analysis

[0131] Output: The generated response (e.g., "What brand of milk would you like?")

[0132] Step 4: Generate and display product suggestions

[0133] The server suggests appropriate products by taking into account the user's past purchase history and preferences. It references a purchase history database to identify brands of milk previously purchased. The server generates a product list based on this information, creating suggestions such as "We have these brands of milk: Brand A, Brand B, Brand C." This product list is sent to the terminal and displayed on the screen so that the user can visually confirm it. The user selects the desired product from the displayed products.

[0134] Input: User's past purchase history, preferences

[0135] Data processing: Analysis of purchase history, creation of product list

[0136] Output: Product suggestion list

[0137] Step 5: Finalize and submit your order

[0138] After the user selects the product they wish to purchase, the terminal displays a final confirmation screen. This screen displays details of the selected product and includes a "Confirm Order" button. When the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company's API, completing the order process.

[0139] Input: Product selected by user, press of final confirmation button

[0140] Data processing: Sending order details, calling API

[0141] Output: Order processing completed

[0142] The above specific processing steps make it easy for even elderly people and users with low digital literacy to shop online.

[0143] (Application example 1)

[0144] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0145] Conventional online shopping systems have the problem that the ordering process is complicated and difficult to use for elderly people and users with low digital literacy. Furthermore, product selection and suggestions are standardized, making it difficult to flexibly respond to user preferences. In particular, the lack of a dialogue-based ordering system using natural language has left many users feeling uneasy about operation and leading to operational errors.

[0146] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0147] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order products in a natural conversational format; means for analyzing the user's order details and generating a response using a generative AI model; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected and make a final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for the natural language processing means to generate a prompt sentence according to the order details using the generative AI model; and means for displaying a prioritized list of suggested products. This enables even elderly people and users with low digital literacy to easily shop online in a natural conversational format.

[0148] A "means for users to enter authentication information to log in to their account" means providing an interface for users to enter the authentication information (such as username and password) required to access their account.

[0149] The "means for transmitting the authentication information to a server and performing user authentication" means transmitting the input authentication information to a server, and having the server verify the authenticity of the user based on that information.

[0150] "Means for encouraging authenticated users to order products in a natural conversational manner" means allowing a user who has successfully logged in to begin the process of ordering products in a natural language conversational manner.

[0151] "Means for analyzing the user's order and generating a response using a generative AI model" refers to means for analyzing the order entered by the user and generating an appropriate response using a generative AI model (such as GPT-3).

[0152] "Means for suggesting products to users" refers to means for suggesting appropriate products and services to users based on the user's input and past purchase history.

[0153] The "means for displaying a list of proposed products on a screen" refers to a means for displaying the proposed products and services on a display screen so that the proposed products and services can be visually confirmed.

[0154] "Means for ordering the products selected by the user and making final confirmation" refers to the means for the user to make final confirmation of the products or services selected and confirm the order.

[0155] "Means for sending the order details after final confirmation to the mail order company and completing the order" refers to the means by which the user sends the order details that have been finally confirmed to the mail order company and completes the processing of the order.

[0156] "Means for the natural language processing means to use a generative AI model to generate a prompt sentence according to the order content" refers to means for analyzing the user's order content and then using a generative AI model to generate an appropriate prompt sentence.

[0157] "Means for prioritizing and displaying a list of suggested products" refers to means for ranking suggested products or services based on importance or relevance and displaying them on the screen in that order.

[0158] The present invention relates to a system that allows users to conduct online shopping in a natural, conversational style. This system provides an interface that is easy for users to operate and is designed to be easy to use, especially for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0159] User authentication

[0160] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0161] Order in a natural conversational style

[0162] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order sushi." Once the entry is complete, the device sends the input to the server.

[0163] Input analysis and response generation

[0164] The server analyzes the received text and uses a generative AI model (such as GPT-3) to generate an appropriate response, such as a specific question or suggestion like, "What kind of sushi would you like?" This response is then sent back to the device and used as the display content for the user.

[0165] Generate product suggestions

[0166] The server suggests appropriate products in response to a user's request, taking into account their purchase history and preferences. For example, it generates a list of products such as "How about this vegetarian sushi?" based on the user's past orders and preferences. This list is sent to the device and displayed visually to the user. The list is also prioritized and displayed.

[0167] View product suggestions

[0168] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0169] Final confirmation and submission of order

[0170] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0171] Hardware and software used

[0172] The system is implemented using the following hardware and software.

[0173] Hardware: End devices such as smartphones, tablets, and PCs.

[0174] Software: Python, natural language processing libraries, OpenAI® GPT-3 API.

[0175] Examples of prompt statements

[0176] Examples of prompts to input to a generative AI model:

[0177] 1. Generate a response to a user's order entry:

[0178] User input: "I want to order sushi"

[0179] GPT-3 prompt: "What kind of sushi would you like? Typical options include tuna, salmon, eel, and California rolls."

[0180] 2. Product proposal generation:

[0181] User preferences: "Likes Japanese food, prefers vegetarian options"

[0182] Purchase history: "Ordered tempura, tofu salad, vegetable sushi"

[0183] GPT-3 prompt: "Based on the user's preferences and purchase history, suggest items. Consider Japanese vegetarian options."

[0184] This system allows even elderly people and those with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the use of generative AI models allows users to operate the system seamlessly and receive more personalized service.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1: User authentication

[0187] The user enters their username and password into the login screen of their smartphone or tablet. This provides the user with authentication information to access the system. The device sends the entered authentication information to the server. The server verifies the received authentication information and authenticates the user. If authentication is successful, the server sends a message of successful authentication to the device, and the device proceeds to the next step.

[0188] Input: Username and Password

[0189] Output: Authentication success message

[0190] Step 2: Natural conversational order prompts

[0191] The device presents the authenticated user with a text entry field, prompting them to enter details of the items they wish to order. The user enters their order in text form and sends a specific request, such as "I would like to order sushi," to the server.

[0192] Input: User-entered text for the order

[0193] Output: Text data sent to the server

[0194] Step 3: Parsing input and generating response

[0195] The server analyzes the received text data and generates an appropriate response using a generative AI model (e.g., GPT-3). Specifically, the server generates prompts to ask specific questions or make suggestions, such as "What kind of sushi would you like?", and then uses the generative AI model to generate a response based on the prompt. This response is then sent back to the device and displayed to the user.

[0196] Input: Text data of the user's order details

[0197] Output: Response text generated by the GPT-3 model

[0198] Step 4: Generate product suggestions

[0199] The server uses a generative AI model to make appropriate product suggestions, taking into account the user's order details, purchase history, and preferences. For example, it might generate a list of specific suggestions, such as "How about this vegetarian sushi?" This list is prioritized and sent to the device.

[0200] Input: Order details, purchase history, user preferences

[0201] Output: Text data of the suggested product list

[0202] Step 5: View product suggestions

[0203] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and the user can select the products they wish to purchase from the displayed products.

[0204] Input: Text data of the suggested product list

[0205] Output: Product list displayed on screen

[0206] Step 6: Finalize your order

[0207] The terminal displays the details of the product selected by the user and prompts for final confirmation. After the user confirms the details and presses the "Confirm Order" button, the terminal sends the order details to the server.

[0208] Input: The product selected by the user

[0209] Output: Final confirmation screen and order confirmation

[0210] Step 7: Submit and complete your order

[0211] The server receives the final order details sent from the terminal and sends them to the mail order company. Once the mail order company has completed the order processing, the server sends a message to the terminal notifying the user that the order was successful.

[0212] Input: Final confirmed order details

[0213] Output: Order successful message

[0214] This ensures that users' orders are processed reliably at each step, making it easy for even elderly people and users with low digital literacy to enjoy online shopping.

[0215] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0216] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[0217] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, a means for the user to finally confirm the products selected by the user and send them to the mail-order company, and an emotion engine that recognizes the user's emotions.

[0218] User authentication

[0219] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends this authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0220] Natural conversation generation

[0221] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the input to the server.

[0222] Input analysis and response generation

[0223] The server analyzes the received text and generates an appropriate response using natural language processing and an emotion engine. For example, it may ask specific questions or make suggestions, such as "What brand of milk would you like?" If the emotion engine recognizes the user's emotion, it adjusts the tone and content of the response. This response is then sent back to the device and used as the display content for the user.

[0224] Generate product suggestions

[0225] In response to a user's request, the server suggests appropriate products, taking into account the user's purchase history and preferences. The emotion engine also takes into account information such as the emotional state the user was in when purchasing a particular product. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[0226] View product suggestions

[0227] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0228] Final confirmation and submission of order

[0229] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0230] Specific examples

[0231] For example, if a user types, "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest quick-cooking meals and easy-to-prepare kits. The tone of the response is also considered, and the suggestions are presented in a friendly format, such as, "On a day like this, how about these easy-to-prepare ingredients?"

[0232] This system allows elderly people and users with low digital literacy to easily enjoy online shopping. Combined with an emotion engine, the system can optimally respond to the user's state and provide a more personalized shopping experience.

[0233] The processing flow will be explained below.

[0234] Step 1:

[0235] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[0236] Step 2:

[0237] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[0238] Step 3:

[0239] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[0240] Step 4:

[0241] The device sends the user's input to the server, which receives the input and analyzes it using natural language processing means and an emotion engine.

[0242] Step 5:

[0243] The server uses natural language processing to analyze the user's input. At the same time, the emotion engine recognizes the user's emotion from the text. For example, if the user inputs "I need ingredients for dinner in a hurry," the emotion engine will recognize the user's emotion as "hurrying."

[0244] Step 6:

[0245] Based on the analysis, the server generates an appropriate response, such as a specific suggestion or question like, "If you're in a hurry, how about this quick meal kit?" This response is then sent back to the device and displayed to the user.

[0246] Step 7:

[0247] The terminal displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further information about the displayed product or question.

[0248] Step 8:

[0249] The server generates product suggestions based on the user's additional input, taking into account the user's past purchase history, preferences, and emotional information obtained from the emotion engine to generate an appropriate product list.

[0250] Step 9:

[0251] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[0252] Step 10:

[0253] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[0254] Step 11:

[0255] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[0256] Step 12:

[0257] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[0258] Step 13:

[0259] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[0260] Example 2

[0261] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0262] Modern online shopping systems present a problem: elderly users and those with low digital literacy find them difficult to use. Users are particularly prone to feeling stressed during the product selection and ordering process, which reduces their willingness to purchase. Furthermore, traditional systems do not take users' emotions into consideration and can only provide a uniform response, making it difficult to provide a user-friendly experience.

[0263] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a natural language processing means for analyzing the contents of the user's order and generating an appropriate response, a means including an emotion engine for recognizing the user's emotion and adjusting the response based on the emotion, and a means for suggesting products to the user. This makes it possible to provide a friendly shopping experience that is easy to use even for elderly people and users with low digital literacy and that takes the user's emotions into consideration.

[0264] "User authentication" is the process of verifying the authentication information (such as username and password) entered by a user when accessing a system to verify that the user is a legitimate account holder.

[0265] A "natural conversational style" is a style of interaction that allows a user to interact with a computer in a natural way, just like a conversation between humans.

[0266] "Natural language processing" is a technology that allows computers to understand, interpret, and generate human language, and is used for text analysis and response generation.

[0267] An "emotion engine" is a technology that recognizes emotions from user input text, etc., and adjusts the system's responses and suggestions based on those emotions.

[0268] "Product suggestion" is the process of selecting and suggesting suitable products to a user based on their input, past purchase history, and emotional state.

[0269] "Final confirmation" is the process by which the user finally confirms and confirms the order details of the products selected by the user.

[0270] A "mail order company" is a company or business that receives orders and provides products.

[0271] "Digital literacy" is the ability to understand and effectively use digital technologies, including the internet and computers.

[0272] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[0273] User authentication

[0274] The terminal first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the terminal sends this authentication information to the server. The server then verifies the received authentication information and authenticates that the user is a legitimate account holder. This process uses a web browser or mobile application as the user interface, and a database management system such as SQL is used on the server side to access the database.

[0275] Natural conversation generation

[0276] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the information to the server. The HTTP protocol is used for data communication.

[0277] Input analysis and response generation

[0278] The server analyzes the received text and generates an appropriate response using natural language processing means and an emotion engine. Natural language processing libraries such as NLTK and spaCy are used as natural language processing means. Microsoft® Azure®'s Text Analytics API and Google® Cloud Natural Language API are often used as emotion engines. For example, specific questions and suggestions such as "What brand of milk would you like?" are posed. If the emotion engine recognizes the user's emotion, the tone and content of the response are adjusted. This response is then sent back to the device and used as the content displayed to the user.

[0279] Generate product suggestions

[0280] In response to a user's request, the server suggests appropriate products, taking into account their purchasing history and preferences. This is done using a recommendation algorithm that uses past database references and machine learning models. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[0281] View product suggestions

[0282] The terminal displays the list of product suggestions sent from the server on the screen, allowing the user to visually check it. The user selects the products they wish to purchase from the displayed products. Dynamic web pages using HTML and JavaScript (registered trademark) are often used here.

[0283] Final confirmation and submission of order

[0284] A screen for the user to make a final confirmation of the products they have selected is displayed on the terminal. After the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order processing. This final data transmission is done via email or API call.

[0285] Specific examples

[0286] For example, if a user inputs "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest foods that can be prepared in a short time or easy-to-prepare kits. The tone of the response is also taken into consideration, and the suggestions are presented in a friendly format, such as "On a day like this, how about these easy-to-prepare ingredients?" This system makes it easy for elderly people and users with low digital literacy to enjoy online shopping. By combining the emotion engine, the system can optimally respond to the user's condition and provide a more personalized shopping experience.

[0287] Prompt Sentence Examples

[0288] In this system, an example of a prompt sentence to be input to the generative AI model is shown below.

[0289] User Input: "I'm tired and want to prepare a quick dinner."

[0290] Expected AI response:

[0291] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[0292] In this way, the generative AI model takes into account the user's emotional state and generates more appropriate and relatable responses.

[0293] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0294] Step 1: Displaying the user authentication screen

[0295] The terminal displays a login screen to the user and prompts them to enter their username and password. The input interface uses HTML and JavaScript. Input from the user is received in the following format:

[0296] Input: Username and Password

[0297] Output: Form data including entered authentication information

[0298] Step 2: Submit user credentials

[0299] When a user enters their authentication information and presses the login button, the device sends this information to the server as an HTTP POST request, often an AJAX request.

[0300] Input: The credentials entered by the user (username and password)

[0301] Output: Authentication information sent to the server

[0302] Step 3: Verify authentication

[0303] The server checks the received authentication information against a database to verify that the user is a valid account holder. It searches the database using an SQL query, and if the authentication information matches, it returns a successful authentication.

[0304] Input: Submitted credentials

[0305] Output: Authentication success or failure status

[0306] Step 4: Screen displayed after successful authentication

[0307] After the server returns a successful authentication, the device presents the user with a screen containing a text input field and instructions, using additional JavaScript and CSS to dynamically modify the user interface.

[0308] Input: Authentication success status

[0309] Output: Order entry screen

[0310] Step 5: Enter your order details

[0311] The user enters the order details or question in text format. For example, they might enter "I would like to order milk." This input data is used in the next step.

[0312] Input: User text input

[0313] Output: Text data of the entered order details

[0314] Step 6: Submit your order

[0315] The terminal sends the entered order details to the server, again using an AJAX request.

[0316] Input: User text input

[0317] Output: Order details sent to the server

[0318] Step 7: Text analysis and response generation

[0319] The server analyzes the received text using natural language processing tools (such as NLTK or spaCy), and simultaneously uses an emotion engine (such as Microsoft Azure or Google Cloud Natural Language API) to recognize the user's emotions and adjust the tone and content of the response. Generative AI models are also used.

[0320] Input: Text data of the order details sent

[0321] Output: Analysis results and appropriate response data

[0322] Step 8: Generate product suggestions

[0323] Based on the recognized emotions and order details, the server generates product suggestions that take into account purchase history and preferences, using recommendation algorithms and database queries.

[0324] Input: Analysis results, sentiment data, past purchase data

[0325] Output: A list of product suggestions for the user

[0326] Step 9: View product suggestions

[0327] The terminal displays the product suggestion list sent from the server on the screen and allows the user to select. In this process, a product list is dynamically generated and displayed on the screen.

[0328] Input: Product suggestion list

[0329] Output: A visual representation of the product suggestions

[0330] Step 10: Product selection and final confirmation

[0331] The user selects the items they wish to purchase from the displayed items and then confirms their purchase on a final screen, where the order list and total price are displayed.

[0332] Input: Select from product suggestion list

[0333] Output: Final confirmation screen

[0334] Step 11: Submit the final order

[0335] When the user presses the "Confirm Order" button, the terminal sends the order details to the server, which then receives the details and sends them to the mail order company.

[0336] Input: Final confirmed order details

[0337] Output: Order data sent to mail order company

[0338] Example prompt sentences

[0339] User Input: "I'm tired and want to prepare a quick dinner."

[0340] Expected AI response:

[0341] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[0342] By explaining each processing step in detail in this way, it is possible to concretely understand how the present system operates.

[0343] (Application example 2)

[0344] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0345] One problem that elderly people and users with low digital literacy face when trying to easily use online shopping is that conventional shopping systems require complicated operations and procedures, making them unfriendly. Furthermore, they lack appropriate responses and product suggestions based on the user's emotional state, making it difficult to achieve a personalized shopping experience.

[0346] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order a product in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means including an emotion engine that recognizes the user's emotional state and adjusts the response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and make a final confirmation; and means for transmitting the order details after final confirmation to a mail-order company and completing the order. This enables easy and personalized online shopping through natural conversation and responses according to the user's emotional state, even for elderly people and users with low digital literacy.

[0347] "User authentication" is the process of sending the authentication information required for a user to log in to an account to a server to verify that the user is a legitimate account holder.

[0348] "Natural conversational style" refers to a natural language communication style that allows users to interact with a system through text input or voice input.

[0349] "Natural language processing" is a technology that enables computers to understand and analyze human language (natural language) and generate appropriate responses.

[0350] An "emotion engine" is a system that recognizes a user's emotional state from their input, voice data, facial expressions, etc., and adjusts the response accordingly.

[0351] "Product suggestion" is a process of selecting and presenting suitable products to a user, taking into account the user's past purchase history and current emotional state.

[0352] "Screen display" refers to the means by which the information presented by the system and the list of suggested products are visually displayed to the user.

[0353] "Final confirmation" is a procedure for confirming and finalizing the order details of the products selected by the user.

[0354] "Submitting an order" is the procedure of sending the order details that have been finally confirmed by the user to the mail order company, thereby completing the order processing.

[0355] "Elderly people" are generally people aged 65 or over.

[0356] "Digital literacy" is the ability to search for, use, generate and evaluate information using digital devices, including computers and the internet.

[0357] "Online shopping" is the act of purchasing goods and services using the Internet.

[0358] The present invention relates to a system that allows even elderly people and users with low digital literacy to easily shop online. Specific embodiments for carrying out the present invention will be described below.

[0359] System Overview

[0360] The system of the present invention comprises a terminal through which a user inputs authentication information for logging into an account, means for transmitting the authentication information to a server and performing user authentication, means for prompting the authenticated user to order products in a natural conversational manner, natural language processing means for analyzing the contents of the user's order and generating an appropriate response, an emotion engine for recognizing the user's emotional state and adjusting the response, means for suggesting products to the user, means for displaying a list of suggested products on a screen, means for ordering products selected by the user and making a final confirmation, and means for transmitting the order details after final confirmation to a mail order company and completing the order.

[0361] Hardware and Software

[0362] The system is implemented using the following hardware and software:

[0363] Hardware:

[0364] Smartphone: Responsible for user input and display of responses.

[0365] Server: Performs authentication verification, natural language processing, product recommendations, and emotion recognition.

[0366] software:

[0367] Natural Language Processing (NLPProcessor): Analyzes user text or voice input and generates a response.

[0368] Emotion Engine: Detects the emotional state of the user from their input and tailors the response.

[0369] Communication module: Sends and receives data between the server and smartphone.

[0370] Natural Language Processing and Emotion Recognition

[0371] The server uses an NLP processor to analyze the text and voice data entered by the user to understand the product the user wants and the question they are asking. The Emotion Engine then detects the user's emotional state from the voice and text and tailors the response to convey sincerity and familiarity.

[0372] Specific examples

[0373] For example, if a user says "I want fresh vegetables," the NLP Processor analyzes the input and generates a list of fresh vegetables. At the same time, the Emotion Engine detects positive emotions and suggests "How about some fresh spinach?" based on the user's needs.

[0374] Also, if a user types, "I'm tired today," the Emotion Engine takes that emotional state into account and generates a response such as, "On a day like this, I recommend a simple vegetable mix."

[0375] Example prompt sentence:

[0376] User input: "I want fresh vegetables"

[0377] Emotional state: Positive

[0378] Output prompt: "Today's special is fresh spinach."

[0379] User input: "I'm tired today"

[0380] Emotional state: Fatigue

[0381] Output prompt: "How about this easy-to-prepare vegetable mix for a day like this?"

[0382] This system allows users to easily shop online and receive appropriate product suggestions based on their emotions, providing a comfortable and personalized shopping experience.

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] The user enters a username and password into the authentication information input screen on their smartphone. The device sends this authentication information to the server. The input data is the username and password, and the output data is the authentication request sent to the server.

[0386] Step 2:

[0387] The server verifies the received authentication information and verifies whether the user is a valid account holder. In this process, the input is the authentication information and the output is the authentication success or failure status. Specifically, the server queries the database to verify that the information matches.

[0388] Step 3:

[0389] If authentication is successful, the terminal will present the user with a text entry field and voice input options for entering orders or questions. The input data is the authentication success status, and the output data is the input screen.

[0390] Step 4:

[0391] The user inputs the product they wish to order and their question into the terminal in text or voice format. The input data is the user's order or question, and the output data is the input text data or voice data.

[0392] Step 5:

[0393] The device sends the user's input text or voice data to the server. The input is the text or voice data, and the output is a request sent to the server.

[0394] Step 6:

[0395] The server uses natural language processing (NLPProcessor) to analyze the received data. The input data is text data or audio data, and the output data is a candidate response as a result of the analysis. Specifically, the NLPProcessor performs grammatical and semantic analysis.

[0396] Step 7:

[0397] The server uses an emotion engine to recognize the emotional state from the user's input. The input data is text data or voice data, and the output data is the classification result of the emotional state. The emotion engine analyzes emotions from, for example, the tone of voice and word choice.

[0398] Step 8:

[0399] The server generates a response based on the user's emotions based on the analysis results of the NLP Processor and the emotional state of the Emotion Engine. The input data is the analysis results and the emotional state, and the output data is the adjusted response. Specifically, if the emotional state is positive, the response is adjusted to use more friendly language.

[0400] Step 9:

[0401] The server considers the user's past purchase history and current emotional state to suggest appropriate products. The input data is the user's purchase history and emotional state, and the output data is a list of suggested products. Specifically, the server selects products of high interest from the user's purchase history and adds promotional text according to the user's emotional state.

[0402] Step 10:

[0403] The server sends a list of suggested products to the terminal, which then displays it on the screen. The input data is the product list, and the output data is the product list screen that is displayed. Specifically, the terminal displays the list in a visually easy-to-understand format.

[0404] Step 11:

[0405] The user selects the product they want to purchase from the displayed products and confirms the order. The input data is the selected product, and the output data is the final confirmation screen and the user's confirmation operation.

[0406] Step 12:

[0407] Once the user has made a final confirmation, the terminal sends the order details to the server. The server then sends the order details to the mail order company, completing the order. The input data is the final confirmed order details, and the output data is the result of the order being sent to the mail order company. Specifically, the communication module performs the process of sending the order data.

[0408] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0409] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0411] [Second embodiment]

[0412] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0413] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0414] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0416] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0418] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0419] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0420] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0421] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0423] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0424] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0425] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, and a means for the user to confirm the products selected by the user and send them to the mail order company.

[0426] User authentication

[0427] The device first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0428] Natural conversation generation

[0429] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once the entry is complete, the device sends the input to the server.

[0430] Input analysis and response generation

[0431] The server analyzes the received text and uses natural language processing to generate an appropriate response, such as a specific question or suggestion like "What brand of milk would you like?" This response is then sent back to the device and used as the display for the user.

[0432] Generate product suggestions

[0433] The server generates a list of products based on the user's request, taking into account their purchasing history and preferences. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the terminal and displayed visually to the user.

[0434] View product suggestions

[0435] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0436] Final confirmation and submission of order

[0437] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0438] This system allows even elderly people and users with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the natural conversational interface allows users to operate the system without any discomfort.

[0439] The processing flow will be explained below.

[0440] Step 1:

[0441] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[0442] Step 2:

[0443] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[0444] Step 3:

[0445] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[0446] Step 4:

[0447] The terminal sends the user's input to the server, which receives the input and analyzes it using natural language processing means.

[0448] Step 5:

[0449] The server analyzes the input and generates an appropriate response, such as a question like "What brand of milk would you like?", and sends the response to the device.

[0450] Step 6:

[0451] The device displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further input.

[0452] Step 7:

[0453] The server generates product suggestions based on detailed input from the user, taking into account the user's past purchase history and preferences to create an appropriate product list.

[0454] Step 8:

[0455] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[0456] Step 9:

[0457] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[0458] Step 10:

[0459] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[0460] Step 11:

[0461] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[0462] Step 12:

[0463] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[0464] Example 1

[0465] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0466] For elderly people and users with low digital literacy, online shopping is often complicated and difficult to operate. In particular, if appropriate options are not provided during the product selection and ordering process, there is a high risk of selecting the wrong product. Conventional online shopping systems have complex interfaces that make them difficult for users to use.

[0467] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0468] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server and performing user authentication; means for prompting the authenticated user to order products in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and for final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for suggesting products based on the user's past purchase history and preferences; means for displaying the response generated by the natural language processing means to the user; and means for analyzing the user's input and generating prompt sentences using a generative AI model. This allows users to operate the system intuitively and easily, making online shopping smooth for even elderly people and users with low digital literacy.

[0469] "Authentication Information" refers to the username and password you enter to log into your account.

[0470] "Server" refers to a computer system for processing and storing data.

[0471] "Terminal" refers to devices used by users, such as smartphones, tablets, and computers.

[0472] "User" refers to a person who uses this system to shop online.

[0473] "Natural language processing" refers to the technology of using a computer to analyze human language and generate appropriate responses.

[0474] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate and analyze natural language.

[0475] "Prompt sentence" refers to the text data input into a generative AI model.

[0476] "User authentication" refers to the process of verifying that a user has a valid account.

[0477] "Order Details" refers to the list of products that the User wishes to purchase and their detailed information.

[0478] A "mail order company" refers to a company that receives orders from users and sells products.

[0479] "Product Suggestions" refers to a list of products suggested to users based on their purchasing history and preferences.

[0480] "Final confirmation" refers to the process of final confirmation of the product selected by the user.

[0481] "Purchase history" refers to a record of products a user has purchased in the past.

[0482] "Preferences" refers to a user's tendency to prefer certain products or brands.

[0483] "Text input" refers to character data that a user inputs using a keyboard or touch panel.

[0484] "Response" refers to the appropriate response that a generative AI model gives in response to user input.

[0485] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0486] Hardware and Software

[0487] The hardware used includes the devices operated by users (smartphones, tablets, PCs, etc.) and the servers (cloud servers or physical servers) that process and store data. The software includes applications that make up the user interface, generative AI models for natural language processing (e.g., GPT-3), and APIs for connecting with mail-order companies.

[0488] User authentication

[0489] The terminal first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the authentication information to the server. For example, if the username "user123" and password "password123" are entered, the server compares this information with its database for authentication. If authentication is successful, the server returns a "Login successful" status code and a session ID to the terminal.

[0490] Natural conversation generation

[0491] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once this input is complete, the device sends the input to the server.

[0492] Input analysis and response generation

[0493] The server analyzes the received text using natural language processing tools. It uses a generative AI model (e.g., GPT-3) to understand the user's intent and generate an appropriate response. For example, it might generate a question like, "What brand of milk would you like?" and send it to the device.

[0494] Generate and display product suggestions

[0495] The server takes into account the user's purchase history and preferences to suggest appropriate products. For example, it generates a suggestion such as, "We have this brand of milk. Would you like it?" and sends it to the device. The device then displays a list of suggested products on the screen so that the user can visually check them. The user can then select the desired product from the displayed products.

[0496] Final confirmation and submission of order

[0497] After the user selects the product they wish to purchase, a screen for final confirmation will be displayed on the terminal. This screen displays details of the selected product and a "Confirm Order" button. When the user makes their final confirmation and presses the "Confirm Order" button, the terminal will send the order details to the server. The server will then send this order details to the mail order company and complete the order process.

[0498] Use of concrete examples and prompts

[0499] Specific examples of prompt sentences are shown below.

[0500] User input: "I want to order milk."

[0501] Prompt the AI ​​model: "The user has typed 'I would like to order milk.' Next, ask related questions to confirm the specific brand and quantity."

[0502] The prompt allows the generative AI model to ask appropriate questions or make suggestions based on the user's input, such as "What brand of milk would you like?" or "How much do you need?", and display these responses to the user.

[0503] As described above, this system is designed to enable users to shop online with an intuitive and natural interface, and is particularly notable for being easy to operate even for elderly people and users with low digital literacy.

[0504] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0505] Step 1: User authentication

[0506] The terminal displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the entered authentication information (username "user123" and password "password123") to the server. The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it generates a "login successful" status code and a session ID and sends them back to the terminal.

[0507] Input: Username, Password

[0508] Data processing: Comparison with database

[0509] Output: "Login successful" status code and session ID

[0510] Step 2: Start a conversation

[0511] After authentication is complete, the device displays a text entry field and allows the user to begin the product ordering conversation. The entry field appears with the message "Please enter the product you would like to order." The user enters the specific order details in text format, such as "I would like to order milk." Once the entry is complete, the device sends the text content to the server.

[0512] Input: User's order text (e.g. "I'd like to order milk")

[0513] Data processing: Sending text data

[0514] Output: Send text data to the server

[0515] Step 3: Parsing input and generating response

[0516] The server analyzes the received text using natural language processing. A generative AI model (e.g., GPT-3) is used to understand the intent of the user's input and generate an appropriate response. In this process, a prompt sentence is input into the AI ​​model for analysis. For example, a prompt sentence could be, "The user inputs 'I would like to order milk.' Please ask related questions next to confirm the specific brand and quantity." The server generates a generated response, "What brand of milk would you like?", and sends it to the device.

[0517] Input: User text input, prompt

[0518] Data processing: natural language processing, intent analysis

[0519] Output: The generated response (e.g., "What brand of milk would you like?")

[0520] Step 4: Generate and display product suggestions

[0521] The server suggests appropriate products by taking into account the user's past purchase history and preferences. It references a purchase history database to identify brands of milk previously purchased. The server generates a product list based on this information, creating suggestions such as "We have these brands of milk: Brand A, Brand B, Brand C." This product list is sent to the terminal and displayed on the screen so that the user can visually confirm it. The user selects the desired product from the displayed products.

[0522] Input: User's past purchase history, preferences

[0523] Data processing: Analysis of purchase history, creation of product list

[0524] Output: Product suggestion list

[0525] Step 5: Finalize and submit your order

[0526] After the user selects the product they wish to purchase, the terminal displays a final confirmation screen. This screen displays details of the selected product and includes a "Confirm Order" button. When the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company's API, completing the order process.

[0527] Input: Product selected by user, press of final confirmation button

[0528] Data processing: Sending order details, calling API

[0529] Output: Order processing completed

[0530] The above specific processing steps make it easy for even elderly people and users with low digital literacy to shop online.

[0531] (Application example 1)

[0532] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0533] Conventional online shopping systems have the problem that the ordering process is complicated and difficult to use for elderly people and users with low digital literacy. Furthermore, product selection and suggestions are standardized, making it difficult to flexibly respond to user preferences. In particular, the lack of a dialogue-based ordering system using natural language has left many users feeling uneasy about operation and leading to operational errors.

[0534] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0535] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order products in a natural conversational format; means for analyzing the user's order details and generating a response using a generative AI model; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected and make a final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for the natural language processing means to generate a prompt sentence according to the order details using the generative AI model; and means for displaying a prioritized list of suggested products. This enables even elderly people and users with low digital literacy to easily shop online in a natural conversational format.

[0536] A "means for users to enter authentication information to log in to their account" means providing an interface for users to enter the authentication information (such as username and password) required to access their account.

[0537] The "means for transmitting the authentication information to a server and performing user authentication" means transmitting the input authentication information to a server, and having the server verify the authenticity of the user based on that information.

[0538] "Means for encouraging authenticated users to order products in a natural conversational manner" means allowing a user who has successfully logged in to begin the process of ordering products in a natural language conversational manner.

[0539] "Means for analyzing the user's order and generating a response using a generative AI model" refers to means for analyzing the order entered by the user and generating an appropriate response using a generative AI model (such as GPT-3).

[0540] "Means for suggesting products to users" refers to means for suggesting appropriate products and services to users based on the user's input and past purchase history.

[0541] The "means for displaying a list of proposed products on a screen" refers to a means for displaying the proposed products and services on a display screen so that the proposed products and services can be visually confirmed.

[0542] "Means for ordering the products selected by the user and making final confirmation" refers to the means for the user to make final confirmation of the products or services selected and confirm the order.

[0543] "Means for sending the order details after final confirmation to the mail order company and completing the order" refers to the means by which the user sends the order details that have been finally confirmed to the mail order company and completes the processing of the order.

[0544] "Means for the natural language processing means to use a generative AI model to generate a prompt sentence according to the order content" refers to means for analyzing the user's order content and then using a generative AI model to generate an appropriate prompt sentence.

[0545] "Means for prioritizing and displaying a list of suggested products" refers to means for ranking suggested products or services based on importance or relevance and displaying them on the screen in that order.

[0546] The present invention relates to a system that allows users to conduct online shopping in a natural, conversational style. This system provides an interface that is easy for users to operate and is designed to be easy to use, especially for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0547] User authentication

[0548] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0549] Order in a natural conversational style

[0550] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order sushi." Once the entry is complete, the device sends the input to the server.

[0551] Input analysis and response generation

[0552] The server analyzes the received text and uses a generative AI model (such as GPT-3) to generate an appropriate response, such as a specific question or suggestion like, "What kind of sushi would you like?" This response is then sent back to the device and used as the display content for the user.

[0553] Generate product suggestions

[0554] The server suggests appropriate products in response to a user's request, taking into account their purchase history and preferences. For example, it generates a list of products such as "How about this vegetarian sushi?" based on the user's past orders and preferences. This list is sent to the device and displayed visually to the user. The list is also prioritized and displayed.

[0555] View product suggestions

[0556] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0557] Final confirmation and submission of order

[0558] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0559] Hardware and software used

[0560] The system is implemented using the following hardware and software.

[0561] Hardware: End devices such as smartphones, tablets, and PCs.

[0562] Software: Python, natural language processing libraries, OpenAI GPT-3 API.

[0563] Examples of prompt statements

[0564] Examples of prompts to input to a generative AI model:

[0565] 1. Generate a response to a user's order entry:

[0566] User input: "I want to order sushi"

[0567] GPT-3 prompt: "What kind of sushi would you like? Typical options include tuna, salmon, eel, and California rolls."

[0568] 2. Product proposal generation:

[0569] User preferences: "Likes Japanese food, prefers vegetarian options"

[0570] Purchase history: "Ordered tempura, tofu salad, vegetable sushi"

[0571] GPT-3 prompt: "Based on the user's preferences and purchase history, suggest items. Consider Japanese vegetarian options."

[0572] This system allows even elderly people and those with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the use of generative AI models allows users to operate the system seamlessly and receive more personalized service.

[0573] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0574] Step 1: User authentication

[0575] The user enters their username and password into the login screen of their smartphone or tablet. This provides the user with authentication information to access the system. The device sends the entered authentication information to the server. The server verifies the received authentication information and authenticates the user. If authentication is successful, the server sends a message of successful authentication to the device, and the device proceeds to the next step.

[0576] Input: Username and Password

[0577] Output: Authentication success message

[0578] Step 2: Natural conversational order prompts

[0579] The device presents the authenticated user with a text entry field, prompting them to enter details of the items they wish to order. The user enters their order in text form and sends a specific request, such as "I would like to order sushi," to the server.

[0580] Input: User-entered text for the order

[0581] Output: Text data sent to the server

[0582] Step 3: Parsing input and generating response

[0583] The server analyzes the received text data and generates an appropriate response using a generative AI model (e.g., GPT-3). Specifically, the server generates prompts to ask specific questions or make suggestions, such as "What kind of sushi would you like?", and then uses the generative AI model to generate a response based on the prompt. This response is then sent back to the device and displayed to the user.

[0584] Input: Text data of the user's order details

[0585] Output: Response text generated by the GPT-3 model

[0586] Step 4: Generate product suggestions

[0587] The server uses a generative AI model to make appropriate product suggestions, taking into account the user's order details, purchase history, and preferences. For example, it might generate a list of specific suggestions, such as "How about this vegetarian sushi?" This list is prioritized and sent to the device.

[0588] Input: Order details, purchase history, user preferences

[0589] Output: Text data of the suggested product list

[0590] Step 5: View product suggestions

[0591] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and the user can select the products they wish to purchase from the displayed products.

[0592] Input: Text data of the suggested product list

[0593] Output: Product list displayed on screen

[0594] Step 6: Finalize your order

[0595] The terminal displays the details of the product selected by the user and prompts for final confirmation. After the user confirms the details and presses the "Confirm Order" button, the terminal sends the order details to the server.

[0596] Input: The product selected by the user

[0597] Output: Final confirmation screen and order confirmation

[0598] Step 7: Submit and complete your order

[0599] The server receives the final order details sent from the terminal and sends them to the mail order company. Once the mail order company has completed the order processing, the server sends a message to the terminal notifying the user that the order was successful.

[0600] Input: Final confirmed order details

[0601] Output: Order successful message

[0602] This ensures that users' orders are processed reliably at each step, making it easy for even elderly people and users with low digital literacy to enjoy online shopping.

[0603] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0604] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[0605] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, a means for the user to finally confirm the products selected by the user and send them to the mail-order company, and an emotion engine that recognizes the user's emotions.

[0606] User authentication

[0607] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends this authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0608] Natural conversation generation

[0609] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the input to the server.

[0610] Input analysis and response generation

[0611] The server analyzes the received text and generates an appropriate response using natural language processing and an emotion engine. For example, it may ask specific questions or make suggestions, such as "What brand of milk would you like?" If the emotion engine recognizes the user's emotion, it adjusts the tone and content of the response. This response is then sent back to the device and used as the display content for the user.

[0612] Generate product suggestions

[0613] In response to a user's request, the server suggests appropriate products, taking into account the user's purchase history and preferences. The emotion engine also takes into account information such as the emotional state the user was in when purchasing a particular product. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[0614] View product suggestions

[0615] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0616] Final confirmation and submission of order

[0617] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0618] Specific examples

[0619] For example, if a user types, "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest quick-cooking meals and easy-to-prepare kits. The tone of the response is also considered, and the suggestions are presented in a friendly format, such as, "On a day like this, how about these easy-to-prepare ingredients?"

[0620] This system allows elderly people and users with low digital literacy to easily enjoy online shopping. Combined with an emotion engine, the system can optimally respond to the user's state and provide a more personalized shopping experience.

[0621] The processing flow will be explained below.

[0622] Step 1:

[0623] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[0624] Step 2:

[0625] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[0626] Step 3:

[0627] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[0628] Step 4:

[0629] The device sends the user's input to the server, which receives the input and analyzes it using natural language processing means and an emotion engine.

[0630] Step 5:

[0631] The server uses natural language processing to analyze the user's input. At the same time, the emotion engine recognizes the user's emotion from the text. For example, if the user inputs "I need ingredients for dinner in a hurry," the emotion engine will recognize the user's emotion as "hurrying."

[0632] Step 6:

[0633] Based on the analysis, the server generates an appropriate response, such as a specific suggestion or question like, "If you're in a hurry, how about this quick meal kit?" This response is then sent back to the device and displayed to the user.

[0634] Step 7:

[0635] The terminal displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further information about the displayed product or question.

[0636] Step 8:

[0637] The server generates product suggestions based on the user's additional input, taking into account the user's past purchase history, preferences, and emotional information obtained from the emotion engine to generate an appropriate product list.

[0638] Step 9:

[0639] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[0640] Step 10:

[0641] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[0642] Step 11:

[0643] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[0644] Step 12:

[0645] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[0646] Step 13:

[0647] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[0648] Example 2

[0649] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0650] Modern online shopping systems present a problem: elderly users and those with low digital literacy find them difficult to use. Users are particularly prone to feeling stressed during the product selection and ordering process, which reduces their willingness to purchase. Furthermore, traditional systems do not take users' emotions into consideration and can only provide a uniform response, making it difficult to provide a user-friendly experience.

[0651] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a natural language processing means for analyzing the contents of the user's order and generating an appropriate response, a means including an emotion engine for recognizing the user's emotion and adjusting the response based on the emotion, and a means for suggesting products to the user. This makes it possible to provide a friendly shopping experience that is easy to use even for elderly people and users with low digital literacy and that takes the user's emotions into consideration.

[0652] "User authentication" is the process of verifying the authentication information (such as username and password) entered by a user when accessing a system to verify that the user is a legitimate account holder.

[0653] A "natural conversational style" is a style of interaction that allows a user to interact with a computer in a natural way, just like a conversation between humans.

[0654] "Natural language processing" is a technology that allows computers to understand, interpret, and generate human language, and is used for text analysis and response generation.

[0655] An "emotion engine" is a technology that recognizes emotions from user input text, etc., and adjusts the system's responses and suggestions based on those emotions.

[0656] "Product suggestion" is the process of selecting and suggesting suitable products to a user based on their input, past purchase history, and emotional state.

[0657] "Final confirmation" is the process by which the user finally confirms and confirms the order details of the products selected by the user.

[0658] A "mail order company" is a company or business that receives orders and provides products.

[0659] "Digital literacy" is the ability to understand and effectively use digital technologies, including the internet and computers.

[0660] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[0661] User authentication

[0662] The terminal first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the terminal sends this authentication information to the server. The server then verifies the received authentication information and authenticates that the user is a legitimate account holder. This process uses a web browser or mobile application as the user interface, and a database management system such as SQL is used on the server side to access the database.

[0663] Natural conversation generation

[0664] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the information to the server. The HTTP protocol is used for data communication.

[0665] Input analysis and response generation

[0666] The server analyzes the received text and generates an appropriate response using natural language processing means and an emotion engine. Natural language processing libraries such as NLTK and spaCy are used as natural language processing means, and Microsoft Azure's Text Analytics API and Google Cloud Natural Language API are often used as emotion engines. For example, specific questions and suggestions such as "What brand of milk would you like?" are asked. If the emotion engine recognizes the user's emotion, the tone and content of the response are adjusted. This response is then sent back to the device and used as the content to be displayed to the user.

[0667] Generate product suggestions

[0668] In response to a user's request, the server suggests appropriate products, taking into account their purchasing history and preferences. This is done using a recommendation algorithm that uses past database references and machine learning models. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[0669] View product suggestions

[0670] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them. The user selects the products they want to purchase from the displayed products. Dynamic web pages using HTML and JavaScript are often used here.

[0671] Final confirmation and submission of order

[0672] A screen for the user to make a final confirmation of the products they have selected is displayed on the terminal. After the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order processing. This final data transmission is done via email or API call.

[0673] Specific examples

[0674] For example, if a user inputs "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest foods that can be prepared in a short time or easy-to-prepare kits. The tone of the response is also taken into consideration, and the suggestions are presented in a friendly format, such as "On a day like this, how about these easy-to-prepare ingredients?" This system makes it easy for elderly people and users with low digital literacy to enjoy online shopping. By combining the emotion engine, the system can optimally respond to the user's condition and provide a more personalized shopping experience.

[0675] Prompt Sentence Examples

[0676] In this system, an example of a prompt sentence to be input to the generative AI model is shown below.

[0677] User Input: "I'm tired and want to prepare a quick dinner."

[0678] Expected AI response:

[0679] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[0680] In this way, the generative AI model takes into account the user's emotional state and generates more appropriate and relatable responses.

[0681] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0682] Step 1: Displaying the user authentication screen

[0683] The terminal displays a login screen to the user and prompts them to enter their username and password. The input interface uses HTML and JavaScript. Input from the user is received in the following format:

[0684] Input: Username and Password

[0685] Output: Form data including entered authentication information

[0686] Step 2: Submit user credentials

[0687] When a user enters their authentication information and presses the login button, the device sends this information to the server as an HTTP POST request, often an AJAX request.

[0688] Input: The credentials entered by the user (username and password)

[0689] Output: Authentication information sent to the server

[0690] Step 3: Verify authentication

[0691] The server checks the received authentication information against a database to verify that the user is a valid account holder. It searches the database using an SQL query, and if the authentication information matches, it returns a successful authentication.

[0692] Input: Submitted credentials

[0693] Output: Authentication success or failure status

[0694] Step 4: Screen displayed after successful authentication

[0695] After the server returns a successful authentication, the device presents the user with a screen containing a text input field and instructions, using additional JavaScript and CSS to dynamically modify the user interface.

[0696] Input: Authentication success status

[0697] Output: Order entry screen

[0698] Step 5: Enter your order details

[0699] The user enters the order details or question in text format. For example, they might enter "I would like to order milk." This input data is used in the next step.

[0700] Input: User text input

[0701] Output: Text data of the entered order details

[0702] Step 6: Submit your order

[0703] The terminal sends the entered order details to the server, again using an AJAX request.

[0704] Input: User text input

[0705] Output: Order details sent to the server

[0706] Step 7: Text analysis and response generation

[0707] The server analyzes the received text using natural language processing tools (such as NLTK or spaCy), and simultaneously uses an emotion engine (such as Microsoft Azure or Google Cloud Natural Language API) to recognize the user's emotions and adjust the tone and content of the response. Generative AI models are also used.

[0708] Input: Text data of the order details sent

[0709] Output: Analysis results and appropriate response data

[0710] Step 8: Generate product suggestions

[0711] Based on the recognized emotions and order details, the server generates product suggestions that take into account purchase history and preferences, using recommendation algorithms and database queries.

[0712] Input: Analysis results, sentiment data, past purchase data

[0713] Output: A list of product suggestions for the user

[0714] Step 9: View product suggestions

[0715] The terminal displays the product suggestion list sent from the server on the screen and allows the user to select. In this process, a product list is dynamically generated and displayed on the screen.

[0716] Input: Product suggestion list

[0717] Output: A visual representation of the product suggestions

[0718] Step 10: Product selection and final confirmation

[0719] The user selects the items they wish to purchase from the displayed items and then confirms their purchase on a final screen, where the order list and total price are displayed.

[0720] Input: Select from product suggestion list

[0721] Output: Final confirmation screen

[0722] Step 11: Submit the final order

[0723] When the user presses the "Confirm Order" button, the terminal sends the order details to the server, which then receives the details and sends them to the mail order company.

[0724] Input: Final confirmed order details

[0725] Output: Order data sent to mail order company

[0726] Example prompt sentences

[0727] User Input: "I'm tired and want to prepare a quick dinner."

[0728] Expected AI response:

[0729] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[0730] By explaining each processing step in detail in this way, it is possible to concretely understand how the present system operates.

[0731] (Application example 2)

[0732] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0733] One problem that elderly people and users with low digital literacy face when trying to easily use online shopping is that conventional shopping systems require complicated operations and procedures, making them unfriendly. Furthermore, they lack appropriate responses and product suggestions based on the user's emotional state, making it difficult to achieve a personalized shopping experience.

[0734] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order a product in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means including an emotion engine that recognizes the user's emotional state and adjusts the response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and make a final confirmation; and means for transmitting the order details after final confirmation to a mail-order company and completing the order. This enables easy and personalized online shopping through natural conversation and responses according to the user's emotional state, even for elderly people and users with low digital literacy.

[0735] "User authentication" is the process of sending the authentication information required for a user to log in to an account to a server to verify that the user is a legitimate account holder.

[0736] "Natural conversational style" refers to a natural language communication style that allows users to interact with a system through text input or voice input.

[0737] "Natural language processing" is a technology that enables computers to understand and analyze human language (natural language) and generate appropriate responses.

[0738] An "emotion engine" is a system that recognizes a user's emotional state from their input, voice data, facial expressions, etc., and adjusts the response accordingly.

[0739] "Product suggestion" is a process of selecting and presenting suitable products to a user, taking into account the user's past purchase history and current emotional state.

[0740] "Screen display" refers to the means by which the information presented by the system and the list of suggested products are visually displayed to the user.

[0741] "Final confirmation" is a procedure for confirming and finalizing the order details of the products selected by the user.

[0742] "Submitting an order" is the procedure of sending the order details that have been finally confirmed by the user to the mail order company, thereby completing the order processing.

[0743] "Elderly people" are generally people aged 65 or over.

[0744] "Digital literacy" is the ability to search for, use, generate and evaluate information using digital devices, including computers and the internet.

[0745] "Online shopping" is the act of purchasing goods and services using the Internet.

[0746] The present invention relates to a system that allows even elderly people and users with low digital literacy to easily shop online. Specific embodiments for carrying out the present invention will be described below.

[0747] System Overview

[0748] The system of the present invention comprises a terminal through which a user inputs authentication information for logging into an account, means for transmitting the authentication information to a server and performing user authentication, means for prompting the authenticated user to order products in a natural conversational manner, natural language processing means for analyzing the contents of the user's order and generating an appropriate response, an emotion engine for recognizing the user's emotional state and adjusting the response, means for suggesting products to the user, means for displaying a list of suggested products on a screen, means for ordering products selected by the user and making a final confirmation, and means for transmitting the order details after final confirmation to a mail order company and completing the order.

[0749] Hardware and Software

[0750] The system is implemented using the following hardware and software:

[0751] Hardware:

[0752] Smartphone: Responsible for user input and display of responses.

[0753] Server: Performs authentication verification, natural language processing, product recommendations, and emotion recognition.

[0754] software:

[0755] Natural Language Processing (NLPProcessor): Analyzes user text or voice input and generates a response.

[0756] Emotion Engine: Detects the emotional state of the user from their input and tailors the response.

[0757] Communication module: Sends and receives data between the server and smartphone.

[0758] Natural Language Processing and Emotion Recognition

[0759] The server uses an NLP processor to analyze the text and voice data entered by the user to understand the product the user wants and the question they are asking. The Emotion Engine then detects the user's emotional state from the voice and text and tailors the response to convey sincerity and familiarity.

[0760] Specific examples

[0761] For example, if a user says "I want fresh vegetables," the NLP Processor analyzes the input and generates a list of fresh vegetables. At the same time, the Emotion Engine detects positive emotions and suggests "How about some fresh spinach?" based on the user's needs.

[0762] Also, if a user types, "I'm tired today," the Emotion Engine takes that emotional state into account and generates a response such as, "On a day like this, I recommend a simple vegetable mix."

[0763] Example prompt sentence:

[0764] User input: "I want fresh vegetables"

[0765] Emotional state: Positive

[0766] Output prompt: "Today's special is fresh spinach."

[0767] User input: "I'm tired today"

[0768] Emotional state: Fatigue

[0769] Output prompt: "How about this easy-to-prepare vegetable mix for a day like this?"

[0770] This system allows users to easily shop online and receive appropriate product suggestions based on their emotions, providing a comfortable and personalized shopping experience.

[0771] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0772] Step 1:

[0773] The user enters a username and password into the authentication information input screen on their smartphone. The device sends this authentication information to the server. The input data is the username and password, and the output data is the authentication request sent to the server.

[0774] Step 2:

[0775] The server verifies the received authentication information and verifies whether the user is a valid account holder. In this process, the input is the authentication information and the output is the authentication success or failure status. Specifically, the server queries the database to verify that the information matches.

[0776] Step 3:

[0777] If authentication is successful, the terminal will present the user with a text entry field and voice input options for entering orders or questions. The input data is the authentication success status, and the output data is the input screen.

[0778] Step 4:

[0779] The user inputs the product they wish to order and their question into the terminal in text or voice format. The input data is the user's order or question, and the output data is the input text data or voice data.

[0780] Step 5:

[0781] The device sends the user's input text or voice data to the server. The input is the text or voice data, and the output is a request sent to the server.

[0782] Step 6:

[0783] The server uses natural language processing (NLPProcessor) to analyze the received data. The input data is text data or audio data, and the output data is a candidate response as a result of the analysis. Specifically, the NLPProcessor performs grammatical and semantic analysis.

[0784] Step 7:

[0785] The server uses an emotion engine to recognize the emotional state from the user's input. The input data is text data or voice data, and the output data is the classification result of the emotional state. The emotion engine analyzes emotions from, for example, the tone of voice and word choice.

[0786] Step 8:

[0787] The server generates a response based on the user's emotions based on the analysis results of the NLP Processor and the emotional state of the Emotion Engine. The input data is the analysis results and the emotional state, and the output data is the adjusted response. Specifically, if the emotional state is positive, the response is adjusted to use more friendly language.

[0788] Step 9:

[0789] The server considers the user's past purchase history and current emotional state to suggest appropriate products. The input data is the user's purchase history and emotional state, and the output data is a list of suggested products. Specifically, the server selects products of high interest from the user's purchase history and adds promotional text according to the user's emotional state.

[0790] Step 10:

[0791] The server sends a list of suggested products to the terminal, which then displays it on the screen. The input data is the product list, and the output data is the product list screen that is displayed. Specifically, the terminal displays the list in a visually easy-to-understand format.

[0792] Step 11:

[0793] The user selects the product they want to purchase from the displayed products and confirms the order. The input data is the selected product, and the output data is the final confirmation screen and the user's confirmation operation.

[0794] Step 12:

[0795] Once the user has made a final confirmation, the terminal sends the order details to the server. The server then sends the order details to the mail order company, completing the order. The input data is the final confirmed order details, and the output data is the result of the order being sent to the mail order company. Specifically, the communication module performs the process of sending the order data.

[0796] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0797] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0799] [Third embodiment]

[0800] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0801] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0802] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0803] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0804] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0805] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0806] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0807] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0808] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0809] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0810] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0811] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0812] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0813] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, and a means for the user to confirm the products selected by the user and send them to the mail order company.

[0814] User authentication

[0815] The device first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0816] Natural conversation generation

[0817] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once the entry is complete, the device sends the input to the server.

[0818] Input analysis and response generation

[0819] The server analyzes the received text and uses natural language processing to generate an appropriate response, such as a specific question or suggestion like "What brand of milk would you like?" This response is then sent back to the device and used as the display for the user.

[0820] Generate product suggestions

[0821] The server generates a list of products based on the user's request, taking into account their purchasing history and preferences. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the terminal and displayed visually to the user.

[0822] View product suggestions

[0823] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0824] Final confirmation and submission of order

[0825] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0826] This system allows even elderly people and users with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the natural conversational interface allows users to operate the system without any discomfort.

[0827] The processing flow will be explained below.

[0828] Step 1:

[0829] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[0830] Step 2:

[0831] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[0832] Step 3:

[0833] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[0834] Step 4:

[0835] The terminal sends the user's input to the server, which receives the input and analyzes it using natural language processing means.

[0836] Step 5:

[0837] The server analyzes the input and generates an appropriate response, such as a question like "What brand of milk would you like?", and sends the response to the device.

[0838] Step 6:

[0839] The device displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further input.

[0840] Step 7:

[0841] The server generates product suggestions based on detailed input from the user, taking into account the user's past purchase history and preferences to create an appropriate product list.

[0842] Step 8:

[0843] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[0844] Step 9:

[0845] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[0846] Step 10:

[0847] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[0848] Step 11:

[0849] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[0850] Step 12:

[0851] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[0852] Example 1

[0853] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0854] For elderly people and users with low digital literacy, online shopping is often complicated and difficult to operate. In particular, if appropriate options are not provided during the product selection and ordering process, there is a high risk of selecting the wrong product. Conventional online shopping systems have complex interfaces that make them difficult for users to use.

[0855] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0856] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server and performing user authentication; means for prompting the authenticated user to order products in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and for final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for suggesting products based on the user's past purchase history and preferences; means for displaying the response generated by the natural language processing means to the user; and means for analyzing the user's input and generating prompt sentences using a generative AI model. This allows users to operate the system intuitively and easily, making online shopping smooth for even elderly people and users with low digital literacy.

[0857] "Authentication Information" refers to the username and password you enter to log into your account.

[0858] "Server" refers to a computer system for processing and storing data.

[0859] "Terminal" refers to devices used by users, such as smartphones, tablets, and computers.

[0860] "User" refers to a person who uses this system to shop online.

[0861] "Natural language processing" refers to the technology of using a computer to analyze human language and generate appropriate responses.

[0862] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate and analyze natural language.

[0863] "Prompt sentence" refers to the text data input into a generative AI model.

[0864] "User authentication" refers to the process of verifying that a user has a valid account.

[0865] "Order Details" refers to the list of products that the User wishes to purchase and their detailed information.

[0866] A "mail order company" refers to a company that receives orders from users and sells products.

[0867] "Product Suggestions" refers to a list of products suggested to users based on their purchasing history and preferences.

[0868] "Final confirmation" refers to the process of final confirmation of the product selected by the user.

[0869] "Purchase history" refers to a record of products a user has purchased in the past.

[0870] "Preferences" refers to a user's tendency to prefer certain products or brands.

[0871] "Text input" refers to character data that a user inputs using a keyboard or touch panel.

[0872] "Response" refers to the appropriate response that a generative AI model gives in response to user input.

[0873] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0874] Hardware and Software

[0875] The hardware used includes the devices operated by users (smartphones, tablets, PCs, etc.) and the servers (cloud servers or physical servers) that process and store data. The software includes applications that make up the user interface, generative AI models for natural language processing (e.g., GPT-3), and APIs for connecting with mail-order companies.

[0876] User authentication

[0877] The terminal first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the authentication information to the server. For example, if the username "user123" and password "password123" are entered, the server compares this information with its database for authentication. If authentication is successful, the server returns a "Login successful" status code and a session ID to the terminal.

[0878] Natural conversation generation

[0879] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once this input is complete, the device sends the input to the server.

[0880] Input analysis and response generation

[0881] The server analyzes the received text using natural language processing tools. It uses a generative AI model (e.g., GPT-3) to understand the user's intent and generate an appropriate response. For example, it might generate a question like, "What brand of milk would you like?" and send it to the device.

[0882] Generate and display product suggestions

[0883] The server takes into account the user's purchase history and preferences to suggest appropriate products. For example, it generates a suggestion such as, "We have this brand of milk. Would you like it?" and sends it to the device. The device then displays a list of suggested products on the screen so that the user can visually check them. The user can then select the desired product from the displayed products.

[0884] Final confirmation and submission of order

[0885] After the user selects the product they wish to purchase, a screen for final confirmation will be displayed on the terminal. This screen displays details of the selected product and a "Confirm Order" button. When the user makes their final confirmation and presses the "Confirm Order" button, the terminal will send the order details to the server. The server will then send this order details to the mail order company and complete the order process.

[0886] Use of concrete examples and prompts

[0887] Specific examples of prompt sentences are shown below.

[0888] User input: "I want to order milk."

[0889] Prompt the AI ​​model: "The user has typed 'I would like to order milk.' Next, ask related questions to confirm the specific brand and quantity."

[0890] The prompt allows the generative AI model to ask appropriate questions or make suggestions based on the user's input, such as "What brand of milk would you like?" or "How much do you need?", and display these responses to the user.

[0891] As described above, this system is designed to enable users to shop online with an intuitive and natural interface, and is particularly notable for being easy to operate even for elderly people and users with low digital literacy.

[0892] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0893] Step 1: User authentication

[0894] The terminal displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the entered authentication information (username "user123" and password "password123") to the server. The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it generates a "login successful" status code and a session ID and sends them back to the terminal.

[0895] Input: Username, Password

[0896] Data processing: Comparison with database

[0897] Output: "Login successful" status code and session ID

[0898] Step 2: Start a conversation

[0899] After authentication is complete, the device displays a text entry field and allows the user to begin the product ordering conversation. The entry field appears with the message "Please enter the product you would like to order." The user enters the specific order details in text format, such as "I would like to order milk." Once the entry is complete, the device sends the text content to the server.

[0900] Input: User's order text (e.g. "I'd like to order milk")

[0901] Data processing: Sending text data

[0902] Output: Send text data to the server

[0903] Step 3: Parsing input and generating response

[0904] The server analyzes the received text using natural language processing. A generative AI model (e.g., GPT-3) is used to understand the intent of the user's input and generate an appropriate response. In this process, a prompt sentence is input into the AI ​​model for analysis. For example, a prompt sentence could be, "The user inputs 'I would like to order milk.' Please ask related questions next to confirm the specific brand and quantity." The server generates a generated response, "What brand of milk would you like?", and sends it to the device.

[0905] Input: User text input, prompt

[0906] Data processing: natural language processing, intent analysis

[0907] Output: The generated response (e.g., "What brand of milk would you like?")

[0908] Step 4: Generate and display product suggestions

[0909] The server suggests appropriate products by taking into account the user's past purchase history and preferences. It references a purchase history database to identify brands of milk previously purchased. The server generates a product list based on this information, creating suggestions such as "We have these brands of milk: Brand A, Brand B, Brand C." This product list is sent to the terminal and displayed on the screen so that the user can visually confirm it. The user selects the desired product from the displayed products.

[0910] Input: User's past purchase history, preferences

[0911] Data processing: Analysis of purchase history, creation of product list

[0912] Output: Product suggestion list

[0913] Step 5: Finalize and submit your order

[0914] After the user selects the product they wish to purchase, the terminal displays a final confirmation screen. This screen displays details of the selected product and includes a "Confirm Order" button. When the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company's API, completing the order process.

[0915] Input: Product selected by user, press of final confirmation button

[0916] Data processing: Sending order details, calling API

[0917] Output: Order processing completed

[0918] The above specific processing steps make it easy for even elderly people and users with low digital literacy to shop online.

[0919] (Application example 1)

[0920] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0921] Conventional online shopping systems have the problem that the ordering process is complicated and difficult to use for elderly people and users with low digital literacy. Furthermore, product selection and suggestions are standardized, making it difficult to flexibly respond to user preferences. In particular, the lack of a dialogue-based ordering system using natural language has left many users feeling uneasy about operation and leading to operational errors.

[0922] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0923] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order products in a natural conversational format; means for analyzing the user's order details and generating a response using a generative AI model; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected and make a final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for the natural language processing means to generate a prompt sentence according to the order details using the generative AI model; and means for displaying a prioritized list of suggested products. This enables even elderly people and users with low digital literacy to easily shop online in a natural conversational format.

[0924] A "means for users to enter authentication information to log in to their account" means providing an interface for users to enter the authentication information (such as username and password) required to access their account.

[0925] The "means for transmitting the authentication information to a server and performing user authentication" means transmitting the input authentication information to a server, and having the server verify the authenticity of the user based on that information.

[0926] "Means for encouraging authenticated users to order products in a natural conversational manner" means allowing a user who has successfully logged in to begin the process of ordering products in a natural language conversational manner.

[0927] "Means for analyzing the user's order and generating a response using a generative AI model" refers to means for analyzing the order entered by the user and generating an appropriate response using a generative AI model (such as GPT-3).

[0928] "Means for suggesting products to users" refers to means for suggesting appropriate products and services to users based on the user's input and past purchase history.

[0929] The "means for displaying a list of proposed products on a screen" refers to a means for displaying the proposed products and services on a display screen so that the proposed products and services can be visually confirmed.

[0930] "Means for ordering the products selected by the user and making final confirmation" refers to the means for the user to make final confirmation of the products or services selected and confirm the order.

[0931] "Means for sending the order details after final confirmation to the mail order company and completing the order" refers to the means by which the user sends the order details that have been finally confirmed to the mail order company and completes the processing of the order.

[0932] "Means for the natural language processing means to use a generative AI model to generate a prompt sentence according to the order content" refers to means for analyzing the user's order content and then using a generative AI model to generate an appropriate prompt sentence.

[0933] "Means for prioritizing and displaying a list of suggested products" refers to means for ranking suggested products or services based on importance or relevance and displaying them on the screen in that order.

[0934] The present invention relates to a system that allows users to conduct online shopping in a natural, conversational style. This system provides an interface that is easy for users to operate and is designed to be easy to use, especially for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[0935] User authentication

[0936] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0937] Order in a natural conversational style

[0938] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order sushi." Once the entry is complete, the device sends the input to the server.

[0939] Input analysis and response generation

[0940] The server analyzes the received text and uses a generative AI model (such as GPT-3) to generate an appropriate response, such as a specific question or suggestion like, "What kind of sushi would you like?" This response is then sent back to the device and used as the display content for the user.

[0941] Generate product suggestions

[0942] The server suggests appropriate products in response to a user's request, taking into account their purchase history and preferences. For example, it generates a list of products such as "How about this vegetarian sushi?" based on the user's past orders and preferences. This list is sent to the device and displayed visually to the user. The list is also prioritized and displayed.

[0943] View product suggestions

[0944] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[0945] Final confirmation and submission of order

[0946] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[0947] Hardware and software used

[0948] The system is implemented using the following hardware and software.

[0949] Hardware: End devices such as smartphones, tablets, and PCs.

[0950] Software: Python, natural language processing libraries, OpenAI GPT-3 API.

[0951] Examples of prompt statements

[0952] Examples of prompts to input to a generative AI model:

[0953] 1. Generate a response to a user's order entry:

[0954] User input: "I want to order sushi"

[0955] GPT-3 prompt: "What kind of sushi would you like? Typical options include tuna, salmon, eel, and California rolls."

[0956] 2. Product proposal generation:

[0957] User preferences: "Likes Japanese food, prefers vegetarian options"

[0958] Purchase history: "Ordered tempura, tofu salad, vegetable sushi"

[0959] GPT-3 prompt: "Based on the user's preferences and purchase history, suggest items. Consider Japanese vegetarian options."

[0960] This system allows even elderly people and those with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the use of generative AI models allows users to operate the system seamlessly and receive more personalized service.

[0961] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0962] Step 1: User authentication

[0963] The user enters their username and password into the login screen of their smartphone or tablet. This provides the user with authentication information to access the system. The device sends the entered authentication information to the server. The server verifies the received authentication information and authenticates the user. If authentication is successful, the server sends a message of successful authentication to the device, and the device proceeds to the next step.

[0964] Input: Username and Password

[0965] Output: Authentication success message

[0966] Step 2: Natural conversational order prompts

[0967] The device presents the authenticated user with a text entry field, prompting them to enter details of the items they wish to order. The user enters their order in text form and sends a specific request, such as "I would like to order sushi," to the server.

[0968] Input: User-entered text for the order

[0969] Output: Text data sent to the server

[0970] Step 3: Parsing input and generating response

[0971] The server analyzes the received text data and generates an appropriate response using a generative AI model (e.g., GPT-3). Specifically, the server generates prompts to ask specific questions or make suggestions, such as "What kind of sushi would you like?", and then uses the generative AI model to generate a response based on the prompt. This response is then sent back to the device and displayed to the user.

[0972] Input: Text data of the user's order details

[0973] Output: Response text generated by the GPT-3 model

[0974] Step 4: Generate product suggestions

[0975] The server uses a generative AI model to make appropriate product suggestions, taking into account the user's order details, purchase history, and preferences. For example, it might generate a list of specific suggestions, such as "How about this vegetarian sushi?" This list is prioritized and sent to the device.

[0976] Input: Order details, purchase history, user preferences

[0977] Output: Text data of the suggested product list

[0978] Step 5: View product suggestions

[0979] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and the user can select the products they wish to purchase from the displayed products.

[0980] Input: Text data of the suggested product list

[0981] Output: Product list displayed on screen

[0982] Step 6: Finalize your order

[0983] The terminal displays the details of the product selected by the user and prompts for final confirmation. After the user confirms the details and presses the "Confirm Order" button, the terminal sends the order details to the server.

[0984] Input: The product selected by the user

[0985] Output: Final confirmation screen and order confirmation

[0986] Step 7: Submit and complete your order

[0987] The server receives the final order details sent from the terminal and sends them to the mail order company. Once the mail order company has completed the order processing, the server sends a message to the terminal notifying the user that the order was successful.

[0988] Input: Final confirmed order details

[0989] Output: Order successful message

[0990] This ensures that users' orders are processed reliably at each step, making it easy for even elderly people and users with low digital literacy to enjoy online shopping.

[0991] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0992] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[0993] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, a means for the user to finally confirm the products selected by the user and send them to the mail-order company, and an emotion engine that recognizes the user's emotions.

[0994] User authentication

[0995] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends this authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[0996] Natural conversation generation

[0997] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the input to the server.

[0998] Input analysis and response generation

[0999] The server analyzes the received text and generates an appropriate response using natural language processing and an emotion engine. For example, it may ask specific questions or make suggestions, such as "What brand of milk would you like?" If the emotion engine recognizes the user's emotion, it adjusts the tone and content of the response. This response is then sent back to the device and used as the display content for the user.

[1000] Generate product suggestions

[1001] In response to a user's request, the server suggests appropriate products, taking into account the user's purchase history and preferences. The emotion engine also takes into account information such as the emotional state the user was in when purchasing a particular product. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[1002] View product suggestions

[1003] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[1004] Final confirmation and submission of order

[1005] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[1006] Specific examples

[1007] For example, if a user types, "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest quick-cooking meals and easy-to-prepare kits. The tone of the response is also considered, and the suggestions are presented in a friendly format, such as, "On a day like this, how about these easy-to-prepare ingredients?"

[1008] This system allows elderly people and users with low digital literacy to easily enjoy online shopping. Combined with an emotion engine, the system can optimally respond to the user's state and provide a more personalized shopping experience.

[1009] The processing flow will be explained below.

[1010] Step 1:

[1011] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[1012] Step 2:

[1013] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[1014] Step 3:

[1015] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[1016] Step 4:

[1017] The device sends the user's input to the server, which receives the input and analyzes it using natural language processing means and an emotion engine.

[1018] Step 5:

[1019] The server uses natural language processing to analyze the user's input. At the same time, the emotion engine recognizes the user's emotion from the text. For example, if the user inputs "I need ingredients for dinner in a hurry," the emotion engine will recognize the user's emotion as "hurrying."

[1020] Step 6:

[1021] Based on the analysis, the server generates an appropriate response, such as a specific suggestion or question like, "If you're in a hurry, how about this quick meal kit?" This response is then sent back to the device and displayed to the user.

[1022] Step 7:

[1023] The terminal displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further information about the displayed product or question.

[1024] Step 8:

[1025] The server generates product suggestions based on the user's additional input, taking into account the user's past purchase history, preferences, and emotional information obtained from the emotion engine to generate an appropriate product list.

[1026] Step 9:

[1027] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[1028] Step 10:

[1029] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[1030] Step 11:

[1031] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[1032] Step 12:

[1033] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[1034] Step 13:

[1035] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[1036] Example 2

[1037] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1038] Modern online shopping systems present a problem: elderly users and those with low digital literacy find them difficult to use. Users are particularly prone to feeling stressed during the product selection and ordering process, which reduces their willingness to purchase. Furthermore, traditional systems do not take users' emotions into consideration and can only provide a uniform response, making it difficult to provide a user-friendly experience.

[1039] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a natural language processing means for analyzing the contents of the user's order and generating an appropriate response, a means including an emotion engine for recognizing the user's emotion and adjusting the response based on the emotion, and a means for suggesting products to the user. This makes it possible to provide a friendly shopping experience that is easy to use even for elderly people and users with low digital literacy and that takes the user's emotions into consideration.

[1040] "User authentication" is the process of verifying the authentication information (such as username and password) entered by a user when accessing a system to verify that the user is a legitimate account holder.

[1041] A "natural conversational style" is a style of interaction that allows a user to interact with a computer in a natural way, just like a conversation between humans.

[1042] "Natural language processing" is a technology that allows computers to understand, interpret, and generate human language, and is used for text analysis and response generation.

[1043] An "emotion engine" is a technology that recognizes emotions from user input text, etc., and adjusts the system's responses and suggestions based on those emotions.

[1044] "Product suggestion" is the process of selecting and suggesting suitable products to a user based on their input, past purchase history, and emotional state.

[1045] "Final confirmation" is the process by which the user finally confirms and confirms the order details of the products selected by the user.

[1046] A "mail order company" is a company or business that receives orders and provides products.

[1047] "Digital literacy" is the ability to understand and effectively use digital technologies, including the internet and computers.

[1048] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[1049] User authentication

[1050] The terminal first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the terminal sends this authentication information to the server. The server then verifies the received authentication information and authenticates that the user is a legitimate account holder. This process uses a web browser or mobile application as the user interface, and a database management system such as SQL is used on the server side to access the database.

[1051] Natural conversation generation

[1052] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the information to the server. The HTTP protocol is used for data communication.

[1053] Input analysis and response generation

[1054] The server analyzes the received text and generates an appropriate response using natural language processing means and an emotion engine. Natural language processing libraries such as NLTK and spaCy are used as natural language processing means, and Microsoft Azure's Text Analytics API and Google Cloud Natural Language API are often used as emotion engines. For example, specific questions and suggestions such as "What brand of milk would you like?" are asked. If the emotion engine recognizes the user's emotion, the tone and content of the response are adjusted. This response is then sent back to the device and used as the content to be displayed to the user.

[1055] Generate product suggestions

[1056] In response to a user's request, the server suggests appropriate products, taking into account their purchasing history and preferences. This is done using a recommendation algorithm that uses past database references and machine learning models. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[1057] View product suggestions

[1058] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them. The user selects the products they want to purchase from the displayed products. Dynamic web pages using HTML and JavaScript are often used here.

[1059] Final confirmation and submission of order

[1060] A screen for the user to make a final confirmation of the products they have selected is displayed on the terminal. After the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order processing. This final data transmission is done via email or API call.

[1061] Specific examples

[1062] For example, if a user inputs "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest foods that can be prepared in a short time or easy-to-prepare kits. The tone of the response is also taken into consideration, and the suggestions are presented in a friendly format, such as "On a day like this, how about these easy-to-prepare ingredients?" This system makes it easy for elderly people and users with low digital literacy to enjoy online shopping. By combining the emotion engine, the system can optimally respond to the user's condition and provide a more personalized shopping experience.

[1063] Prompt Sentence Examples

[1064] In this system, an example of a prompt sentence to be input to the generative AI model is shown below.

[1065] User Input: "I'm tired and want to prepare a quick dinner."

[1066] Expected AI response:

[1067] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[1068] In this way, the generative AI model takes into account the user's emotional state and generates more appropriate and relatable responses.

[1069] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1070] Step 1: Displaying the user authentication screen

[1071] The terminal displays a login screen to the user and prompts them to enter their username and password. The input interface uses HTML and JavaScript. Input from the user is received in the following format:

[1072] Input: Username and Password

[1073] Output: Form data including entered authentication information

[1074] Step 2: Submit user credentials

[1075] When a user enters their authentication information and presses the login button, the device sends this information to the server as an HTTP POST request, often an AJAX request.

[1076] Input: The credentials entered by the user (username and password)

[1077] Output: Authentication information sent to the server

[1078] Step 3: Verify authentication

[1079] The server checks the received authentication information against a database to verify that the user is a valid account holder. It searches the database using an SQL query, and if the authentication information matches, it returns a successful authentication.

[1080] Input: Submitted credentials

[1081] Output: Authentication success or failure status

[1082] Step 4: Screen displayed after successful authentication

[1083] After the server returns a successful authentication, the device presents the user with a screen containing a text input field and instructions, using additional JavaScript and CSS to dynamically modify the user interface.

[1084] Input: Authentication success status

[1085] Output: Order entry screen

[1086] Step 5: Enter your order details

[1087] The user enters the order details or question in text format. For example, they might enter "I would like to order milk." This input data is used in the next step.

[1088] Input: User text input

[1089] Output: Text data of the entered order details

[1090] Step 6: Submit your order

[1091] The terminal sends the entered order details to the server, again using an AJAX request.

[1092] Input: User text input

[1093] Output: Order details sent to the server

[1094] Step 7: Text analysis and response generation

[1095] The server analyzes the received text using natural language processing tools (such as NLTK or spaCy), and simultaneously uses an emotion engine (such as Microsoft Azure or Google Cloud Natural Language API) to recognize the user's emotions and adjust the tone and content of the response. Generative AI models are also used.

[1096] Input: Text data of the order details sent

[1097] Output: Analysis results and appropriate response data

[1098] Step 8: Generate product suggestions

[1099] Based on the recognized emotions and order details, the server generates product suggestions that take into account purchase history and preferences, using recommendation algorithms and database queries.

[1100] Input: Analysis results, sentiment data, past purchase data

[1101] Output: A list of product suggestions for the user

[1102] Step 9: View product suggestions

[1103] The terminal displays the product suggestion list sent from the server on the screen and allows the user to select. In this process, a product list is dynamically generated and displayed on the screen.

[1104] Input: Product suggestion list

[1105] Output: A visual representation of the product suggestions

[1106] Step 10: Product selection and final confirmation

[1107] The user selects the items they wish to purchase from the displayed items and then confirms their purchase on a final screen, where the order list and total price are displayed.

[1108] Input: Select from product suggestion list

[1109] Output: Final confirmation screen

[1110] Step 11: Submit the final order

[1111] When the user presses the "Confirm Order" button, the terminal sends the order details to the server, which then receives the details and sends them to the mail order company.

[1112] Input: Final confirmed order details

[1113] Output: Order data sent to mail order company

[1114] Example prompt sentences

[1115] User Input: "I'm tired and want to prepare a quick dinner."

[1116] Expected AI response:

[1117] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[1118] By explaining each processing step in detail in this way, it is possible to concretely understand how the present system operates.

[1119] (Application example 2)

[1120] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1121] One problem that elderly people and users with low digital literacy face when trying to easily use online shopping is that conventional shopping systems require complicated operations and procedures, making them unfriendly. Furthermore, they lack appropriate responses and product suggestions based on the user's emotional state, making it difficult to achieve a personalized shopping experience.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order a product in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means including an emotion engine that recognizes the user's emotional state and adjusts the response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and make a final confirmation; and means for transmitting the order details after final confirmation to a mail-order company and completing the order. This enables easy and personalized online shopping through natural conversation and responses according to the user's emotional state, even for elderly people and users with low digital literacy.

[1123] "User authentication" is the process of sending the authentication information required for a user to log in to an account to a server to verify that the user is a legitimate account holder.

[1124] "Natural conversational style" refers to a natural language communication style that allows users to interact with a system through text input or voice input.

[1125] "Natural language processing" is a technology that enables computers to understand and analyze human language (natural language) and generate appropriate responses.

[1126] An "emotion engine" is a system that recognizes a user's emotional state from their input, voice data, facial expressions, etc., and adjusts the response accordingly.

[1127] "Product suggestion" is a process of selecting and presenting suitable products to a user, taking into account the user's past purchase history and current emotional state.

[1128] "Screen display" refers to the means by which the information presented by the system and the list of suggested products are visually displayed to the user.

[1129] "Final confirmation" is a procedure for confirming and finalizing the order details of the products selected by the user.

[1130] "Submitting an order" is the procedure of sending the order details that have been finally confirmed by the user to the mail order company, thereby completing the order processing.

[1131] "Elderly people" are generally people aged 65 or over.

[1132] "Digital literacy" is the ability to search for, use, generate and evaluate information using digital devices, including computers and the internet.

[1133] "Online shopping" is the act of purchasing goods and services using the Internet.

[1134] The present invention relates to a system that allows even elderly people and users with low digital literacy to easily shop online. Specific embodiments for carrying out the present invention will be described below.

[1135] System Overview

[1136] The system of the present invention comprises a terminal through which a user inputs authentication information for logging into an account, means for transmitting the authentication information to a server and performing user authentication, means for prompting the authenticated user to order products in a natural conversational manner, natural language processing means for analyzing the contents of the user's order and generating an appropriate response, an emotion engine for recognizing the user's emotional state and adjusting the response, means for suggesting products to the user, means for displaying a list of suggested products on a screen, means for ordering products selected by the user and making a final confirmation, and means for transmitting the order details after final confirmation to a mail order company and completing the order.

[1137] Hardware and Software

[1138] The system is implemented using the following hardware and software:

[1139] Hardware:

[1140] Smartphone: Responsible for user input and display of responses.

[1141] Server: Performs authentication verification, natural language processing, product recommendations, and emotion recognition.

[1142] software:

[1143] Natural Language Processing (NLPProcessor): Analyzes user text or voice input and generates a response.

[1144] Emotion Engine: Detects the emotional state of the user from their input and tailors the response.

[1145] Communication module: Sends and receives data between the server and smartphone.

[1146] Natural Language Processing and Emotion Recognition

[1147] The server uses an NLP processor to analyze the text and voice data entered by the user to understand the product the user wants and the question they are asking. The Emotion Engine then detects the user's emotional state from the voice and text and tailors the response to convey sincerity and familiarity.

[1148] Specific examples

[1149] For example, if a user says "I want fresh vegetables," the NLP Processor analyzes the input and generates a list of fresh vegetables. At the same time, the Emotion Engine detects positive emotions and suggests "How about some fresh spinach?" based on the user's needs.

[1150] Also, if a user types, "I'm tired today," the Emotion Engine takes that emotional state into account and generates a response such as, "On a day like this, I recommend a simple vegetable mix."

[1151] Example prompt sentence:

[1152] User input: "I want fresh vegetables"

[1153] Emotional state: Positive

[1154] Output prompt: "Today's special is fresh spinach."

[1155] User input: "I'm tired today"

[1156] Emotional state: Fatigue

[1157] Output prompt: "How about this easy-to-prepare vegetable mix for a day like this?"

[1158] This system allows users to easily shop online and receive appropriate product suggestions based on their emotions, providing a comfortable and personalized shopping experience.

[1159] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1160] Step 1:

[1161] The user enters a username and password into the authentication information input screen on their smartphone. The device sends this authentication information to the server. The input data is the username and password, and the output data is the authentication request sent to the server.

[1162] Step 2:

[1163] The server verifies the received authentication information and verifies whether the user is a valid account holder. In this process, the input is the authentication information and the output is the authentication success or failure status. Specifically, the server queries the database to verify that the information matches.

[1164] Step 3:

[1165] If authentication is successful, the terminal will present the user with a text entry field and voice input options for entering orders or questions. The input data is the authentication success status, and the output data is the input screen.

[1166] Step 4:

[1167] The user inputs the product they wish to order and their question into the terminal in text or voice format. The input data is the user's order or question, and the output data is the input text data or voice data.

[1168] Step 5:

[1169] The device sends the user's input text or voice data to the server. The input is the text or voice data, and the output is a request sent to the server.

[1170] Step 6:

[1171] The server uses natural language processing (NLPProcessor) to analyze the received data. The input data is text data or audio data, and the output data is a candidate response as a result of the analysis. Specifically, the NLPProcessor performs grammatical and semantic analysis.

[1172] Step 7:

[1173] The server uses an emotion engine to recognize the emotional state from the user's input. The input data is text data or voice data, and the output data is the classification result of the emotional state. The emotion engine analyzes emotions from, for example, the tone of voice and word choice.

[1174] Step 8:

[1175] The server generates a response based on the user's emotions based on the analysis results of the NLP Processor and the emotional state of the Emotion Engine. The input data is the analysis results and the emotional state, and the output data is the adjusted response. Specifically, if the emotional state is positive, the response is adjusted to use more friendly language.

[1176] Step 9:

[1177] The server considers the user's past purchase history and current emotional state to suggest appropriate products. The input data is the user's purchase history and emotional state, and the output data is a list of suggested products. Specifically, the server selects products of high interest from the user's purchase history and adds promotional text according to the user's emotional state.

[1178] Step 10:

[1179] The server sends a list of suggested products to the terminal, which then displays it on the screen. The input data is the product list, and the output data is the product list screen that is displayed. Specifically, the terminal displays the list in a visually easy-to-understand format.

[1180] Step 11:

[1181] The user selects the product they want to purchase from the displayed products and confirms the order. The input data is the selected product, and the output data is the final confirmation screen and the user's confirmation operation.

[1182] Step 12:

[1183] Once the user has made a final confirmation, the terminal sends the order details to the server. The server then sends the order details to the mail order company, completing the order. The input data is the final confirmed order details, and the output data is the result of the order being sent to the mail order company. Specifically, the communication module performs the process of sending the order data.

[1184] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1185] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1186] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1187] [Fourth embodiment]

[1188] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1189] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1190] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1191] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1192] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1193] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1194] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1195] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1196] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1197] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1198] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1199] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1200] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1201] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[1202] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, and a means for the user to confirm the products selected by the user and send them to the mail order company.

[1203] User authentication

[1204] The device first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[1205] Natural conversation generation

[1206] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once the entry is complete, the device sends the input to the server.

[1207] Input analysis and response generation

[1208] The server analyzes the received text and uses natural language processing to generate an appropriate response, such as a specific question or suggestion like "What brand of milk would you like?" This response is then sent back to the device and used as the display for the user.

[1209] Generate product suggestions

[1210] The server generates a list of products based on the user's request, taking into account their purchasing history and preferences. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the terminal and displayed visually to the user.

[1211] View product suggestions

[1212] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[1213] Final confirmation and submission of order

[1214] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[1215] This system allows even elderly people and users with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the natural conversational interface allows users to operate the system without any discomfort.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[1219] Step 2:

[1220] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[1221] Step 3:

[1222] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[1223] Step 4:

[1224] The terminal sends the user's input to the server, which receives the input and analyzes it using natural language processing means.

[1225] Step 5:

[1226] The server analyzes the input and generates an appropriate response, such as a question like "What brand of milk would you like?", and sends the response to the device.

[1227] Step 6:

[1228] The device displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further input.

[1229] Step 7:

[1230] The server generates product suggestions based on detailed input from the user, taking into account the user's past purchase history and preferences to create an appropriate product list.

[1231] Step 8:

[1232] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[1233] Step 9:

[1234] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[1235] Step 10:

[1236] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[1237] Step 11:

[1238] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[1239] Step 12:

[1240] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[1241] Example 1

[1242] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1243] For elderly people and users with low digital literacy, online shopping is often complicated and difficult to operate. In particular, if appropriate options are not provided during the product selection and ordering process, there is a high risk of selecting the wrong product. Conventional online shopping systems have complex interfaces that make them difficult for users to use.

[1244] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1245] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server and performing user authentication; means for prompting the authenticated user to order products in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and for final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for suggesting products based on the user's past purchase history and preferences; means for displaying the response generated by the natural language processing means to the user; and means for analyzing the user's input and generating prompt sentences using a generative AI model. This allows users to operate the system intuitively and easily, making online shopping smooth for even elderly people and users with low digital literacy.

[1246] "Authentication Information" refers to the username and password you enter to log into your account.

[1247] "Server" refers to a computer system for processing and storing data.

[1248] "Terminal" refers to devices used by users, such as smartphones, tablets, and computers.

[1249] "User" refers to a person who uses this system to shop online.

[1250] "Natural language processing" refers to the technology of using a computer to analyze human language and generate appropriate responses.

[1251] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate and analyze natural language.

[1252] "Prompt sentence" refers to the text data input into a generative AI model.

[1253] "User authentication" refers to the process of verifying that a user has a valid account.

[1254] "Order Details" refers to the list of products that the User wishes to purchase and their detailed information.

[1255] A "mail order company" refers to a company that receives orders from users and sells products.

[1256] "Product Suggestions" refers to a list of products suggested to users based on their purchasing history and preferences.

[1257] "Final confirmation" refers to the process of final confirmation of the product selected by the user.

[1258] "Purchase history" refers to a record of products a user has purchased in the past.

[1259] "Preferences" refers to a user's tendency to prefer certain products or brands.

[1260] "Text input" refers to character data that a user inputs using a keyboard or touch panel.

[1261] "Response" refers to the appropriate response that a generative AI model gives in response to user input.

[1262] The present invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easy for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[1263] Hardware and Software

[1264] The hardware used includes the devices operated by users (smartphones, tablets, PCs, etc.) and the servers (cloud servers or physical servers) that process and store data. The software includes applications that make up the user interface, generative AI models for natural language processing (e.g., GPT-3), and APIs for connecting with mail-order companies.

[1265] User authentication

[1266] The terminal first displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the authentication information to the server. For example, if the username "user123" and password "password123" are entered, the server compares this information with its database for authentication. If authentication is successful, the server returns a "Login successful" status code and a session ID to the terminal.

[1267] Natural conversation generation

[1268] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order milk." Once this input is complete, the device sends the input to the server.

[1269] Input analysis and response generation

[1270] The server analyzes the received text using natural language processing tools. It uses a generative AI model (e.g., GPT-3) to understand the user's intent and generate an appropriate response. For example, it might generate a question like, "What brand of milk would you like?" and send it to the device.

[1271] Generate and display product suggestions

[1272] The server takes into account the user's purchase history and preferences to suggest appropriate products. For example, it generates a suggestion such as, "We have this brand of milk. Would you like it?" and sends it to the device. The device then displays a list of suggested products on the screen so that the user can visually check them. The user can then select the desired product from the displayed products.

[1273] Final confirmation and submission of order

[1274] After the user selects the product they wish to purchase, a screen for final confirmation will be displayed on the terminal. This screen displays details of the selected product and a "Confirm Order" button. When the user makes their final confirmation and presses the "Confirm Order" button, the terminal will send the order details to the server. The server will then send this order details to the mail order company and complete the order process.

[1275] Use of concrete examples and prompts

[1276] Specific examples of prompt sentences are shown below.

[1277] User input: "I want to order milk."

[1278] Prompt the AI ​​model: "The user has typed 'I would like to order milk.' Next, ask related questions to confirm the specific brand and quantity."

[1279] The prompt allows the generative AI model to ask appropriate questions or make suggestions based on the user's input, such as "What brand of milk would you like?" or "How much do you need?", and display these responses to the user.

[1280] As described above, this system is designed to enable users to shop online with an intuitive and natural interface, and is particularly notable for being easy to operate even for elderly people and users with low digital literacy.

[1281] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1282] Step 1: User authentication

[1283] The terminal displays a login screen to the user, prompting them to enter their username and password. When the user enters this information and presses the login button, the terminal sends the entered authentication information (username "user123" and password "password123") to the server. The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it generates a "login successful" status code and a session ID and sends them back to the terminal.

[1284] Input: Username, Password

[1285] Data processing: Comparison with database

[1286] Output: "Login successful" status code and session ID

[1287] Step 2: Start a conversation

[1288] After authentication is complete, the device displays a text entry field and allows the user to begin the product ordering conversation. The entry field appears with the message "Please enter the product you would like to order." The user enters the specific order details in text format, such as "I would like to order milk." Once the entry is complete, the device sends the text content to the server.

[1289] Input: User's order text (e.g. "I'd like to order milk")

[1290] Data processing: Sending text data

[1291] Output: Send text data to the server

[1292] Step 3: Parsing input and generating response

[1293] The server analyzes the received text using natural language processing. A generative AI model (e.g., GPT-3) is used to understand the intent of the user's input and generate an appropriate response. In this process, a prompt sentence is input into the AI ​​model for analysis. For example, a prompt sentence could be, "The user inputs 'I would like to order milk.' Please ask related questions next to confirm the specific brand and quantity." The server generates a generated response, "What brand of milk would you like?", and sends it to the device.

[1294] Input: User text input, prompt

[1295] Data processing: natural language processing, intent analysis

[1296] Output: The generated response (e.g., "What brand of milk would you like?")

[1297] Step 4: Generate and display product suggestions

[1298] The server suggests appropriate products by taking into account the user's past purchase history and preferences. It references a purchase history database to identify brands of milk previously purchased. The server generates a product list based on this information, creating suggestions such as "We have these brands of milk: Brand A, Brand B, Brand C." This product list is sent to the terminal and displayed on the screen so that the user can visually confirm it. The user selects the desired product from the displayed products.

[1299] Input: User's past purchase history, preferences

[1300] Data processing: Analysis of purchase history, creation of product list

[1301] Output: Product suggestion list

[1302] Step 5: Finalize and submit your order

[1303] After the user selects the product they wish to purchase, the terminal displays a final confirmation screen. This screen displays details of the selected product and includes a "Confirm Order" button. When the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company's API, completing the order process.

[1304] Input: Product selected by user, press of final confirmation button

[1305] Data processing: Sending order details, calling API

[1306] Output: Order processing completed

[1307] The above specific processing steps make it easy for even elderly people and users with low digital literacy to shop online.

[1308] (Application example 1)

[1309] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1310] Conventional online shopping systems have the problem that the ordering process is complicated and difficult to use for elderly people and users with low digital literacy. Furthermore, product selection and suggestions are standardized, making it difficult to flexibly respond to user preferences. In particular, the lack of a dialogue-based ordering system using natural language has left many users feeling uneasy about operation and leading to operational errors.

[1311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1312] In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order products in a natural conversational format; means for analyzing the user's order details and generating a response using a generative AI model; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected and make a final confirmation; means for transmitting the order details after final confirmation to the mail order company and completing the order; means for the natural language processing means to generate a prompt sentence according to the order details using the generative AI model; and means for displaying a prioritized list of suggested products. This enables even elderly people and users with low digital literacy to easily shop online in a natural conversational format.

[1313] A "means for users to enter authentication information to log in to their account" means providing an interface for users to enter the authentication information (such as username and password) required to access their account.

[1314] The "means for transmitting the authentication information to a server and performing user authentication" means transmitting the input authentication information to a server, and having the server verify the authenticity of the user based on that information.

[1315] "Means for encouraging authenticated users to order products in a natural conversational manner" means allowing a user who has successfully logged in to begin the process of ordering products in a natural language conversational manner.

[1316] "Means for analyzing the user's order and generating a response using a generative AI model" refers to means for analyzing the order entered by the user and generating an appropriate response using a generative AI model (such as GPT-3).

[1317] "Means for suggesting products to users" refers to means for suggesting appropriate products and services to users based on the user's input and past purchase history.

[1318] The "means for displaying a list of proposed products on a screen" refers to a means for displaying the proposed products and services on a display screen so that the proposed products and services can be visually confirmed.

[1319] "Means for ordering the products selected by the user and making final confirmation" refers to the means for the user to make final confirmation of the products or services selected and confirm the order.

[1320] "Means for sending the order details after final confirmation to the mail order company and completing the order" refers to the means by which the user sends the order details that have been finally confirmed to the mail order company and completes the processing of the order.

[1321] "Means for the natural language processing means to use a generative AI model to generate a prompt sentence according to the order content" refers to means for analyzing the user's order content and then using a generative AI model to generate an appropriate prompt sentence.

[1322] "Means for prioritizing and displaying a list of suggested products" refers to means for ranking suggested products or services based on importance or relevance and displaying them on the screen in that order.

[1323] The present invention relates to a system that allows users to conduct online shopping in a natural, conversational style. This system provides an interface that is easy for users to operate and is designed to be easy to use, especially for elderly people and users with low digital literacy. Specific embodiments for implementing this system are described below.

[1324] User authentication

[1325] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends the authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[1326] Order in a natural conversational style

[1327] After authentication is complete, the device displays a text entry field and initiates a conversation for the user to order a product. The user enters the product and its details in text format. For example, they might enter "I would like to order sushi." Once the entry is complete, the device sends the input to the server.

[1328] Input analysis and response generation

[1329] The server analyzes the received text and uses a generative AI model (such as GPT-3) to generate an appropriate response, such as a specific question or suggestion like, "What kind of sushi would you like?" This response is then sent back to the device and used as the display content for the user.

[1330] Generate product suggestions

[1331] The server suggests appropriate products in response to a user's request, taking into account their purchase history and preferences. For example, it generates a list of products such as "How about this vegetarian sushi?" based on the user's past orders and preferences. This list is sent to the device and displayed visually to the user. The list is also prioritized and displayed.

[1332] View product suggestions

[1333] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[1334] Final confirmation and submission of order

[1335] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[1336] Hardware and software used

[1337] The system is implemented using the following hardware and software.

[1338] Hardware: End devices such as smartphones, tablets, and PCs.

[1339] Software: Python, natural language processing libraries, OpenAI GPT-3 API.

[1340] Examples of prompt statements

[1341] Examples of prompts to input to a generative AI model:

[1342] 1. Generate a response to a user's order entry:

[1343] User input: "I want to order sushi"

[1344] GPT-3 prompt: "What kind of sushi would you like? Typical options include tuna, salmon, eel, and California rolls."

[1345] 2. Product proposal generation:

[1346] User preferences: "Likes Japanese food, prefers vegetarian options"

[1347] Purchase history: "Ordered tempura, tofu salad, vegetable sushi"

[1348] GPT-3 prompt: "Based on the user's preferences and purchase history, suggest items. Consider Japanese vegetarian options."

[1349] This system allows even elderly people and those with low digital literacy to easily enjoy online shopping, simplifying the process from selecting products to placing an order. In addition, the use of generative AI models allows users to operate the system seamlessly and receive more personalized service.

[1350] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1351] Step 1: User authentication

[1352] The user enters their username and password into the login screen of their smartphone or tablet. This provides the user with authentication information to access the system. The device sends the entered authentication information to the server. The server verifies the received authentication information and authenticates the user. If authentication is successful, the server sends a message of successful authentication to the device, and the device proceeds to the next step.

[1353] Input: Username and Password

[1354] Output: Authentication success message

[1355] Step 2: Natural conversational order prompts

[1356] The device presents the authenticated user with a text entry field, prompting them to enter details of the items they wish to order. The user enters their order in text form and sends a specific request, such as "I would like to order sushi," to the server.

[1357] Input: User-entered text for the order

[1358] Output: Text data sent to the server

[1359] Step 3: Parsing input and generating response

[1360] The server analyzes the received text data and generates an appropriate response using a generative AI model (e.g., GPT-3). Specifically, the server generates prompts to ask specific questions or make suggestions, such as "What kind of sushi would you like?", and then uses the generative AI model to generate a response based on the prompt. This response is then sent back to the device and displayed to the user.

[1361] Input: Text data of the user's order details

[1362] Output: Response text generated by the GPT-3 model

[1363] Step 4: Generate product suggestions

[1364] The server uses a generative AI model to make appropriate product suggestions, taking into account the user's order details, purchase history, and preferences. For example, it might generate a list of specific suggestions, such as "How about this vegetarian sushi?" This list is prioritized and sent to the device.

[1365] Input: Order details, purchase history, user preferences

[1366] Output: Text data of the suggested product list

[1367] Step 5: View product suggestions

[1368] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and the user can select the products they wish to purchase from the displayed products.

[1369] Input: Text data of the suggested product list

[1370] Output: Product list displayed on screen

[1371] Step 6: Finalize your order

[1372] The terminal displays the details of the product selected by the user and prompts for final confirmation. After the user confirms the details and presses the "Confirm Order" button, the terminal sends the order details to the server.

[1373] Input: The product selected by the user

[1374] Output: Final confirmation screen and order confirmation

[1375] Step 7: Submit and complete your order

[1376] The server receives the final order details sent from the terminal and sends them to the mail order company. Once the mail order company has completed the order processing, the server sends a message to the terminal notifying the user that the order was successful.

[1377] Input: Final confirmed order details

[1378] Output: Order successful message

[1379] This ensures that users' orders are processed reliably at each step, making it easy for even elderly people and users with low digital literacy to enjoy online shopping.

[1380] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1381] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[1382] The system consists of a means for users to log in to their account by entering authentication information, a means for encouraging them to order products in a natural conversational format, a natural language processing means for analyzing the user's order details and generating an appropriate response, a means for suggesting products to the user, a means for displaying the suggested products on the screen, a means for the user to finally confirm the products selected by the user and send them to the mail-order company, and an emotion engine that recognizes the user's emotions.

[1383] User authentication

[1384] The device first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the device sends this authentication information to the server. The server then verifies the received authentication information and verifies that the user is a legitimate account holder.

[1385] Natural conversation generation

[1386] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the input to the server.

[1387] Input analysis and response generation

[1388] The server analyzes the received text and generates an appropriate response using natural language processing and an emotion engine. For example, it may ask specific questions or make suggestions, such as "What brand of milk would you like?" If the emotion engine recognizes the user's emotion, it adjusts the tone and content of the response. This response is then sent back to the device and used as the display content for the user.

[1389] Generate product suggestions

[1390] In response to a user's request, the server suggests appropriate products, taking into account the user's purchase history and preferences. The emotion engine also takes into account information such as the emotional state the user was in when purchasing a particular product. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[1391] View product suggestions

[1392] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them, and then select the products they wish to purchase from the displayed products.

[1393] Final confirmation and submission of order

[1394] The user selects the product they wish to purchase and a screen for final confirmation is displayed on the terminal. After the user makes the final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order process.

[1395] Specific examples

[1396] For example, if a user types, "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest quick-cooking meals and easy-to-prepare kits. The tone of the response is also considered, and the suggestions are presented in a friendly format, such as, "On a day like this, how about these easy-to-prepare ingredients?"

[1397] This system allows elderly people and users with low digital literacy to easily enjoy online shopping. Combined with an emotion engine, the system can optimally respond to the user's state and provide a more personalized shopping experience.

[1398] The processing flow will be explained below.

[1399] Step 1:

[1400] The terminal displays a login screen to the user and prompts them to enter their username and password. When the user enters this information and presses the login button, the terminal sends this authentication information to the server.

[1401] Step 2:

[1402] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the server sends a message of authentication success to the terminal.

[1403] Step 3:

[1404] After successful authentication, the terminal displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format.

[1405] Step 4:

[1406] The device sends the user's input to the server, which receives the input and analyzes it using natural language processing means and an emotion engine.

[1407] Step 5:

[1408] The server uses natural language processing to analyze the user's input. At the same time, the emotion engine recognizes the user's emotion from the text. For example, if the user inputs "I need ingredients for dinner in a hurry," the emotion engine will recognize the user's emotion as "hurrying."

[1409] Step 6:

[1410] Based on the analysis, the server generates an appropriate response, such as a specific suggestion or question like, "If you're in a hurry, how about this quick meal kit?" This response is then sent back to the device and displayed to the user.

[1411] Step 7:

[1412] The terminal displays the response received from the server on the screen and presents the question or suggestion to the user, who then enters further information about the displayed product or question.

[1413] Step 8:

[1414] The server generates product suggestions based on the user's additional input, taking into account the user's past purchase history, preferences, and emotional information obtained from the emotion engine to generate an appropriate product list.

[1415] Step 9:

[1416] The server sends the generated product suggestions to the terminal, which displays the product list on the screen so that the user can visually check it.

[1417] Step 10:

[1418] The user selects the products they wish to purchase from the displayed items and enters their selection into the terminal, which then transmits it to the server.

[1419] Step 11:

[1420] The server prepares a final confirmation of the order based on the product selected by the user, sends the order details to the terminal, and displays a message prompting the user to confirm.

[1421] Step 12:

[1422] The user confirms the order details and presses the "Confirm Order" button. The device then sends the details to the server.

[1423] Step 13:

[1424] The server sends the confirmed order details to the mail order company and completes the order process. The user is then notified that the order has been completed.

[1425] Example 2

[1426] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1427] Modern online shopping systems present a problem: elderly users and those with low digital literacy find them difficult to use. Users are particularly prone to feeling stressed during the product selection and ordering process, which reduces their willingness to purchase. Furthermore, traditional systems do not take users' emotions into consideration and can only provide a uniform response, making it difficult to provide a user-friendly experience.

[1428] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a natural language processing means for analyzing the contents of the user's order and generating an appropriate response, a means including an emotion engine for recognizing the user's emotion and adjusting the response based on the emotion, and a means for suggesting products to the user. This makes it possible to provide a friendly shopping experience that is easy to use even for elderly people and users with low digital literacy and that takes the user's emotions into consideration.

[1429] "User authentication" is the process of verifying the authentication information (such as username and password) entered by a user when accessing a system to verify that the user is a legitimate account holder.

[1430] A "natural conversational style" is a style of interaction that allows a user to interact with a computer in a natural way, just like a conversation between humans.

[1431] "Natural language processing" is a technology that allows computers to understand, interpret, and generate human language, and is used for text analysis and response generation.

[1432] An "emotion engine" is a technology that recognizes emotions from user input text, etc., and adjusts the system's responses and suggestions based on those emotions.

[1433] "Product suggestion" is the process of selecting and suggesting suitable products to a user based on their input, past purchase history, and emotional state.

[1434] "Final confirmation" is the process by which the user finally confirms and confirms the order details of the products selected by the user.

[1435] A "mail order company" is a company or business that receives orders and provides products.

[1436] "Digital literacy" is the ability to understand and effectively use digital technologies, including the internet and computers.

[1437] This invention relates to an online shopping system that utilizes conversation-based AI, designed to make online shopping easier for elderly people and users with low digital literacy. Furthermore, the system provides a more user-friendly experience by combining it with an emotion engine that recognizes user emotions and adjusts responses based on those emotions.

[1438] User authentication

[1439] The terminal first displays a login screen to the user, prompting them to enter their username and password. After the user enters this information and presses the login button, the terminal sends this authentication information to the server. The server then verifies the received authentication information and authenticates that the user is a legitimate account holder. This process uses a web browser or mobile application as the user interface, and a database management system such as SQL is used on the server side to access the database.

[1440] Natural conversation generation

[1441] After authentication is complete, the device displays a text entry field and prompts the user to enter an order or question. The user enters the product and details they want to order in text format. For example, they might enter "I'd like to order milk." Once the entry is complete, the device sends the information to the server. The HTTP protocol is used for data communication.

[1442] Input analysis and response generation

[1443] The server analyzes the received text and generates an appropriate response using natural language processing means and an emotion engine. Natural language processing libraries such as NLTK and spaCy are used as natural language processing means, and Microsoft Azure's Text Analytics API and Google Cloud Natural Language API are often used as emotion engines. For example, specific questions and suggestions such as "What brand of milk would you like?" are asked. If the emotion engine recognizes the user's emotion, the tone and content of the response are adjusted. This response is then sent back to the device and used as the content to be displayed to the user.

[1444] Generate product suggestions

[1445] In response to a user's request, the server suggests appropriate products, taking into account their purchasing history and preferences. This is done using a recommendation algorithm that uses past database references and machine learning models. For example, it generates a list of products such as, "We have this brand of milk. Would you like it?" This list is sent to the device and displayed visually to the user.

[1446] View product suggestions

[1447] The terminal displays the list of product suggestions sent from the server on the screen so that the user can visually check them. The user selects the products they want to purchase from the displayed products. Dynamic web pages using HTML and JavaScript are often used here.

[1448] Final confirmation and submission of order

[1449] A screen for the user to make a final confirmation of the products they have selected is displayed on the terminal. After the user makes a final confirmation and presses the "Confirm Order" button, the terminal sends the order details to the server. The server then sends the order details to the mail order company and completes the order processing. This final data transmission is done via email or API call.

[1450] Specific examples

[1451] For example, if a user inputs "I'm tired, so I want to prepare a quick dinner," the emotion engine will recognize the user's emotion and suggest foods that can be prepared in a short time or easy-to-prepare kits. The tone of the response is also taken into consideration, and the suggestions are presented in a friendly format, such as "On a day like this, how about these easy-to-prepare ingredients?" This system makes it easy for elderly people and users with low digital literacy to enjoy online shopping. By combining the emotion engine, the system can optimally respond to the user's condition and provide a more personalized shopping experience.

[1452] Prompt Sentence Examples

[1453] In this system, an example of a prompt sentence to be input to the generative AI model is shown below.

[1454] User Input: "I'm tired and want to prepare a quick dinner."

[1455] Expected AI response:

[1456] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[1457] In this way, the generative AI model takes into account the user's emotional state and generates more appropriate and relatable responses.

[1458] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1459] Step 1: Displaying the user authentication screen

[1460] The terminal displays a login screen to the user and prompts them to enter their username and password. The input interface uses HTML and JavaScript. Input from the user is received in the following format:

[1461] Input: Username and Password

[1462] Output: Form data including entered authentication information

[1463] Step 2: Submit user credentials

[1464] When a user enters their authentication information and presses the login button, the device sends this information to the server as an HTTP POST request, often an AJAX request.

[1465] Input: The credentials entered by the user (username and password)

[1466] Output: Authentication information sent to the server

[1467] Step 3: Verify authentication

[1468] The server checks the received authentication information against a database to verify that the user is a valid account holder. It searches the database using an SQL query, and if the authentication information matches, it returns a successful authentication.

[1469] Input: Submitted credentials

[1470] Output: Authentication success or failure status

[1471] Step 4: Screen displayed after successful authentication

[1472] After the server returns a successful authentication, the device presents the user with a screen containing a text input field and instructions, using additional JavaScript and CSS to dynamically modify the user interface.

[1473] Input: Authentication success status

[1474] Output: Order entry screen

[1475] Step 5: Enter your order details

[1476] The user enters the order details or question in text format. For example, they might enter "I would like to order milk." This input data is used in the next step.

[1477] Input: User text input

[1478] Output: Text data of the entered order details

[1479] Step 6: Submit your order

[1480] The terminal sends the entered order details to the server, again using an AJAX request.

[1481] Input: User text input

[1482] Output: Order details sent to the server

[1483] Step 7: Text analysis and response generation

[1484] The server analyzes the received text using natural language processing tools (such as NLTK or spaCy), and simultaneously uses an emotion engine (such as Microsoft Azure or Google Cloud Natural Language API) to recognize the user's emotions and adjust the tone and content of the response. Generative AI models are also used.

[1485] Input: Text data of the order details sent

[1486] Output: Analysis results and appropriate response data

[1487] Step 8: Generate product suggestions

[1488] Based on the recognized emotions and order details, the server generates product suggestions that take into account purchase history and preferences, using recommendation algorithms and database queries.

[1489] Input: Analysis results, sentiment data, past purchase data

[1490] Output: A list of product suggestions for the user

[1491] Step 9: View product suggestions

[1492] The terminal displays the product suggestion list sent from the server on the screen and allows the user to select. In this process, a product list is dynamically generated and displayed on the screen.

[1493] Input: Product suggestion list

[1494] Output: A visual representation of the product suggestions

[1495] Step 10: Product selection and final confirmation

[1496] The user selects the items they wish to purchase from the displayed items and then confirms their purchase on a final screen, where the order list and total price are displayed.

[1497] Input: Select from product suggestion list

[1498] Output: Final confirmation screen

[1499] Step 11: Submit the final order

[1500] When the user presses the "Confirm Order" button, the terminal sends the order details to the server, which then receives the details and sends them to the mail order company.

[1501] Input: Final confirmed order details

[1502] Output: Order data sent to mail order company

[1503] Example prompt sentences

[1504] User Input: "I'm tired and want to prepare a quick dinner."

[1505] Expected AI response:

[1506] "On a day like this, how about these easy-to-prepare ingredients? Can you tell me the brand of milk?"

[1507] By explaining each processing step in detail in this way, it is possible to concretely understand how the present system operates.

[1508] (Application example 2)

[1509] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1510] One problem that elderly people and users with low digital literacy face when trying to easily use online shopping is that conventional shopping systems require complicated operations and procedures, making them unfriendly. Furthermore, they lack appropriate responses and product suggestions based on the user's emotional state, making it difficult to achieve a personalized shopping experience.

[1511] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input authentication information to log in to an account; means for transmitting the authentication information to the server to perform user authentication; means for prompting the authenticated user to order a product in a natural conversational format; natural language processing means for analyzing the user's order details and generating an appropriate response; means including an emotion engine that recognizes the user's emotional state and adjusts the response; means for suggesting products to the user; means for displaying a list of suggested products on a screen; means for the user to order the products selected by the user and make a final confirmation; and means for transmitting the order details after final confirmation to a mail-order company and completing the order. This enables easy and personalized online shopping through natural conversation and responses according to the user's emotional state, even for elderly people and users with low digital literacy.

[1512] "User authentication" is the process of sending the authentication information required for a user to log in to an account to a server to verify that the user is a legitimate account holder.

[1513] "Natural conversational style" refers to a natural language communication style that allows users to interact with a system through text input or voice input.

[1514] "Natural language processing" is a technology that enables computers to understand and analyze human language (natural language) and generate appropriate responses.

[1515] An "emotion engine" is a system that recognizes a user's emotional state from their input, voice data, facial expressions, etc., and adjusts the response accordingly.

[1516] "Product suggestion" is a process of selecting and presenting suitable products to a user, taking into account the user's past purchase history and current emotional state.

[1517] "Screen display" refers to the means by which the information presented by the system and the list of suggested products are visually displayed to the user.

[1518] "Final confirmation" is a procedure for confirming and finalizing the order details of the products selected by the user.

[1519] "Submitting an order" is the procedure of sending the order details that have been finally confirmed by the user to the mail order company, thereby completing the order processing.

[1520] "Elderly people" are generally people aged 65 or over.

[1521] "Digital literacy" is the ability to search for, use, generate and evaluate information using digital devices, including computers and the internet.

[1522] "Online shopping" is the act of purchasing goods and services using the Internet.

[1523] The present invention relates to a system that allows even elderly people and users with low digital literacy to easily shop online. Specific embodiments for carrying out the present invention will be described below.

[1524] System Overview

[1525] The system of the present invention comprises a terminal through which a user inputs authentication information for logging into an account, means for transmitting the authentication information to a server and performing user authentication, means for prompting the authenticated user to order products in a natural conversational manner, natural language processing means for analyzing the contents of the user's order and generating an appropriate response, an emotion engine for recognizing the user's emotional state and adjusting the response, means for suggesting products to the user, means for displaying a list of suggested products on a screen, means for ordering products selected by the user and making a final confirmation, and means for transmitting the order details after final confirmation to a mail order company and completing the order.

[1526] Hardware and Software

[1527] The system is implemented using the following hardware and software:

[1528] Hardware:

[1529] Smartphone: Responsible for user input and display of responses.

[1530] Server: Performs authentication verification, natural language processing, product recommendations, and emotion recognition.

[1531] software:

[1532] Natural Language Processing (NLPProcessor): Analyzes user text or voice input and generates a response.

[1533] Emotion Engine: Detects the emotional state of the user from their input and tailors the response.

[1534] Communication module: Sends and receives data between the server and smartphone.

[1535] Natural Language Processing and Emotion Recognition

[1536] The server uses an NLP processor to analyze the text and voice data entered by the user to understand the product the user wants and the question they are asking. The Emotion Engine then detects the user's emotional state from the voice and text and tailors the response to convey sincerity and familiarity.

[1537] Specific examples

[1538] For example, if a user says "I want fresh vegetables," the NLP Processor analyzes the input and generates a list of fresh vegetables. At the same time, the Emotion Engine detects positive emotions and suggests "How about some fresh spinach?" based on the user's needs.

[1539] Also, if a user types, "I'm tired today," the Emotion Engine takes that emotional state into account and generates a response such as, "On a day like this, I recommend a simple vegetable mix."

[1540] Example prompt sentence:

[1541] User input: "I want fresh vegetables"

[1542] Emotional state: Positive

[1543] Output prompt: "Today's special is fresh spinach."

[1544] User input: "I'm tired today"

[1545] Emotional state: Fatigue

[1546] Output prompt: "How about this easy-to-prepare vegetable mix for a day like this?"

[1547] This system allows users to easily shop online and receive appropriate product suggestions based on their emotions, providing a comfortable and personalized shopping experience.

[1548] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1549] Step 1:

[1550] The user enters a username and password into the authentication information input screen on their smartphone. The device sends this authentication information to the server. The input data is the username and password, and the output data is the authentication request sent to the server.

[1551] Step 2:

[1552] The server verifies the received authentication information and verifies whether the user is a valid account holder. In this process, the input is the authentication information and the output is the authentication success or failure status. Specifically, the server queries the database to verify that the information matches.

[1553] Step 3:

[1554] If authentication is successful, the terminal will present the user with a text entry field and voice input options for entering orders or questions. The input data is the authentication success status, and the output data is the input screen.

[1555] Step 4:

[1556] The user inputs the product they wish to order and their question into the terminal in text or voice format. The input data is the user's order or question, and the output data is the input text data or voice data.

[1557] Step 5:

[1558] The device sends the user's input text or voice data to the server. The input is the text or voice data, and the output is a request sent to the server.

[1559] Step 6:

[1560] The server uses natural language processing (NLPProcessor) to analyze the received data. The input data is text data or audio data, and the output data is a candidate response as a result of the analysis. Specifically, the NLPProcessor performs grammatical and semantic analysis.

[1561] Step 7:

[1562] The server uses an emotion engine to recognize the emotional state from the user's input. The input data is text data or voice data, and the output data is the classification result of the emotional state. The emotion engine analyzes emotions from, for example, the tone of voice and word choice.

[1563] Step 8:

[1564] The server generates a response based on the user's emotions based on the analysis results of the NLP Processor and the emotional state of the Emotion Engine. The input data is the analysis results and the emotional state, and the output data is the adjusted response. Specifically, if the emotional state is positive, the response is adjusted to use more friendly language.

[1565] Step 9:

[1566] The server considers the user's past purchase history and current emotional state to suggest appropriate products. The input data is the user's purchase history and emotional state, and the output data is a list of suggested products. Specifically, the server selects products of high interest from the user's purchase history and adds promotional text according to the user's emotional state.

[1567] Step 10:

[1568] The server sends a list of suggested products to the terminal, which then displays it on the screen. The input data is the product list, and the output data is the product list screen that is displayed. Specifically, the terminal displays the list in a visually easy-to-understand format.

[1569] Step 11:

[1570] The user selects the product they want to purchase from the displayed products and confirms the order. The input data is the selected product, and the output data is the final confirmation screen and the user's confirmation operation.

[1571] Step 12:

[1572] Once the user has made a final confirmation, the terminal sends the order details to the server. The server then sends the order details to the mail order company, completing the order. The input data is the final confirmed order details, and the output data is the result of the order being sent to the mail order company. Specifically, the communication module performs the process of sending the order data.

[1573] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1574] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1575] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1576] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1577] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1578] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1579] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1580] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1581] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1582] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1583] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1584] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1585] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1586] 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.

[1587] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1588] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1589] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1590] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1591] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1592] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1593] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1594] The following is further disclosed regarding the above embodiment.

[1595] (Claim 1)

[1596] A means for users to enter their credentials to log into their account;

[1597] means for transmitting the authentication information to a server to perform user authentication;

[1598] A way for authenticated users to order products in a natural conversational way,

[1599] natural language processing means for analyzing the user's order and generating an appropriate response;

[1600] A means of making product suggestions to users;

[1601] means for displaying a list of suggested products on a screen;

[1602] A means for the user to order and finalize the selected products;

[1603] The system includes a means for transmitting the order details after final confirmation to the mail order company and completing the order.

[1604] (Claim 2)

[1605] 10. The system of claim 1, wherein the natural language processing means analyzes a user's text input and generates a response.

[1606] (Claim 3)

[1607] 2. The system according to claim 1, wherein the product suggestion means suggests appropriate products by taking into consideration the user's past purchase history.

[1608] "Example 1"

[1609] (Claim 1)

[1610] A means for users to enter their credentials to log into their account;

[1611] means for transmitting the authentication information to a server to perform user authentication;

[1612] A way for authenticated users to order products in a natural conversational way,

[1613] natural language processing means for analyzing the user's order and generating an appropriate response;

[1614] A means of making product suggestions to users;

[1615] means for displaying a list of suggested products on a screen;

[1616] A means for the user to order and finalize the selected products;

[1617] A means for transmitting the final confirmed order details to the mail order company to complete the order;

[1618] A means for suggesting products based on the user's past purchase history and preferences;

[1619] means for displaying to a user the response generated by said natural language processing means;

[1620] means for analyzing the user's input using a generative AI model to generate a prompt sentence;

[1621] A system including:

[1622] (Claim 2)

[1623] 10. The system of claim 1, wherein the natural language processing means analyzes a user's text input and generates a response.

[1624] (Claim 3)

[1625] 2. The system according to claim 1, wherein the product suggestion means suggests appropriate products by taking into consideration the user's past purchase history.

[1626] "Application Example 1"

[1627] (Claim 1)

[1628] A means for users to enter their credentials to log into their account;

[1629] means for transmitting the authentication information to a server to perform user authentication;

[1630] A way for authenticated users to order products in a natural conversational way,

[1631] natural language processing means for analyzing the user's order and generating an appropriate response;

[1632] A means of making product suggestions to users;

[1633] means for displaying a list of suggested products on a screen;

[1634] A means for the user to order and finalize the selected products;

[1635] A means for transmitting the final confirmed order details to the mail order company to complete the order;

[1636] a means for generating a prompt sentence according to the order content by using a generative AI model in the natural language processing means;

[1637] a means for prioritizing and displaying a list of suggested products;

[1638] A system including:

[1639] (Claim 2)

[1640] 2. The system of claim 1, wherein the natural language processing means analyzes a user's text input and generates a response using a generative AI model.

[1641] (Claim 3)

[1642] The system according to claim 1, characterized in that the product suggestion means suggests appropriate products by taking into consideration the user's past purchase history and prompt sentences based on a generative AI model.

[1643] "Example 2: Combining Emotion Engines"

[1644] (Claim 1)

[1645] A means for users to enter their credentials to log into their account;

[1646] means for transmitting the authentication information to a server to perform user authentication;

[1647] A way for authenticated users to order products in a natural conversational way,

[1648] natural language processing means for analyzing the user's order and generating an appropriate response;

[1649] means including an emotion engine for recognizing an emotion of a user and adjusting a response based on the emotion;

[1650] A means of making product suggestions to users;

[1651] means for displaying a list of suggested products on a screen;

[1652] A means for the user to order and finalize the selected products;

[1653] The system includes a means for transmitting the order details after final confirmation to the mail order company and completing the order.

[1654] (Claim 2)

[1655] 10. The system of claim 1, wherein the natural language processing means analyzes a user's text input and generates a response.

[1656] (Claim 3)

[1657] 2. The system according to claim 1, wherein the product suggestion means suggests appropriate products taking into consideration the user's past purchase history and emotional state.

[1658] "Application example 2 when combining emotion engines"

[1659] (Claim 1)

[1660] A means for users to enter their credentials to log into their account;

[1661] means for transmitting the authentication information to a server to perform user authentication;

[1662] A way for authenticated users to order products in a natural conversational way,

[1663] natural language processing means for analyzing the user's order and generating an appropriate response;

[1664] means including an emotion engine for recognizing the user's emotional state and adjusting a response;

[1665] A means of making product suggestions to users;

[1666] means for displaying a list of suggested products on a screen;

[1667] A means for the user to order and finalize the selected products;

[1668] The system includes a means for transmitting the order details after final confirmation to the mail order company and completing the order.

[1669] (Claim 2)

[1670] 2. The system of claim 1, wherein the natural language processing means analyzes a user's text and voice inputs and generates a response.

[1671] (Claim 3)

[1672] 2. The system according to claim 1, wherein the product suggestion means suggests appropriate products taking into consideration the user's past purchase history and emotional state. [Explanation of symbols]

[1673] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to enter their credentials to log into their account; means for transmitting the authentication information to a server to perform user authentication; A way for authenticated users to order products in a natural conversational way, natural language processing means for analyzing the user's order and generating an appropriate response; A means of making product suggestions to users; means for displaying a list of suggested products on a screen; A means for the user to order and finalize the selected products; The system includes a means for transmitting the order details after final confirmation to the mail order company and completing the order.

2. 2. The system of claim 1, wherein the natural language processing means analyzes a user's text input and generates a response.

3. 2. The system according to claim 1, wherein the product suggestion means suggests appropriate products by taking into consideration the user's past purchase history.

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