System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving request information related to a commodity or service from a user, a means for analyzing the request information and extracting a hidden need of the user, a means for retrieving an optimum commodity or service from a commodity database based on the hidden need, and a means for presenting a retrieval result to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] On conventional e-commerce sites, users typically search for products based on their own knowledge and subjective judgment, compare options, and then make a purchase. However, this method can make it difficult for users to find products or services that meet their true needs due to limitations in their knowledge and information, which can result in a decrease in purchasing motivation and satisfaction. The problem that this invention aims to solve is to improve the purchasing experience on e-commerce sites by effectively identifying users' true needs and suggesting products and services that meet those needs. [Means for solving the problem]

[0005] The present invention provides a system including means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs, means for searching for optimal products and services from a product database based on the hidden needs, and means for presenting the search results to the user.The above-mentioned problems are solved by further including means for providing detailed information on the products and services in the search results and generating it in response to additional questions from the user, means for recommending personalized products and services taking into account the user's past purchase history and browsing history, means for receiving purchase decision information from the user and completing the order process, means for providing product usage instructions and warranty information as post-purchase follow-up, means for collecting user evaluations and feedback, means for providing insights necessary for product manufacturers or service providers in marketing and planning based on the collected user data, and means for generating and delivering targeted advertisements.

[0006] "User" means an individual or corporation who intends to purchase goods or services using an EC site.

[0007] "Request information" refers to the wishes and conditions related to the products or services a user wishes to purchase, which are provided through an e-commerce site or chatbot.

[0008] "Generative AI" is a type of artificial intelligence that has the ability to generate and understand natural language, and is a technology that provides appropriate information through dialogue with users.

[0009] "Analysis" is the process of interpreting the provided request information and identifying the hidden needs of the user that lie behind it.

[0010] "Hidden needs" are requirements or expectations that users do not explicitly request, but which are important in reality and should be met.

[0011] A "product database" is a data storage device for centrally managing and storing information about products and services offered on an e-commerce site.

[0012] "Search" is the process of finding relevant products and services from a product database based on the user's request information and hidden needs.

[0013] "Presenting" means visually or textually displaying to the user the products or services selected as search results.

[0014] "Detailed information" refers to information such as specific specifications, functions, and prices about the product or service selected by the user.

[0015] "Personalization" is the process of suggesting optimal products and services based on each user's specific needs and past behavioral history.

[0016] The "order process" is a series of processes that involves collecting the information necessary to officially purchase the product or service selected by the user and finalizing the purchase.

[0017] "After-sales support" is the process of providing support for the use of a product or service after the user has purchased it.

[0018] "Rating" means expressing satisfaction or opinion about a product or service purchased by a user.

[0019] "Feedback" refers to opinions, impressions, requests, etc. provided by users, and is information that can be used to improve services and develop new products.

[0020] "Insights" are analytical results and insights obtained based on collected data, and are used in marketing and product planning.

[0021] "Targeted advertising" is the process of generating and delivering advertisements optimized for specific user demographics based on collected data. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] This invention relates to an e-commerce concierge system that collects user request information, analyzes those requests to discover hidden needs, and proposes optimal products and services. This system supports users in selecting, purchasing, and following up on products and services when using an e-commerce site through a chatbot interface that uses a generative AI model.

[0044] System Overview

[0045] The system consists of the following main components:

[0046] 1. User: An individual or corporation that accesses an e-commerce site and provides information about products and services.

[0047] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0048] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[0049] Program processing flow

[0050] Step 1: Gather user needs

[0051] A user accesses an e-commerce site through a device and inputs their request, such as "I want a new smartphone." The device then sends this information to a server. The server then launches a chatbot interface using generative AI and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[0052] Step 2: Needs analysis and product proposal

[0053] The server uses a generative AI model to analyze the request information collected from the user and extract the user's hidden needs. For example, it clarifies specific needs such as "gaming use, high-performance camera, budget under 50,000 yen." Based on the results of this analysis, the server searches the e-commerce site's database for relevant products and identifies the most suitable products and services. It then presents these search results to the user.

[0054] Step 3: Provide details and purchase assistance

[0055] If the user selects an item that interests them from the displayed items and requests more detailed information, the terminal sends this request to the server. The server generates detailed information about the selected item (specs, features, price, etc.) and provides it to the user. If the user indicates their intention to purchase the item, a purchase procedure form is displayed through the terminal, and the user enters the necessary information.

[0056] Step 4: Confirm your purchase and follow-up

[0057] Once the user has completed the purchase process, the terminal sends the order information to the server. The server confirms the order and sends the user an order confirmation email and post-purchase follow-up information (such as instructions for use and warranty information). The server also collects user ratings and feedback, which it uses as data for future product suggestions and service improvements.

[0058] Specific examples

[0059] Below are some specific examples of how this system can be used.

[0060] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0061] 2. Device: Sends this information to the server.

[0062] 3. Server: Uses generative AI to ask the user, "What is your budget?"

[0063] 4. User: "It's under 50,000 yen."

[0064] 5. Server: Based on the recommendation, the server searches the EC site's database for smartphones that meet the criteria and presents candidates to the user.

[0065] 6. User: Ask a specific question like, "Can you tell me more about the camera performance of this phone?"

[0066] 7. Server: Generates and provides detailed information about the camera capabilities of the specified smartphone to the user.

[0067] 8. User: Makes the final decision: "I will buy this smartphone."

[0068] 9. Server: Generates the checkout form and confirms the order information.

[0069] This invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user accesses the e-commerce site using a device and enters the requested information into the chatbot interface, such as "I want a new smartphone."

[0073] Step 2:

[0074] The device sends the user's request information to the server, which then uses generative AI to activate a chatbot and begin a dialogue with the user.

[0075] Step 3:

[0076] The server uses generative AI to analyze the user's request information and generate additional questions such as "What do you want to use it for?", "What features do you want?", and "What is your budget?"

[0077] Step 4:

[0078] The device displays additional questions to the user, who answers, "I need a high-performance camera for gaming, and my budget is under 50,000 yen."

[0079] Step 5:

[0080] The device sends the user's answers to the server, which uses generative AI to analyze the user's detailed needs. It identifies the user's hidden needs and extracts the requirements of "gaming use," "high-performance camera," and "budget under 50,000 yen."

[0081] Step 6:

[0082] The server searches the e-commerce site's database for products that match the requirements, and uses generative AI to generate a list of the most suitable smartphones.

[0083] Step 7:

[0084] The server sends the generated product list to the terminal along with a message suggesting the product to the user, and the terminal displays the list to the user.

[0085] Step 8:

[0086] The user selects one of the suggested products and requests more information about it, for example, by entering "What are the camera specs of this smartphone?"

[0087] Step 9:

[0088] The device sends the user's request to the server, which retrieves detailed information about the selected product (such as the camera's specs, performance, and price) from a database and compiles it into a form that is easy for the generative AI to understand.

[0089] Step 10:

[0090] The server sends the generated details to the terminal, which displays them to the user.

[0091] Step 11:

[0092] The user checks the product details and enters the final decision, "I will purchase this smartphone."

[0093] Step 12:

[0094] The terminal sends the purchase decision information to the server, which generates an order form and asks the user to enter credit card information and shipping address information.

[0095] Step 13:

[0096] The terminal displays an order procedure form to the user, and the user enters the necessary information, after which the terminal sends the information to the server.

[0097] Step 14:

[0098] The server checks the order information and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (usage instructions, warranty information) and sends them to the user.

[0099] Step 15:

[0100] The user receives the purchased item, sets up and uses the smartphone based on the after-sales information, and sends their satisfaction and feedback to the server via the channel.

[0101] Step 16:

[0102] The server stores user feedback in a database and uses it for future marketing and product planning. It also generates targeted advertisements based on user data and delivers them appropriately.

[0103] Example 1

[0104] 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."

[0105] Conventional e-commerce systems have the problem that it takes a great deal of time and effort for users to clearly understand their needs and select the most suitable products and services. It is also difficult to effectively extract hidden needs that users have and appropriately suggest products and services that meet those needs. This can lead to a decrease in user satisfaction on e-commerce sites, and there has been a demand for an improved purchasing experience.

[0106] 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.

[0107] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs using a product model, means for searching a database for optimal products and services based on the hidden needs, means for presenting the search results to the user, and means for collecting additional request information from the user via an interactive interface. This makes it possible to effectively extract hidden needs based on the user's request information and quickly propose optimal products and services that meet those needs.

[0108] "User" means an individual or corporation that accesses the EC site and provides information about products and services.

[0109] "Product and service requirement information" refers to information such as the features, functions, and budget of a specific product or service that the user wants.

[0110] "Product modeling" is a technology that uses AI to analyze user input information and generate new insights and data.

[0111] "Hidden needs" refer to requests and desires that users have not explicitly recognized but that they implicitly desire.

[0112] A "database" is an organized collection of information, an information system designed to be easy to search and retrieve.

[0113] "Search results" are a list of suitable products and services retrieved from a database based on the user's request information and hidden needs.

[0114] An "interactive interface" refers to interactive software for communicating with users and gathering information through questions and answers.

[0115] "Additional requested information" refers to the user providing more detailed information or specific questions in addition to the initial requested information.

[0116] The present invention relates to an e-commerce concierge system that collects user request information, analyzes the requests to discover the user's hidden needs, and proposes optimal products and services. This system is specifically implemented using the following steps.

[0117] First, a user accesses an e-commerce site using a device such as a smartphone or PC (hereafter referred to as a terminal) and inputs request information, such as "I want a new smartphone." This request information reflects detailed needs based on the user's usage and preferences.

[0118] The device then immediately sends this input information to the server. This communication uses a common security protocol (e.g., HTTPS), ensuring secure data transfer. The server then analyzes the received request information using a product model (e.g., OpenAI's GPT-4). This analysis extracts not only the user's explicit needs, but also their latent and hidden needs.

[0119] For example, if a user's request information includes specific needs such as "gaming use, high-performance camera, budget under 50,000 yen," the server will search the database to suggest the most suitable products and services based on this.The databases used here are those of large-scale e-commerce sites such as Amazon and Rakuten Ichiba, and are accessed via APIs.

[0120] The server organizes the search results and creates a list of products that are most suitable for the user. This list is then presented to the user via their device. The user reviews the presented list of products, and if they want more detailed information, they can enter, for example, "Please tell me more about the camera performance of this smartphone." This input information is also sent from the device to the server, and the product model operates again to generate a detailed answer.

[0121] The system also provides detailed information and purchase assistance. When a user indicates their intention to purchase, the server provides a purchase procedure form via their terminal, and the user enters the necessary information. The server confirms the order information and sends the user an order confirmation email and after-sales information (such as instructions for use and warranty information). At this time, the server can also take into account the user's past purchase history and browsing history to make personalized product and service suggestions.

[0122] Examples of prompt statements

[0123] Below are some examples of prompt sentences.

[0124] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0125] 2. Server: "What's your budget?"

[0126] 3. User: "It's under $500."

[0127] 4. Server: "Then, we recommend the following smartphones." (Shows search results)

[0128] 5. User: "Can you tell me more about the camera performance of this phone?"

[0129] 6. Server: "The camera performance of this smartphone is..." (provides more information)

[0130] In this way, the present invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1:

[0133] A user accesses an EC site using a terminal and inputs request information such as "I want a new smartphone." The request information "I want a new smartphone" is obtained as input. This input data is sent from the terminal to the server.

[0134] Step 2:

[0135] The terminal immediately sends the user's input information to the server. Specifically, this input information is transferred to the server using a security protocol (e.g., HTTPS). As data processing, the user's request information is encoded and converted into a secure format. As output, the encoded request information is sent to the server.

[0136] Step 3:

[0137] The server calls a generative AI model (e.g., OpenAI's GPT-4) to analyze the received request information. It receives the encoded request information as input. The output obtained using the generative AI model generates questions to further collect the user's detailed request information (e.g., "What is the purpose?", "What features and price range do you want?").

[0138] Step 4:

[0139] The server sends an interface containing the generated question to the device. As input, it has the output of the generative AI model. As output, it sends the interface to the device.

[0140] Step 5:

[0141] The user answers additional questions through the chatbot interface. For example, they input detailed requirements such as "for gaming, high-performance camera, budget under 50,000 yen." The user's answer is received as input. This answer is then sent from the device to the server.

[0142] Step 6:

[0143] The terminal sends the user's answer back to the server. As input, there is the user's additional request information. As output, this is sent to the server.

[0144] Step 7:

[0145] The server uses a generative AI model to analyze the collected detailed user request information and extract the user's hidden needs. The detailed request information is given as input. The generative AI model processes the data by analyzing the request information and clarifying the hidden needs. The hidden needs are obtained as output.

[0146] Step 8:

[0147] The server searches the database based on the extracted hidden needs and identifies the most suitable products and services. The hidden needs are given as input. Specifically, it executes a search query against the e-commerce site's database using an API. The output is the search results.

[0148] Step 9:

[0149] The server generates an interface for presenting the search results to the user and sends it to the terminal. The search results are used as input, and the interface is sent to the terminal as output.

[0150] Step 10:

[0151] The user selects an item that interests them from the displayed items, and if they want more information, they input a specific question. For example, they might input, "Please tell me more about the camera performance of this smartphone." The user's specific question is obtained as input.

[0152] Step 11:

[0153] The terminal sends the user's specific question to the server. As input, there is the user's specific question. As output, this is sent to the server.

[0154] Step 12:

[0155] The server generates detailed information about the selected product using a generative AI model. The input is a specific question from the user. Data processing involves retrieving detailed product information from a database and organizing and generating it using the generative AI model. The output is detailed information.

[0156] Step 13:

[0157] The server sends the generated detailed information to the terminal. As input, there is detailed information. As output, the interface is sent to the terminal.

[0158] Step 14:

[0159] The user checks the detailed information and, if he / she wishes to purchase, inputs "I will purchase this smartphone." The user's intention to purchase is obtained as input.

[0160] Step 15:

[0161] The terminal transmits the user's purchase intention to the server. As input, there is the purchase intention. As output, this is transmitted to the server.

[0162] Step 16:

[0163] The server generates a purchase procedure form and provides it to the user. The input is the intention to purchase. The output is a purchase procedure form that is generated and sent to the terminal.

[0164] Step 17:

[0165] The user enters the required information into the form (shipping address, payment information, etc.) and presses the submit button. The required information is obtained as input.

[0166] Step 18:

[0167] The terminal sends the transmitted order information to the server. As input, there is the order information. As output, this is sent to the server.

[0168] Step 19:

[0169] The server verifies and confirms the order, sends an order confirmation email and follow-up information (such as instructions for use and warranty information) to the user, and collects user ratings and feedback. The input is order information. The output is a confirmation email and follow-up information sent to the user. A feedback form is also provided.

[0170] (Application example 1)

[0171] 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."

[0172] In conventional e-commerce systems, users often have difficulty identifying their specific needs or selecting the most suitable product from the vast number of options available. Furthermore, general search functions are unable to fully uncover users' hidden needs, making it difficult to recommend the products and services they truly desire. Furthermore, response times are long when users request detailed information, making it difficult to support a smooth purchasing process. To address these issues, a system is needed that provides more personalized recommendations tailored to user needs and a smooth dialogue interface.

[0173] 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.

[0174] In this invention, the server includes a means for receiving product and service request information from a user, a means for analyzing the request information and extracting the user's hidden needs, a means for searching for optimal products and services from a product database based on the hidden needs, a means for presenting the search results to the user, and a means for providing an interactive interface with the user using a generative AI model. This makes it possible to effectively uncover the user's hidden needs and quickly propose optimal products and services. Furthermore, it is possible to improve the user's experience and smoothly support the purchasing process.

[0175] "User" means any individual or entity that provides requested information regarding goods or services.

[0176] "Requirement information" refers to the specific specifications and desired conditions of the product or service that the user is looking for.

[0177] "Hidden needs" are latent requirements and expectations that users have not explicitly communicated but that become apparent through analysis.

[0178] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate dialogue with users. Specifically, it has the ability to generate natural language based on input data and respond to users.

[0179] A "conversational interface" is an interface that allows a user to interact with a system, allowing the user to enter questions and receive immediate answers.

[0180] A "product database" is a database that stores information about various products and services. Each product includes detailed specifications, prices, user reviews, and other information.

[0181] "Search means" refers to the functions and algorithms used to find the most suitable products and services based on the user's requirements from a product database.

[0182] A "presentation mechanism" is a method or system for presenting search results to a user visually or audibly.

[0183] The present invention relates to an e-commerce concierge system that collects request information from users, analyzes those requests to discover the users' hidden needs, and proposes optimal products and services. This system provides a chatbot interface using a generative AI model via devices such as smartphones and tablets, and grasps the users' needs through dialogue with them and proposes products. Details of embodiments of the present invention are described below.

[0184] First, a user accesses the system through a device such as a smartphone or tablet. A chatbot application using a generative AI model is installed on the device. This application provides an interface for receiving requested information from the user.

[0185] The server receives and analyzes the request information sent by the user. Specifically, it analyzes the request information using a generative AI model (e.g., GPT-4) to extract hidden needs that the user has not explicitly communicated. This analysis uses natural language processing technology to identify potential needs based on the user's input.

[0186] Next, the server searches the product database for the most suitable products and services based on the extracted needs. For example, if a user inputs "I want a new smartphone," and further answers "one that can play games, has a high-performance camera, and a budget of less than 50,000 yen," the server searches the database for products that meet these conditions. A database search algorithm is used for the search.

[0187] The search results are displayed on the screen of a smartphone or tablet. The user can review the displayed search results and select the product that interests them. If the user wants more detailed information, they can ask the chatbot an additional question. For example, they can enter a question such as, "Please tell me more about the camera performance of this smartphone." The server receives this request, generates detailed information about the selected product, and provides it to the user.

[0188] Once the user decides to purchase, the chatbot interface will help them through the purchase process smoothly. A purchase form will be generated and the order will be confirmed by entering the necessary information. After the purchase, after-sales information (e.g., how to use, warranty details, etc.) will also be provided.

[0189] Below are some examples and prompts:

[0190] Specific examples

[0191] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0192] Terminal: Sends this information to the server.

[0193] Server: Uses generative AI to ask the user, "What is your budget?"

[0194] User: "It's under 50,000 yen."

[0195] Server: Searches for smartphones that meet the criteria and presents candidates to the user.

[0196] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[0197] Server: Generates detailed information about the camera capabilities of a specified smartphone and provides it to the user.

[0198] User: Makes the final decision: "I will buy this smartphone."

[0199] Server: Generates the checkout form and confirms the order information.

[0200] Prompt Sentence Examples

[0201] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0202] Prompt: "To find out what kind of smartphone you're looking for, ask yourself these questions: 1. What will you use it for? 2. What features are important to you? 3. What is your budget?"

[0203] This invention makes it possible to effectively uncover hidden user needs and quickly propose optimal products and services. It also provides a smooth interactive interface, significantly improving the user's purchasing experience.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] A user launches a chatbot application on their smartphone or tablet and inputs information about a product or service they require. For example, they might input, "I want a new smartphone." This input information is sent from the device to the server. To process the input information, the device collects the user's text data and transfers it to the server.

[0207] Step 2:

[0208] The server analyzes the received request information using a generative AI model (e.g., GPT-4). Specifically, it performs natural language processing to analyze the user's request information and extract hidden needs. For example, from a request such as "I want a new smartphone," specific needs such as "for gaming, with a high-performance camera, and a budget of less than 50,000 yen" can be extracted. The server generates the analysis results and prepares for the next step.

[0209] Step 3:

[0210] The server searches the product database for the most suitable products and services based on the extracted hidden needs. This search uses a database search algorithm. For example, it finds smartphones from the database that meet the criteria of "for gaming," "high-performance camera," and "budget under 50,000 yen." The server generates data on the search results and prepares for the next step.

[0211] Step 4:

[0212] The server sends data to the terminal to present the search results to the user. Specifically, it generates an optimal product list to meet the user's request information and provides it to the terminal. The terminal receives this data and displays it to the user through the chatbot interface. The user can then check the presented product list.

[0213] Step 5:

[0214] The user selects a product of interest from the presented product list and requests further details. For example, they might type, "Tell me more about the camera performance of this smartphone." The device then sends this request to the server. The system accurately collects the user's input and processes it for transmission to the server.

[0215] Step 6:

[0216] In response to a user's request, the server provides detailed information about the selected product. Specifically, it uses a generative AI model to extract detailed information from a product database and generate text to answer the user's question. For example, it may provide detailed information about the specifications and features of a smartphone's camera performance. The server then sends the generated detailed information to the device.

[0217] Step 7:

[0218] The terminal receives the detailed information sent from the server and displays it to the user, who can then review the presented details and make a final purchasing decision. The detailed information is visually displayed via a chatbot interface.

[0219] Step 8:

[0220] If the user decides to make a purchase, the terminal generates a purchase procedure form and presents it to the user. The user enters the necessary purchase information and confirms the order. The purchase information is then accurately collected and sent to the server.

[0221] Step 9:

[0222] The server receives the purchase information sent by the user and confirms the order. Specifically, it checks the order details, processes the payment, and generates an order confirmation email to send to the user. It also provides after-sales follow-up information after the purchase and collects data to improve user satisfaction. It processes the user's purchase information and generates after-sales follow-up data.

[0223] Through the above steps, the EC concierge system of the present invention effectively uncovers the user's hidden needs and quickly suggests optimal products and services, thereby realizing a smooth purchasing experience.

[0224] 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.

[0225] This invention relates to an e-commerce concierge system that collects user request information, discovers the user's hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes the user's emotions, more personalized proposals can be made.

[0226] System Overview

[0227] The system consists of the following main components:

[0228] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[0229] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0230] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[0231] 4. Emotion engine: An engine for recognizing user emotions and tailoring suggestions based on those emotions.

[0232] Program processing flow

[0233] Step 1: Gather user needs

[0234] A user accesses an e-commerce site via a device and inputs their request, such as "I want a new smartphone," into the chatbot interface. The device then sends this information to the server. The server then activates the chatbot interface, which uses generative AI, and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[0235] Step 2: Emotion recognition and needs analysis

[0236] The server analyzes the request information collected from the user using a generative AI and an emotion engine. The generative AI analyzes the request information and extracts the user's hidden needs. Meanwhile, the emotion engine recognizes emotions (e.g., excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation. Based on this information, the server can more accurately understand the user's needs.

[0237] Step 3: Find and adjust product offers

[0238] The server searches the e-commerce site's database for products that match the user's needs and the emotions recognized. It also adjusts its suggestions by taking into account the emotional information provided by the emotion engine. For example, if the user is unsure, it may emphasize reviews and warranty information. The adjusted search results are then presented to the user.

[0239] Step 4: Provide details and purchase assistance

[0240] When a user selects an item that interests them from the displayed items and requests more information about it, the device sends this request to the server. The server generates detailed information about the selected item and provides it to the user, taking into account the emotion engine data. For example, if the user is excited, the server adds an additional comment such as "This is a popular model."

[0241] Step 5: Purchase decision and follow-up

[0242] The user checks the product details and enters the final decision, saying, "I'll buy this smartphone." The device then sends the purchase decision to the server. The server then generates an order form, collects the necessary information, and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (such as instructions for use and warranty information) and sends them to the user.

[0243] Specific examples

[0244] Below are some specific examples of how this system can be used.

[0245] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0246] 2. Device: Sends this information to the server.

[0247] 3. Server: Using generative AI, ask the user, "What's your budget?" The emotion engine analyzes the user's input and recognizes that the user is excited.

[0248] 4. User: "It's under 50,000 yen."

[0249] 5. Server: Based on the user's needs, the server searches the e-commerce site's database for smartphones that fit the criteria, and then adjusts the search results by taking into account the analysis results of the emotion engine. For example, the server may add a comment to the suggestion, such as "This smartphone is particularly good at gaming."

[0250] 6. User: Asks a specific question, such as "Tell me more about the camera performance of this phone." The emotion engine recognizes the user's interest.

[0251] 7. Server: Generates detailed information about the camera performance of the specified smartphone, and based on data from the emotion engine, provides information to further pique the user's interest, such as "This smartphone has camera performance that is well-received on social media."

[0252] 8. User: Makes the final decision: "I will buy this smartphone."

[0253] 9. Server: Generates an order form and confirms the order information. Based on the data from the emotion engine, the server sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this smartphone."

[0254] This invention takes into account not only the user's hidden needs but also their current emotions, enabling more personalized product suggestions and significantly improving the purchasing experience on e-commerce sites.

[0255] The processing flow will be explained below.

[0256] Step 1:

[0257] The user accesses the e-commerce site using a device and enters "I want a new smartphone" into the chatbot interface.

[0258] Step 2:

[0259] The device sends the user's request information to the server, which then launches a chatbot interface using generative AI to generate additional questions for the user, such as "purpose, desired features, price range, etc."

[0260] Step 3:

[0261] The user confirms the additional questions displayed on the device and answers, "I need a high-performance camera for gaming, and my budget is less than 50,000 yen."

[0262] Step 4:

[0263] The device sends the user's response to the server. The emotion engine analyzes the user's response and recognizes, for example, that the user is "excited."

[0264] Step 5:

[0265] The server uses generative AI to analyze the user's detailed needs, extracting specific needs such as "gaming use," "high-performance camera," and "budget under 50,000 yen" as a result of the analysis.

[0266] Step 6:

[0267] The server takes into account the analysis results of the emotion engine and adjusts the search results based on the user's excitement level, for example generating a comment such as "A smartphone that offers an exciting gaming experience."

[0268] Step 7:

[0269] The server searches the e-commerce site's database for products that match the criteria, and uses generative AI to generate a list of optimal products, sending it to the device along with a message suggesting the product to the user. The device then displays this list to the user.

[0270] Step 8:

[0271] The user selects one of the presented smartphones and enters a follow-up question: "What are the camera specs of this smartphone?" The device then sends this request to the server.

[0272] Step 9:

[0273] The server retrieves detailed information about the selected smartphone from a database and uses an emotion engine to process the information to further enhance the user's excitement. For example, it might add a comment such as, "This smartphone's camera function is a hot topic on social media."

[0274] Step 10:

[0275] The server sends the generated details to the terminal, which displays them to the user.

[0276] Step 11:

[0277] The user confirms the details and enters the final decision, saying, "I will purchase this smartphone." The device then sends this purchase decision information to the server.

[0278] Step 12:

[0279] The server generates a purchase procedure form and sends it to the terminal to collect the necessary information (credit card information, shipping address information, etc.). The terminal displays this form to the user, who then enters the necessary information.

[0280] Step 13:

[0281] The terminal sends the entered purchase information to the server, which then verifies the order and confirms the order.

[0282] Step 14:

[0283] The server generates an order confirmation email and uses the emotion engine to send it to the user with a positive message, such as "Thank you for your purchase! Many users are happy with this smartphone."

[0284] Step 15:

[0285] The user receives the smartphone they purchased, sets it up using the after-sales service information, and enters their satisfaction and reviews.

[0286] Step 16:

[0287] The device sends user review information to a server, which stores this information in a database and uses it for future marketing and product improvements. The server also generates targeted advertisements based on the collected data and delivers them to users at the appropriate time.

[0288] Example 2

[0289] 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."

[0290] Conventional product recommendation systems for e-commerce sites have difficulty making appropriate recommendations based on user requirements. In particular, it is difficult to make product recommendations that take user emotions into consideration, resulting in problems that do not improve the user experience. Furthermore, since it is not possible to accurately grasp users' hidden needs, there is a risk of user satisfaction decreasing.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0292] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs, means for recognizing the user's emotions and adjusting the request information based on the emotions, means for searching for optimal products and services from a product database based on the hidden needs and emotion information, and means for presenting the search results to the user, thereby enabling personalized product suggestions that take the user's hidden needs and emotions into consideration.

[0293] "User" means an individual or corporation that uses the EC site to provide information about products and services.

[0294] "Request information" is detailed information about the product or service desired by the user, including, for example, the purpose, desired functions, price range, and the like.

[0295] "Analysis" is the process in which the server uses the generated AI model to analyze the user's request information and extract the user's hidden needs.

[0296] "Hidden needs" are potential requirements or desires that are important to users but are not explicitly requested by the user, and are extracted by the generative AI model.

[0297] "Emotion recognition" means that the emotion engine analyzes and recognizes emotions (excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation.

[0298] "Adjusting request information based on emotions" means adjusting the product proposal method and expression according to the recognized user emotions.

[0299] A "product database" is a database that stores information about various products and services offered by an e-commerce site.

[0300] "Searching for products and services" means finding the most suitable products and services from a product database based on the user's hidden needs and emotional information.

[0301] "Detailed information" refers to specific product information that interests users, such as product specifications, features, reviews, and warranty information.

[0302] The "additional questions" are questions that are asked to dig deeper into the requested information that the user provided initially.

[0303] "Adjustment" refers to optimizing the content of product suggestions and detailed information presented based on the user's emotional data.

[0304] "Past purchase history and browsing history" refers to data on purchases and browsing that a user has made on an e-commerce site.

[0305] "Recommending personalized products and services" means individually proposing optimal products and services by taking into account a user's past purchasing history, browsing history, hidden needs, and emotional information.

[0306] MODE FOR CARRYING OUT THE INVENTION

[0307] This invention relates to an e-commerce concierge system that collects user request information, discovers hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes user emotions, more personalized proposals can be made.

[0308] The system consists of the following main components:

[0309] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[0310] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0311] 3. Server: A computer system that receives information from users, analyzes it using a generative AI model and emotion engine, and makes product suggestions.

[0312] Specifically, a user accesses an e-commerce site through a device and inputs request information, such as "I want a new smartphone," into the chatbot interface. This information is sent to the server via the device. The server uses a generative AI model to generate follow-up questions for the user, and presents prompts to the user, such as "What is the purpose, desired features, and price range?" Examples of such prompts are as follows:

[0313] What is your budget?

[0314] If the user responds with, "I want a smartphone that can play games and has a high-performance camera," the device sends this information back to the server. The server then uses a generative AI model and emotion engine to analyze the collected request information and extract the user's hidden needs. For example, it might discover that the user is looking for a smartphone specialized for gaming.

[0315] Furthermore, the emotion engine recognizes emotions from the user's responses. For example, if the server senses that the user is excited, it adjusts its suggestions based on this emotional information. Specifically, it searches the e-commerce site's database for products that meet the user's needs and adjusts the suggestions based on the emotion engine's information. For example, it adds a comment such as, "This smartphone is particularly good at games."

[0316] When a user selects a product that interests them from the suggested items and requests more information, the device sends this request to the server. The server uses a generative AI model to generate detailed product information and provides it to the user, taking into account data from the emotion engine. For example, if the user is excited, the server adds an additional comment such as, "This smartphone has a camera that is highly acclaimed on social media."

[0317] Once the user finally decides to purchase, the device sends this information to the server. The server generates an order form, collects the necessary information, and then confirms the order. The server also uses a generative AI model to generate an order confirmation email and follow-up information, which are then sent to the user. For example, the email may contain a positive message such as, "Thank you for your purchase! Many users are delighted with this smartphone."

[0318] This system enables personalized suggestions that take into account users' hidden needs and emotions, dramatically improving the shopping experience on e-commerce sites.

[0319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0320] Step 1:

[0321] User: Accesses the e-commerce site using a device and inputs "I want a new smartphone" into the chatbot interface. This becomes the input information and is treated as the user's initial request information.

[0322] Terminal: Sends user input information to the server. The input here is the user's request information, and the output is the request data sent to the server.

[0323] Step 2:

[0324] Server: Launches the generative AI model and generates follow-up questions based on the user's initial request information. Specifically, the generative AI model inputs prompts such as "What are the intended use, desired features, and price range?" to create optimal follow-up questions for the user. The input is the user's request information, and the output is the follow-up questions.

[0325] Server: Sends the generated follow-up questions to the terminal. The input here is the follow-up questions, and the output is sent to the terminal.

[0326] Terminal: Display a follow-up question to the user.

[0327] Step 3:

[0328] User: In response to the follow-up question, answers, "I want something that can play games and has a good camera." This becomes new input information.

[0329] Terminal: Sends the user's answer to the server. The input is the user's answer and the output is what is sent to the server.

[0330] Step 4:

[0331] Server: Analyzes the user's responses using a generative AI model and emotion engine. The generative AI model extracts hidden needs, such as the specific features the user desires (game performance, camera performance). The emotion engine identifies the user's emotional state (excitement, etc.) from the user's responses. The input is the user's detailed request information, and the output is hidden needs and emotion information.

[0332] Step 5:

[0333] Server: Searches for the optimal smartphone from a product database based on the user's hidden needs and emotional information. Specifically, it extracts products from the database that meet hidden needs (gaming performance, camera performance), and adjusts the suggestions based on the emotional information. For example, for a user who is feeling excited, it emphasizes "the latest gaming-specific model." The input is the hidden needs and emotional information, and the output is a list of adjusted products.

[0334] Server: Sends the adjusted search results to the device.

[0335] Step 6:

[0336] Device: Presents search results to the user. The input here is a tailored list of products, and the output is a display to the user, such as "This phone is great for gaming and has a great camera."

[0337] Step 7:

[0338] User: Selects a suggested product that interests them and requests more information about it. For example, they might type, "Tell me more about the camera performance of this phone."

[0339] Terminal: Sends this request to the server. The input is the user's request for more information, and the output is the transmission to the server.

[0340] Step 8:

[0341] Server: Generates detailed information about the specified product based on the user's request. Using a generative AI model, it creates detailed information such as product specifications, features, and reviews, and adjusts the information taking into account data from the emotion engine. For example, if the user is excited, it adds an additional comment such as "This smartphone has a camera performance that is highly rated on social media." The input is the user's detailed request information, and the output is detailed information.

[0342] Server: Sends detailed information to the device.

[0343] Step 9:

[0344] Terminal: Presents detailed information to the user. The input is the adjusted detailed information, and the output is what is displayed to the user.

[0345] Step 10:

[0346] User: Checks the product details and enters the final decision, "I'll buy this smartphone."

[0347] Terminal: Sends purchase decision information to the server. The input is the user's purchase decision information, and the output is the transmission to the server.

[0348] Step 11:

[0349] Server: Generates the checkout form and collects the necessary information. The input is the user's purchasing decision information, and the output is the checkout form.

[0350] Server: Confirms the order. Additionally, the generative AI model is used to create an order confirmation email and after-sales information, and an emotional engine is used to add a positive message. For example, "Thank you for your purchase! Many users are pleased with this smartphone." The input is the purchase decision information and the content generated by the generative AI model, and the output is a confirmation email.

[0351] Step 12:

[0352] Server: Sends an order confirmation email to the terminal and provides it to the user. The input is the confirmation email and the output is what is sent to the user.

[0353] (Application example 2)

[0354] 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."

[0355] Conventional e-commerce sites typically use systems that suggest products and services based on user requirements, but it has been difficult to provide personalized suggestions that take into account the user's hidden needs and emotions. As a result, users have difficulty finding the products and services they truly want, resulting in a poor quality shopping experience. The present invention aims to solve these problems by comprehensively analyzing users' emotions and requirements, discovering their hidden needs, and suggesting optimal products and services.

[0356] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving request information regarding products and services from a user, means for analyzing the request information and analyzing the user's hidden needs and emotions, means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions, and means for presenting the search results to the user and providing supplemental information according to the emotions. This makes it possible to make personalized product suggestions that take into account the user's hidden needs and emotions.

[0357] The "means for receiving requested information about products and services from users" refers to an interface and communication means for collecting the user's input desires and needs for products and services.

[0358] The "means for analyzing the request information and extracting the user's hidden needs" refers to an algorithm that uses a generative AI model to derive the user's latent requests and needs based on information explicitly provided by the user.

[0359] The "means for analyzing the hidden needs and the user's emotions" is an engine that has the function of complementing the user's hidden needs with an emotion engine and analyzing the user's emotional state (e.g., excitement, anxiety, joy, etc.).

[0360] The "means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions" refers to an algorithm and search engine that selects the most suitable products and services from a database based on the user's needs and emotional information.

[0361] "Means for presenting search results to the user and providing supplementary information according to their emotions" refers to an interface that displays information about the searched products or services to the user and has the function of adding additional explanations or related information according to the user's emotional state.

[0362] The "means for adjusting the content of the proposal based on the emotion" is an algorithm that dynamically changes the content of the proposed products and services in response to the user's emotion recognized by the emotion engine.

[0363] "Means for recommending personalized products and services by taking into account a user's past purchasing history and browsing history" refers to an algorithm that retrieves a user's past purchasing history and browsing history from a database and recommends personalized products and services accordingly.

[0364] The "means for optimizing product proposals using the emotion data" is an algorithm for optimizing the selection and presentation method of products to be proposed using emotion data obtained from the emotion engine.

[0365] This invention provides an e-commerce system that proposes optimal products and services based on user requirements and emotional data. The system consists of the following main components:

[0366] A means of receiving product and service request information from users

[0367] A means for analyzing the request information and analyzing the user's hidden needs and feelings.

[0368] A means for searching for the most suitable products and services from a product database based on said hidden needs and recognized emotions.

[0369] A means of presenting search results to users and providing supplementary information according to their emotions

[0370] means for adjusting the content of the proposal based on said emotion;

[0371] A means of recommending personalized products and services that take into account a user's past purchasing and browsing history

[0372] means for optimizing product recommendations using said emotion data;

[0373] Natural language description of the program:

[0374] 1. User Interface:

[0375] The server provides an interface for users to input their request information about products and services. Users access the chatbot interface via their smartphone or PC and input their request information, such as "I want a new smartphone." This operation causes the device to send the request information to the server.

[0376] 2. Requirements information collection and analysis:

[0377] Once the server receives the request information, it analyzes it using a generative AI model. Through this analysis, it extracts the user's hidden needs. The emotion engine also analyzes the user's input content and tone for emotional data (e.g., excitement, anxiety, joy, etc.). As a result of this analysis, the user's specific needs and emotional state are revealed.

[0378] 3. Product search and sentiment-based tailoring:

[0379] The server searches for the most suitable products and services from a product database based on the hidden needs and emotion data. The emotion engine adjusts the content of the suggestions based on the user's emotions. For example, if the user is feeling anxious, the server may highlight the reliability of the product and review information.

[0380] 4. Presenting search results and providing supplementary information:

[0381] The server presents the user with search results for the most suitable products. If the user is interested, it provides further details. At this time, it supports the user's purchasing decision by displaying supplemental information according to the user's emotions (e.g., "This smartphone is very popular").

[0382] 5. Personalization based on past history:

[0383] The server takes into account the user's past purchasing and browsing history and uses it to recommend personalized products and services. Using emotion data, the recommendations can be further personalized, increasing user satisfaction.

[0384] Examples:

[0385] Below are some concrete examples of how the system can be used:

[0386] User: "I want a new smartphone that can play games and has a good camera," types the chatbot.

[0387] Server: Uses a generative AI model to ask the user, "What's your budget?" The emotion engine recognizes the user's excitement.

[0388] User: "Under $50,000."

[0389] Server: Searches for products from the database based on the user's needs and adjusts the search results taking into account the analysis results of the sentiment engine. For example, it adds comments to the suggestions, such as "This phone is particularly good at gaming."

[0390] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[0391] Server: Generates detailed information about the camera performance of the specified smartphone and provides information to further pique the user's interest, such as "This smartphone has a camera performance that is well-received on social media."

[0392] User: Makes the final decision to "purchase this smartphone."

[0393] Server: Generates the order checkout form and sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this phone."

[0394] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0395] Step 1:

[0396] The user inputs their request information about a product or service through the chatbot interface. For example, the input information might be something like, "I want a new smartphone. I want one that can play games and has a high-performance camera." The device then sends this information to the server. In this process, the user's request information becomes input data, and this data is sent to the server.

[0397] Step 2:

[0398] The request information received by the server is analyzed using a generative AI model. Specifically, the chatbot generates a follow-up question, such as "What is your budget?", and sends it to the user. The user responds, for example, "Less than 50,000 yen." In this step, the user's specific request information is collected and stored in a database.

[0399] Step 3:

[0400] The server uses an emotion engine to analyze emotion data from the user's input. Here, emotions (excitement, doubt, anxiety, etc.) are recognized from the content of the user's input (e.g., "It's under 50,000 yen") and the tone of the input. The analysis result detects that the user is excited. In this step, the user's text is used as input data, and the user's emotional state is obtained as output data.

[0401] Step 4:

[0402] The server searches the product database for the most suitable products and services based on the request information and emotion data mentioned above. The search results are then reflected in the emotion engine's analysis results, and a comment such as "This smartphone is particularly good at games" is added. The user's request information, emotion data, and database are used as input, and the search results with comments corresponding to the emotion are obtained as output.

[0403] Step 5:

[0404] The server presents the search results to the user. If the user is interested, they may ask a specific question, such as, "Tell me more about the camera performance of this smartphone." The server receives this request and provides detailed information. For example, it may present the result with additional information, such as, "This smartphone has a camera performance that is highly rated on social media." In this step, the additional question from the user becomes input data, and the detailed information based on that question becomes output data.

[0405] Step 6:

[0406] The user finally decides to purchase and enters "I will buy this smartphone." The device sends the purchase decision information to the server. The server generates an order procedure form, collects the necessary information, and then confirms the order. It also sends an order confirmation email with a positive message based on the emotion engine data, such as "Thank you for your purchase! Many users are pleased with this smartphone." In this step, the user's purchase decision information is the input data, and the completion of the order procedure and the confirmation email are the output data.

[0407] Step 7:

[0408] The server takes into account the user's past purchasing and browsing history to optimize future personalized product suggestions. In this step, past purchasing history and emotion data are used as inputs, and personalized suggestion information is the output data.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Second embodiment]

[0413] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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."

[0425] This invention relates to an e-commerce concierge system that collects user request information, analyzes those requests to discover hidden needs, and proposes optimal products and services. This system supports users in selecting, purchasing, and following up on products and services when using an e-commerce site through a chatbot interface that uses a generative AI model.

[0426] System Overview

[0427] The system consists of the following main components:

[0428] 1. User: An individual or corporation that accesses an e-commerce site and provides information about products and services.

[0429] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0430] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[0431] Program processing flow

[0432] Step 1: Gather user needs

[0433] A user accesses an e-commerce site through a device and inputs their request, such as "I want a new smartphone." The device then sends this information to a server. The server then launches a chatbot interface using generative AI and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[0434] Step 2: Needs analysis and product proposal

[0435] The server uses a generative AI model to analyze the request information collected from the user and extract the user's hidden needs. For example, it clarifies specific needs such as "gaming use, high-performance camera, budget under 50,000 yen." Based on the results of this analysis, the server searches the e-commerce site's database for relevant products and identifies the most suitable products and services. It then presents these search results to the user.

[0436] Step 3: Provide details and purchase assistance

[0437] If the user selects an item that interests them from the displayed items and requests more detailed information, the terminal sends this request to the server. The server generates detailed information about the selected item (specs, features, price, etc.) and provides it to the user. If the user indicates their intention to purchase the item, a purchase procedure form is displayed through the terminal, and the user enters the necessary information.

[0438] Step 4: Confirm your purchase and follow-up

[0439] Once the user has completed the purchase process, the terminal sends the order information to the server. The server confirms the order and sends the user an order confirmation email and post-purchase follow-up information (such as instructions for use and warranty information). The server also collects user ratings and feedback, which it uses as data for future product suggestions and service improvements.

[0440] Specific examples

[0441] Below are some specific examples of how this system can be used.

[0442] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0443] 2. Device: Sends this information to the server.

[0444] 3. Server: Uses generative AI to ask the user, "What is your budget?"

[0445] 4. User: "It's under 50,000 yen."

[0446] 5. Server: Based on the recommendation, the server searches the EC site's database for smartphones that meet the criteria and presents candidates to the user.

[0447] 6. User: Ask a specific question like, "Can you tell me more about the camera performance of this phone?"

[0448] 7. Server: Generates and provides detailed information about the camera capabilities of the specified smartphone to the user.

[0449] 8. User: Makes the final decision: "I will buy this smartphone."

[0450] 9. Server: Generates the checkout form and confirms the order information.

[0451] This invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[0452] The processing flow will be explained below.

[0453] Step 1:

[0454] The user accesses the e-commerce site using a device and enters the requested information into the chatbot interface, such as "I want a new smartphone."

[0455] Step 2:

[0456] The device sends the user's request information to the server, which then uses generative AI to activate a chatbot and begin a dialogue with the user.

[0457] Step 3:

[0458] The server uses generative AI to analyze the user's request information and generate additional questions such as "What do you want to use it for?", "What features do you want?", and "What is your budget?"

[0459] Step 4:

[0460] The device displays additional questions to the user, who answers, "I need a high-performance camera for gaming, and my budget is under 50,000 yen."

[0461] Step 5:

[0462] The device sends the user's answers to the server, which uses generative AI to analyze the user's detailed needs. It identifies the user's hidden needs and extracts the requirements of "gaming use," "high-performance camera," and "budget under 50,000 yen."

[0463] Step 6:

[0464] The server searches the e-commerce site's database for products that match the requirements, and uses generative AI to generate a list of the most suitable smartphones.

[0465] Step 7:

[0466] The server sends the generated product list to the terminal along with a message suggesting the product to the user, and the terminal displays the list to the user.

[0467] Step 8:

[0468] The user selects one of the suggested products and requests more information about it, for example, by entering "What are the camera specs of this smartphone?"

[0469] Step 9:

[0470] The device sends the user's request to the server, which retrieves detailed information about the selected product (such as the camera's specs, performance, and price) from a database and compiles it into a form that is easy for the generative AI to understand.

[0471] Step 10:

[0472] The server sends the generated details to the terminal, which displays them to the user.

[0473] Step 11:

[0474] The user checks the product details and enters the final decision, "I will purchase this smartphone."

[0475] Step 12:

[0476] The terminal sends the purchase decision information to the server, which generates an order form and asks the user to enter credit card information and shipping address information.

[0477] Step 13:

[0478] The terminal displays an order procedure form to the user, and the user enters the necessary information, after which the terminal sends the information to the server.

[0479] Step 14:

[0480] The server checks the order information and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (usage instructions, warranty information) and sends them to the user.

[0481] Step 15:

[0482] The user receives the purchased item, sets up and uses the smartphone based on the after-sales information, and sends their satisfaction and feedback to the server via the channel.

[0483] Step 16:

[0484] The server stores user feedback in a database and uses it for future marketing and product planning. It also generates targeted advertisements based on user data and delivers them appropriately.

[0485] Example 1

[0486] 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."

[0487] Conventional e-commerce systems have the problem that it takes a great deal of time and effort for users to clearly understand their needs and select the most suitable products and services. It is also difficult to effectively extract hidden needs that users have and appropriately suggest products and services that meet those needs. This can lead to a decrease in user satisfaction on e-commerce sites, and there has been a demand for an improved purchasing experience.

[0488] 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.

[0489] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs using a product model, means for searching a database for optimal products and services based on the hidden needs, means for presenting the search results to the user, and means for collecting additional request information from the user via an interactive interface. This makes it possible to effectively extract hidden needs based on the user's request information and quickly propose optimal products and services that meet those needs.

[0490] "User" means an individual or corporation that accesses the EC site and provides information about products and services.

[0491] "Product and service requirement information" refers to information such as the features, functions, and budget of a specific product or service that the user wants.

[0492] "Product modeling" is a technology that uses AI to analyze user input information and generate new insights and data.

[0493] "Hidden needs" refer to requests and desires that users have not explicitly recognized but that they implicitly desire.

[0494] A "database" is an organized collection of information, an information system designed to be easy to search and retrieve.

[0495] "Search results" are a list of suitable products and services retrieved from a database based on the user's request information and hidden needs.

[0496] An "interactive interface" refers to interactive software for communicating with users and gathering information through questions and answers.

[0497] "Additional requested information" refers to the user providing more detailed information or specific questions in addition to the initial requested information.

[0498] The present invention relates to an e-commerce concierge system that collects user request information, analyzes the requests to discover the user's hidden needs, and proposes optimal products and services. This system is specifically implemented using the following steps.

[0499] First, a user accesses an e-commerce site using a device such as a smartphone or PC (hereafter referred to as a terminal) and inputs request information, such as "I want a new smartphone." This request information reflects detailed needs based on the user's usage and preferences.

[0500] The device then immediately sends this input information to the server. This communication uses a common security protocol (e.g., HTTPS), ensuring secure data transfer. The server then analyzes the received request information using a product model (e.g., OpenAI's GPT-4). This analysis extracts not only the user's explicit needs, but also their latent and hidden needs.

[0501] For example, if a user's request information includes specific needs such as "gaming use, high-performance camera, budget under 50,000 yen," the server will search the database to suggest the most suitable products and services based on this.The databases used here are those of large-scale e-commerce sites such as Amazon and Rakuten Ichiba, and are accessed via APIs.

[0502] The server organizes the search results and creates a list of products that are most suitable for the user. This list is then presented to the user via their device. The user reviews the presented list of products, and if they want more detailed information, they can enter, for example, "Please tell me more about the camera performance of this smartphone." This input information is also sent from the device to the server, and the product model operates again to generate a detailed answer.

[0503] The system also provides detailed information and purchase assistance. When a user indicates their intention to purchase, the server provides a purchase procedure form via their terminal, and the user enters the necessary information. The server confirms the order information and sends the user an order confirmation email and after-sales information (such as instructions for use and warranty information). At this time, the server can also take into account the user's past purchase history and browsing history to make personalized product and service suggestions.

[0504] Examples of prompt statements

[0505] Below are some examples of prompt sentences.

[0506] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0507] 2. Server: "What's your budget?"

[0508] 3. User: "It's under $500."

[0509] 4. Server: "Then, we recommend the following smartphones." (Shows search results)

[0510] 5. User: "Can you tell me more about the camera performance of this phone?"

[0511] 6. Server: "The camera performance of this smartphone is..." (provides more information)

[0512] In this way, the present invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[0513] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0514] Step 1:

[0515] A user accesses an EC site using a terminal and inputs request information such as "I want a new smartphone." The request information "I want a new smartphone" is obtained as input. This input data is sent from the terminal to the server.

[0516] Step 2:

[0517] The terminal immediately sends the user's input information to the server. Specifically, this input information is transferred to the server using a security protocol (e.g., HTTPS). As data processing, the user's request information is encoded and converted into a secure format. As output, the encoded request information is sent to the server.

[0518] Step 3:

[0519] The server calls a generative AI model (e.g., OpenAI's GPT-4) to analyze the received request information. It receives the encoded request information as input. The output obtained using the generative AI model generates questions to further collect the user's detailed request information (e.g., "What is the purpose?", "What features and price range do you want?").

[0520] Step 4:

[0521] The server sends an interface containing the generated question to the device. As input, it has the output of the generative AI model. As output, it sends the interface to the device.

[0522] Step 5:

[0523] The user answers additional questions through the chatbot interface. For example, they input detailed requirements such as "for gaming, high-performance camera, budget under 50,000 yen." The user's answer is received as input. This answer is then sent from the device to the server.

[0524] Step 6:

[0525] The terminal sends the user's answer back to the server. As input, there is the user's additional request information. As output, this is sent to the server.

[0526] Step 7:

[0527] The server uses a generative AI model to analyze the collected detailed user request information and extract the user's hidden needs. The detailed request information is given as input. The generative AI model processes the data by analyzing the request information and clarifying the hidden needs. The hidden needs are obtained as output.

[0528] Step 8:

[0529] The server searches the database based on the extracted hidden needs and identifies the most suitable products and services. The hidden needs are given as input. Specifically, it executes a search query against the e-commerce site's database using an API. The output is the search results.

[0530] Step 9:

[0531] The server generates an interface for presenting the search results to the user and sends it to the terminal. The search results are used as input, and the interface is sent to the terminal as output.

[0532] Step 10:

[0533] The user selects an item that interests them from the displayed items, and if they want more information, they input a specific question. For example, they might input, "Please tell me more about the camera performance of this smartphone." The user's specific question is obtained as input.

[0534] Step 11:

[0535] The terminal sends the user's specific question to the server. As input, there is the user's specific question. As output, this is sent to the server.

[0536] Step 12:

[0537] The server generates detailed information about the selected product using a generative AI model. The input is a specific question from the user. Data processing involves retrieving detailed product information from a database and organizing and generating it using the generative AI model. The output is detailed information.

[0538] Step 13:

[0539] The server sends the generated detailed information to the terminal. As input, there is detailed information. As output, the interface is sent to the terminal.

[0540] Step 14:

[0541] The user checks the detailed information and, if he / she wishes to purchase, inputs "I will purchase this smartphone." The user's intention to purchase is obtained as input.

[0542] Step 15:

[0543] The terminal transmits the user's purchase intention to the server. As input, there is the purchase intention. As output, this is transmitted to the server.

[0544] Step 16:

[0545] The server generates a purchase procedure form and provides it to the user. The input is the intention to purchase. The output is a purchase procedure form that is generated and sent to the terminal.

[0546] Step 17:

[0547] The user enters the required information into the form (shipping address, payment information, etc.) and presses the submit button. The required information is obtained as input.

[0548] Step 18:

[0549] The terminal sends the transmitted order information to the server. As input, there is the order information. As output, this is sent to the server.

[0550] Step 19:

[0551] The server verifies and confirms the order, sends an order confirmation email and follow-up information (such as instructions for use and warranty information) to the user, and collects user ratings and feedback. The input is order information. The output is a confirmation email and follow-up information sent to the user. A feedback form is also provided.

[0552] (Application example 1)

[0553] 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."

[0554] In conventional e-commerce systems, users often have difficulty identifying their specific needs or selecting the most suitable product from the vast number of options available. Furthermore, general search functions are unable to fully uncover users' hidden needs, making it difficult to recommend the products and services they truly desire. Furthermore, response times are long when users request detailed information, making it difficult to support a smooth purchasing process. To address these issues, a system is needed that provides more personalized recommendations tailored to user needs and a smooth dialogue interface.

[0555] 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.

[0556] In this invention, the server includes a means for receiving product and service request information from a user, a means for analyzing the request information and extracting the user's hidden needs, a means for searching for optimal products and services from a product database based on the hidden needs, a means for presenting the search results to the user, and a means for providing an interactive interface with the user using a generative AI model. This makes it possible to effectively uncover the user's hidden needs and quickly propose optimal products and services. Furthermore, it is possible to improve the user's experience and smoothly support the purchasing process.

[0557] "User" means any individual or entity that provides requested information regarding goods or services.

[0558] "Requirement information" refers to the specific specifications and desired conditions of the product or service that the user is looking for.

[0559] "Hidden needs" are latent requirements and expectations that users have not explicitly communicated but that become apparent through analysis.

[0560] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate dialogue with users. Specifically, it has the ability to generate natural language based on input data and respond to users.

[0561] A "conversational interface" is an interface that allows a user to interact with a system, allowing the user to enter questions and receive immediate answers.

[0562] A "product database" is a database that stores information about various products and services. Each product includes detailed specifications, prices, user reviews, and other information.

[0563] "Search means" refers to the functions and algorithms used to find the most suitable products and services based on the user's requirements from a product database.

[0564] A "presentation mechanism" is a method or system for presenting search results to a user visually or audibly.

[0565] The present invention relates to an e-commerce concierge system that collects request information from users, analyzes those requests to discover the users' hidden needs, and proposes optimal products and services. This system provides a chatbot interface using a generative AI model via devices such as smartphones and tablets, and grasps the users' needs through dialogue with them and proposes products. Details of embodiments of the present invention are described below.

[0566] First, a user accesses the system through a device such as a smartphone or tablet. A chatbot application using a generative AI model is installed on the device. This application provides an interface for receiving requested information from the user.

[0567] The server receives and analyzes the request information sent by the user. Specifically, it analyzes the request information using a generative AI model (e.g., GPT-4) to extract hidden needs that the user has not explicitly communicated. This analysis uses natural language processing technology to identify potential needs based on the user's input.

[0568] Next, the server searches the product database for the most suitable products and services based on the extracted needs. For example, if a user inputs "I want a new smartphone," and further answers "one that can play games, has a high-performance camera, and a budget of less than 50,000 yen," the server searches the database for products that meet these conditions. A database search algorithm is used for the search.

[0569] The search results are displayed on the screen of a smartphone or tablet. The user can review the displayed search results and select the product that interests them. If the user wants more detailed information, they can ask the chatbot an additional question. For example, they can enter a question such as, "Please tell me more about the camera performance of this smartphone." The server receives this request, generates detailed information about the selected product, and provides it to the user.

[0570] Once the user decides to purchase, the chatbot interface will help them through the purchase process smoothly. A purchase form will be generated and the order will be confirmed by entering the necessary information. After the purchase, after-sales information (e.g., how to use, warranty details, etc.) will also be provided.

[0571] Below are some examples and prompts:

[0572] Specific examples

[0573] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0574] Terminal: Sends this information to the server.

[0575] Server: Uses generative AI to ask the user, "What is your budget?"

[0576] User: "It's under 50,000 yen."

[0577] Server: Searches for smartphones that meet the criteria and presents candidates to the user.

[0578] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[0579] Server: Generates detailed information about the camera capabilities of a specified smartphone and provides it to the user.

[0580] User: Makes the final decision: "I will buy this smartphone."

[0581] Server: Generates the checkout form and confirms the order information.

[0582] Prompt Sentence Examples

[0583] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0584] Prompt: "To find out what kind of smartphone you're looking for, ask yourself these questions: 1. What will you use it for? 2. What features are important to you? 3. What is your budget?"

[0585] This invention makes it possible to effectively uncover hidden user needs and quickly propose optimal products and services. It also provides a smooth interactive interface, significantly improving the user's purchasing experience.

[0586] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0587] Step 1:

[0588] A user launches a chatbot application on their smartphone or tablet and inputs information about a product or service they require. For example, they might input, "I want a new smartphone." This input information is sent from the device to the server. To process the input information, the device collects the user's text data and transfers it to the server.

[0589] Step 2:

[0590] The server analyzes the received request information using a generative AI model (e.g., GPT-4). Specifically, it performs natural language processing to analyze the user's request information and extract hidden needs. For example, from a request such as "I want a new smartphone," specific needs such as "for gaming, with a high-performance camera, and a budget of less than 50,000 yen" can be extracted. The server generates the analysis results and prepares for the next step.

[0591] Step 3:

[0592] The server searches the product database for the most suitable products and services based on the extracted hidden needs. This search uses a database search algorithm. For example, it finds smartphones from the database that meet the criteria of "for gaming," "high-performance camera," and "budget under 50,000 yen." The server generates data on the search results and prepares for the next step.

[0593] Step 4:

[0594] The server sends data to the terminal to present the search results to the user. Specifically, it generates an optimal product list to meet the user's request information and provides it to the terminal. The terminal receives this data and displays it to the user through the chatbot interface. The user can then check the presented product list.

[0595] Step 5:

[0596] The user selects a product of interest from the presented product list and requests further details. For example, they might type, "Tell me more about the camera performance of this smartphone." The device then sends this request to the server. The system accurately collects the user's input and processes it for transmission to the server.

[0597] Step 6:

[0598] In response to a user's request, the server provides detailed information about the selected product. Specifically, it uses a generative AI model to extract detailed information from a product database and generate text to answer the user's question. For example, it may provide detailed information about the specifications and features of a smartphone's camera performance. The server then sends the generated detailed information to the device.

[0599] Step 7:

[0600] The terminal receives the detailed information sent from the server and displays it to the user, who can then review the presented details and make a final purchasing decision. The detailed information is visually displayed via a chatbot interface.

[0601] Step 8:

[0602] If the user decides to make a purchase, the terminal generates a purchase procedure form and presents it to the user. The user enters the necessary purchase information and confirms the order. The purchase information is then accurately collected and sent to the server.

[0603] Step 9:

[0604] The server receives the purchase information sent by the user and confirms the order. Specifically, it checks the order details, processes the payment, and generates an order confirmation email to send to the user. It also provides after-sales follow-up information after the purchase and collects data to improve user satisfaction. It processes the user's purchase information and generates after-sales follow-up data.

[0605] Through the above steps, the EC concierge system of the present invention effectively uncovers the user's hidden needs and quickly suggests optimal products and services, thereby realizing a smooth purchasing experience.

[0606] 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.

[0607] This invention relates to an e-commerce concierge system that collects user request information, discovers the user's hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes the user's emotions, more personalized proposals can be made.

[0608] System Overview

[0609] The system consists of the following main components:

[0610] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[0611] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0612] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[0613] 4. Emotion engine: An engine for recognizing user emotions and tailoring suggestions based on those emotions.

[0614] Program processing flow

[0615] Step 1: Gather user needs

[0616] A user accesses an e-commerce site via a device and inputs their request, such as "I want a new smartphone," into the chatbot interface. The device then sends this information to the server. The server then activates the chatbot interface, which uses generative AI, and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[0617] Step 2: Emotion recognition and needs analysis

[0618] The server analyzes the request information collected from the user using a generative AI and an emotion engine. The generative AI analyzes the request information and extracts the user's hidden needs. Meanwhile, the emotion engine recognizes emotions (e.g., excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation. Based on this information, the server can more accurately understand the user's needs.

[0619] Step 3: Find and adjust product offers

[0620] The server searches the e-commerce site's database for products that match the user's needs and the emotions recognized. It also adjusts its suggestions by taking into account the emotional information provided by the emotion engine. For example, if the user is unsure, it may emphasize reviews and warranty information. The adjusted search results are then presented to the user.

[0621] Step 4: Provide details and purchase assistance

[0622] When a user selects an item that interests them from the displayed items and requests more information about it, the device sends this request to the server. The server generates detailed information about the selected item and provides it to the user, taking into account the emotion engine data. For example, if the user is excited, the server adds an additional comment such as "This is a popular model."

[0623] Step 5: Purchase decision and follow-up

[0624] The user checks the product details and enters the final decision, saying, "I'll buy this smartphone." The device then sends the purchase decision to the server. The server then generates an order form, collects the necessary information, and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (such as instructions for use and warranty information) and sends them to the user.

[0625] Specific examples

[0626] Below are some specific examples of how this system can be used.

[0627] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0628] 2. Device: Sends this information to the server.

[0629] 3. Server: Using generative AI, ask the user, "What's your budget?" The emotion engine analyzes the user's input and recognizes that the user is excited.

[0630] 4. User: "It's under 50,000 yen."

[0631] 5. Server: Based on the user's needs, the server searches the e-commerce site's database for smartphones that fit the criteria, and then adjusts the search results by taking into account the analysis results of the emotion engine. For example, the server may add a comment to the suggestion, such as "This smartphone is particularly good at gaming."

[0632] 6. User: Asks a specific question, such as "Tell me more about the camera performance of this phone." The emotion engine recognizes the user's interest.

[0633] 7. Server: Generates detailed information about the camera performance of the specified smartphone, and based on data from the emotion engine, provides information to further pique the user's interest, such as "This smartphone has camera performance that is well-received on social media."

[0634] 8. User: Makes the final decision: "I will buy this smartphone."

[0635] 9. Server: Generates an order form and confirms the order information. Based on the data from the emotion engine, the server sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this smartphone."

[0636] This invention takes into account not only the user's hidden needs but also their current emotions, enabling more personalized product suggestions and significantly improving the purchasing experience on e-commerce sites.

[0637] The processing flow will be explained below.

[0638] Step 1:

[0639] The user accesses the e-commerce site using a device and enters "I want a new smartphone" into the chatbot interface.

[0640] Step 2:

[0641] The device sends the user's request information to the server, which then launches a chatbot interface using generative AI to generate additional questions for the user, such as "purpose, desired features, price range, etc."

[0642] Step 3:

[0643] The user confirms the additional questions displayed on the device and answers, "I need a high-performance camera for gaming, and my budget is less than 50,000 yen."

[0644] Step 4:

[0645] The device sends the user's response to the server. The emotion engine analyzes the user's response and recognizes, for example, that the user is "excited."

[0646] Step 5:

[0647] The server uses generative AI to analyze the user's detailed needs, extracting specific needs such as "gaming use," "high-performance camera," and "budget under 50,000 yen" as a result of the analysis.

[0648] Step 6:

[0649] The server takes into account the analysis results of the emotion engine and adjusts the search results based on the user's excitement level, for example generating a comment such as "A smartphone that offers an exciting gaming experience."

[0650] Step 7:

[0651] The server searches the e-commerce site's database for products that match the criteria, and uses generative AI to generate a list of optimal products, sending it to the device along with a message suggesting the product to the user. The device then displays this list to the user.

[0652] Step 8:

[0653] The user selects one of the presented smartphones and enters a follow-up question: "What are the camera specs of this smartphone?" The device then sends this request to the server.

[0654] Step 9:

[0655] The server retrieves detailed information about the selected smartphone from a database and uses an emotion engine to process the information to further enhance the user's excitement. For example, it might add a comment such as, "This smartphone's camera function is a hot topic on social media."

[0656] Step 10:

[0657] The server sends the generated details to the terminal, which displays them to the user.

[0658] Step 11:

[0659] The user confirms the details and enters the final decision, saying, "I will purchase this smartphone." The device then sends this purchase decision information to the server.

[0660] Step 12:

[0661] The server generates a purchase procedure form and sends it to the terminal to collect the necessary information (credit card information, shipping address information, etc.). The terminal displays this form to the user, who then enters the necessary information.

[0662] Step 13:

[0663] The terminal sends the entered purchase information to the server, which then verifies the order and confirms the order.

[0664] Step 14:

[0665] The server generates an order confirmation email and uses the emotion engine to send it to the user with a positive message, such as "Thank you for your purchase! Many users are happy with this smartphone."

[0666] Step 15:

[0667] The user receives the smartphone they purchased, sets it up using the after-sales service information, and enters their satisfaction and reviews.

[0668] Step 16:

[0669] The device sends user review information to a server, which stores this information in a database and uses it for future marketing and product improvements. The server also generates targeted advertisements based on the collected data and delivers them to users at the appropriate time.

[0670] Example 2

[0671] 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."

[0672] Conventional product recommendation systems for e-commerce sites have difficulty making appropriate recommendations based on user requirements. In particular, it is difficult to make product recommendations that take user emotions into consideration, resulting in problems that do not improve the user experience. Furthermore, since it is not possible to accurately grasp users' hidden needs, there is a risk of user satisfaction decreasing.

[0673] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0674] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs, means for recognizing the user's emotions and adjusting the request information based on the emotions, means for searching for optimal products and services from a product database based on the hidden needs and emotion information, and means for presenting the search results to the user, thereby enabling personalized product suggestions that take the user's hidden needs and emotions into consideration.

[0675] "User" means an individual or corporation that uses the EC site to provide information about products and services.

[0676] "Request information" is detailed information about the product or service desired by the user, including, for example, the purpose, desired functions, price range, and the like.

[0677] "Analysis" is the process in which the server uses the generated AI model to analyze the user's request information and extract the user's hidden needs.

[0678] "Hidden needs" are potential requirements or desires that are important to users but are not explicitly requested by the user, and are extracted by the generative AI model.

[0679] "Emotion recognition" means that the emotion engine analyzes and recognizes emotions (excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation.

[0680] "Adjusting request information based on emotions" means adjusting the product proposal method and expression according to the recognized user emotions.

[0681] A "product database" is a database that stores information about various products and services offered by an e-commerce site.

[0682] "Searching for products and services" means finding the most suitable products and services from a product database based on the user's hidden needs and emotional information.

[0683] "Detailed information" refers to specific product information that interests users, such as product specifications, features, reviews, and warranty information.

[0684] The "additional questions" are questions that are asked to dig deeper into the requested information that the user provided initially.

[0685] "Adjustment" refers to optimizing the content of product suggestions and detailed information presented based on the user's emotional data.

[0686] "Past purchase history and browsing history" refers to data on purchases and browsing that a user has made on an e-commerce site.

[0687] "Recommending personalized products and services" means individually proposing optimal products and services by taking into account a user's past purchasing history, browsing history, hidden needs, and emotional information.

[0688] MODE FOR CARRYING OUT THE INVENTION

[0689] This invention relates to an e-commerce concierge system that collects user request information, discovers hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes user emotions, more personalized proposals can be made.

[0690] The system consists of the following main components:

[0691] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[0692] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0693] 3. Server: A computer system that receives information from users, analyzes it using a generative AI model and emotion engine, and makes product suggestions.

[0694] Specifically, a user accesses an e-commerce site through a device and inputs request information, such as "I want a new smartphone," into the chatbot interface. This information is sent to the server via the device. The server uses a generative AI model to generate follow-up questions for the user, and presents prompts to the user, such as "What is the purpose, desired features, and price range?" Examples of such prompts are as follows:

[0695] What is your budget?

[0696] If the user responds with, "I want a smartphone that can play games and has a high-performance camera," the device sends this information back to the server. The server then uses a generative AI model and emotion engine to analyze the collected request information and extract the user's hidden needs. For example, it might discover that the user is looking for a smartphone specialized for gaming.

[0697] Furthermore, the emotion engine recognizes emotions from the user's responses. For example, if the server senses that the user is excited, it adjusts its suggestions based on this emotional information. Specifically, it searches the e-commerce site's database for products that meet the user's needs and adjusts the suggestions based on the emotion engine's information. For example, it adds a comment such as, "This smartphone is particularly good at games."

[0698] When a user selects a product that interests them from the suggested items and requests more information, the device sends this request to the server. The server uses a generative AI model to generate detailed product information and provides it to the user, taking into account data from the emotion engine. For example, if the user is excited, the server adds an additional comment such as, "This smartphone has a camera that is highly acclaimed on social media."

[0699] Once the user finally decides to purchase, the device sends this information to the server. The server generates an order form, collects the necessary information, and then confirms the order. The server also uses a generative AI model to generate an order confirmation email and follow-up information, which are then sent to the user. For example, the email may contain a positive message such as, "Thank you for your purchase! Many users are delighted with this smartphone."

[0700] This system enables personalized suggestions that take into account users' hidden needs and emotions, dramatically improving the shopping experience on e-commerce sites.

[0701] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0702] Step 1:

[0703] User: Accesses the e-commerce site using a device and inputs "I want a new smartphone" into the chatbot interface. This becomes the input information and is treated as the user's initial request information.

[0704] Terminal: Sends user input information to the server. The input here is the user's request information, and the output is the request data sent to the server.

[0705] Step 2:

[0706] Server: Launches the generative AI model and generates follow-up questions based on the user's initial request information. Specifically, the generative AI model inputs prompts such as "What are the intended use, desired features, and price range?" to create optimal follow-up questions for the user. The input is the user's request information, and the output is the follow-up questions.

[0707] Server: Sends the generated follow-up questions to the terminal. The input here is the follow-up questions, and the output is sent to the terminal.

[0708] Terminal: Display a follow-up question to the user.

[0709] Step 3:

[0710] User: In response to the follow-up question, answers, "I want something that can play games and has a good camera." This becomes new input information.

[0711] Terminal: Sends the user's answer to the server. The input is the user's answer and the output is what is sent to the server.

[0712] Step 4:

[0713] Server: Analyzes the user's responses using a generative AI model and emotion engine. The generative AI model extracts hidden needs, such as the specific features the user desires (game performance, camera performance). The emotion engine identifies the user's emotional state (excitement, etc.) from the user's responses. The input is the user's detailed request information, and the output is hidden needs and emotion information.

[0714] Step 5:

[0715] Server: Searches for the optimal smartphone from a product database based on the user's hidden needs and emotional information. Specifically, it extracts products from the database that meet hidden needs (gaming performance, camera performance), and adjusts the suggestions based on the emotional information. For example, for a user who is feeling excited, it emphasizes "the latest gaming-specific model." The input is the hidden needs and emotional information, and the output is a list of adjusted products.

[0716] Server: Sends the adjusted search results to the device.

[0717] Step 6:

[0718] Device: Presents search results to the user. The input here is a tailored list of products, and the output is a display to the user, such as "This phone is great for gaming and has a great camera."

[0719] Step 7:

[0720] User: Selects a suggested product that interests them and requests more information about it. For example, they might type, "Tell me more about the camera performance of this phone."

[0721] Terminal: Sends this request to the server. The input is the user's request for more information, and the output is the transmission to the server.

[0722] Step 8:

[0723] Server: Generates detailed information about the specified product based on the user's request. Using a generative AI model, it creates detailed information such as product specifications, features, and reviews, and adjusts the information taking into account data from the emotion engine. For example, if the user is excited, it adds an additional comment such as "This smartphone has a camera performance that is highly rated on social media." The input is the user's detailed request information, and the output is detailed information.

[0724] Server: Sends detailed information to the device.

[0725] Step 9:

[0726] Terminal: Presents detailed information to the user. The input is the adjusted detailed information, and the output is what is displayed to the user.

[0727] Step 10:

[0728] User: Checks the product details and enters the final decision, "I'll buy this smartphone."

[0729] Terminal: Sends purchase decision information to the server. The input is the user's purchase decision information, and the output is the transmission to the server.

[0730] Step 11:

[0731] Server: Generates the checkout form and collects the necessary information. The input is the user's purchasing decision information, and the output is the checkout form.

[0732] Server: Confirms the order. Additionally, the generative AI model is used to create an order confirmation email and after-sales information, and an emotional engine is used to add a positive message. For example, "Thank you for your purchase! Many users are pleased with this smartphone." The input is the purchase decision information and the content generated by the generative AI model, and the output is a confirmation email.

[0733] Step 12:

[0734] Server: Sends an order confirmation email to the terminal and provides it to the user. The input is the confirmation email and the output is what is sent to the user.

[0735] (Application example 2)

[0736] 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."

[0737] Conventional e-commerce sites typically use systems that suggest products and services based on user requirements, but it has been difficult to provide personalized suggestions that take into account the user's hidden needs and emotions. As a result, users have difficulty finding the products and services they truly want, resulting in a poor quality shopping experience. The present invention aims to solve these problems by comprehensively analyzing users' emotions and requirements, discovering their hidden needs, and suggesting optimal products and services.

[0738] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving request information regarding products and services from a user, means for analyzing the request information and analyzing the user's hidden needs and emotions, means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions, and means for presenting the search results to the user and providing supplemental information according to the emotions. This makes it possible to make personalized product suggestions that take into account the user's hidden needs and emotions.

[0739] The "means for receiving requested information about products and services from users" refers to an interface and communication means for collecting the user's input desires and needs for products and services.

[0740] The "means for analyzing the request information and extracting the user's hidden needs" refers to an algorithm that uses a generative AI model to derive the user's latent requests and needs based on information explicitly provided by the user.

[0741] The "means for analyzing the hidden needs and the user's emotions" is an engine that has the function of complementing the user's hidden needs with an emotion engine and analyzing the user's emotional state (e.g., excitement, anxiety, joy, etc.).

[0742] The "means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions" refers to an algorithm and search engine that selects the most suitable products and services from a database based on the user's needs and emotional information.

[0743] "Means for presenting search results to the user and providing supplementary information according to their emotions" refers to an interface that displays information about the searched products or services to the user and has the function of adding additional explanations or related information according to the user's emotional state.

[0744] The "means for adjusting the content of the proposal based on the emotion" is an algorithm that dynamically changes the content of the proposed products and services in response to the user's emotion recognized by the emotion engine.

[0745] "Means for recommending personalized products and services by taking into account a user's past purchasing history and browsing history" refers to an algorithm that retrieves a user's past purchasing history and browsing history from a database and recommends personalized products and services accordingly.

[0746] The "means for optimizing product proposals using the emotion data" is an algorithm for optimizing the selection and presentation method of products to be proposed using emotion data obtained from the emotion engine.

[0747] This invention provides an e-commerce system that proposes optimal products and services based on user requirements and emotional data. The system consists of the following main components:

[0748] A means of receiving product and service request information from users

[0749] A means for analyzing the request information and analyzing the user's hidden needs and feelings.

[0750] A means for searching for the most suitable products and services from a product database based on said hidden needs and recognized emotions.

[0751] A means of presenting search results to users and providing supplementary information according to their emotions

[0752] means for adjusting the content of the proposal based on said emotion;

[0753] A means of recommending personalized products and services that take into account a user's past purchasing and browsing history

[0754] means for optimizing product recommendations using said emotion data;

[0755] Natural language description of the program:

[0756] 1. User Interface:

[0757] The server provides an interface for users to input their request information about products and services. Users access the chatbot interface via their smartphone or PC and input their request information, such as "I want a new smartphone." This operation causes the device to send the request information to the server.

[0758] 2. Requirements information collection and analysis:

[0759] Once the server receives the request information, it analyzes it using a generative AI model. Through this analysis, it extracts the user's hidden needs. The emotion engine also analyzes the user's input content and tone for emotional data (e.g., excitement, anxiety, joy, etc.). As a result of this analysis, the user's specific needs and emotional state are revealed.

[0760] 3. Product search and sentiment-based tailoring:

[0761] The server searches for the most suitable products and services from a product database based on the hidden needs and emotion data. The emotion engine adjusts the content of the suggestions based on the user's emotions. For example, if the user is feeling anxious, the server may highlight the reliability of the product and review information.

[0762] 4. Presenting search results and providing supplementary information:

[0763] The server presents the user with search results for the most suitable products. If the user is interested, it provides further details. At this time, it supports the user's purchasing decision by displaying supplemental information according to the user's emotions (e.g., "This smartphone is very popular").

[0764] 5. Personalization based on past history:

[0765] The server takes into account the user's past purchasing and browsing history and uses it to recommend personalized products and services. Using emotion data, the recommendations can be further personalized, increasing user satisfaction.

[0766] Examples:

[0767] Below are some concrete examples of how the system can be used:

[0768] User: "I want a new smartphone that can play games and has a good camera," types the chatbot.

[0769] Server: Uses a generative AI model to ask the user, "What's your budget?" The emotion engine recognizes the user's excitement.

[0770] User: "Under $50,000."

[0771] Server: Searches for products from the database based on the user's needs and adjusts the search results taking into account the analysis results of the sentiment engine. For example, it adds comments to the suggestions, such as "This phone is particularly good at gaming."

[0772] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[0773] Server: Generates detailed information about the camera performance of the specified smartphone and provides information to further pique the user's interest, such as "This smartphone has a camera performance that is well-received on social media."

[0774] User: Makes the final decision to "purchase this smartphone."

[0775] Server: Generates the order checkout form and sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this phone."

[0776] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0777] Step 1:

[0778] The user inputs their request information about a product or service through the chatbot interface. For example, the input information might be something like, "I want a new smartphone. I want one that can play games and has a high-performance camera." The device then sends this information to the server. In this process, the user's request information becomes input data, and this data is sent to the server.

[0779] Step 2:

[0780] The request information received by the server is analyzed using a generative AI model. Specifically, the chatbot generates a follow-up question, such as "What is your budget?", and sends it to the user. The user responds, for example, "Less than 50,000 yen." In this step, the user's specific request information is collected and stored in a database.

[0781] Step 3:

[0782] The server uses an emotion engine to analyze emotion data from the user's input. Here, emotions (excitement, doubt, anxiety, etc.) are recognized from the content of the user's input (e.g., "It's under 50,000 yen") and the tone of the input. The analysis result detects that the user is excited. In this step, the user's text is used as input data, and the user's emotional state is obtained as output data.

[0783] Step 4:

[0784] The server searches the product database for the most suitable products and services based on the request information and emotion data mentioned above. The search results are then reflected in the emotion engine's analysis results, and a comment such as "This smartphone is particularly good at games" is added. The user's request information, emotion data, and database are used as input, and the search results with comments corresponding to the emotion are obtained as output.

[0785] Step 5:

[0786] The server presents the search results to the user. If the user is interested, they may ask a specific question, such as, "Tell me more about the camera performance of this smartphone." The server receives this request and provides detailed information. For example, it may present the result with additional information, such as, "This smartphone has a camera performance that is highly rated on social media." In this step, the additional question from the user becomes input data, and the detailed information based on that question becomes output data.

[0787] Step 6:

[0788] The user finally decides to purchase and enters "I will buy this smartphone." The device sends the purchase decision information to the server. The server generates an order procedure form, collects the necessary information, and then confirms the order. It also sends an order confirmation email with a positive message based on the emotion engine data, such as "Thank you for your purchase! Many users are pleased with this smartphone." In this step, the user's purchase decision information is the input data, and the completion of the order procedure and the confirmation email are the output data.

[0789] Step 7:

[0790] The server takes into account the user's past purchasing and browsing history to optimize future personalized product suggestions. In this step, past purchasing history and emotion data are used as inputs, and personalized suggestion information is the output data.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] [Third embodiment]

[0795] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0796] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0797] 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).

[0798] 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.

[0799] 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.

[0800] 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).

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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."

[0807] This invention relates to an e-commerce concierge system that collects user request information, analyzes those requests to discover hidden needs, and proposes optimal products and services. This system supports users in selecting, purchasing, and following up on products and services when using an e-commerce site through a chatbot interface that uses a generative AI model.

[0808] System Overview

[0809] The system consists of the following main components:

[0810] 1. User: An individual or corporation that accesses an e-commerce site and provides information about products and services.

[0811] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0812] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[0813] Program processing flow

[0814] Step 1: Gather user needs

[0815] A user accesses an e-commerce site through a device and inputs their request, such as "I want a new smartphone." The device then sends this information to a server. The server then launches a chatbot interface using generative AI and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[0816] Step 2: Needs analysis and product proposal

[0817] The server uses a generative AI model to analyze the request information collected from the user and extract the user's hidden needs. For example, it clarifies specific needs such as "gaming use, high-performance camera, budget under 50,000 yen." Based on the results of this analysis, the server searches the e-commerce site's database for relevant products and identifies the most suitable products and services. It then presents these search results to the user.

[0818] Step 3: Provide details and purchase assistance

[0819] If the user selects an item that interests them from the displayed items and requests more detailed information, the terminal sends this request to the server. The server generates detailed information about the selected item (specs, features, price, etc.) and provides it to the user. If the user indicates their intention to purchase the item, a purchase procedure form is displayed through the terminal, and the user enters the necessary information.

[0820] Step 4: Confirm your purchase and follow-up

[0821] Once the user has completed the purchase process, the terminal sends the order information to the server. The server confirms the order and sends the user an order confirmation email and post-purchase follow-up information (such as instructions for use and warranty information). The server also collects user ratings and feedback, which it uses as data for future product suggestions and service improvements.

[0822] Specific examples

[0823] Below are some specific examples of how this system can be used.

[0824] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0825] 2. Device: Sends this information to the server.

[0826] 3. Server: Uses generative AI to ask the user, "What is your budget?"

[0827] 4. User: "It's under 50,000 yen."

[0828] 5. Server: Based on the recommendation, the server searches the EC site's database for smartphones that meet the criteria and presents candidates to the user.

[0829] 6. User: Ask a specific question like, "Can you tell me more about the camera performance of this phone?"

[0830] 7. Server: Generates and provides detailed information about the camera capabilities of the specified smartphone to the user.

[0831] 8. User: Makes the final decision: "I will buy this smartphone."

[0832] 9. Server: Generates the checkout form and confirms the order information.

[0833] This invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[0834] The processing flow will be explained below.

[0835] Step 1:

[0836] The user accesses the e-commerce site using a device and enters the requested information into the chatbot interface, such as "I want a new smartphone."

[0837] Step 2:

[0838] The device sends the user's request information to the server, which then uses generative AI to activate a chatbot and begin a dialogue with the user.

[0839] Step 3:

[0840] The server uses generative AI to analyze the user's request information and generate additional questions such as "What do you want to use it for?", "What features do you want?", and "What is your budget?"

[0841] Step 4:

[0842] The device displays additional questions to the user, who answers, "I need a high-performance camera for gaming, and my budget is under 50,000 yen."

[0843] Step 5:

[0844] The device sends the user's answers to the server, which uses generative AI to analyze the user's detailed needs. It identifies the user's hidden needs and extracts the requirements of "gaming use," "high-performance camera," and "budget under 50,000 yen."

[0845] Step 6:

[0846] The server searches the e-commerce site's database for products that match the requirements, and uses generative AI to generate a list of the most suitable smartphones.

[0847] Step 7:

[0848] The server sends the generated product list to the terminal along with a message suggesting the product to the user, and the terminal displays the list to the user.

[0849] Step 8:

[0850] The user selects one of the suggested products and requests more information about it, for example, by entering "What are the camera specs of this smartphone?"

[0851] Step 9:

[0852] The device sends the user's request to the server, which retrieves detailed information about the selected product (such as the camera's specs, performance, and price) from a database and compiles it into a form that is easy for the generative AI to understand.

[0853] Step 10:

[0854] The server sends the generated details to the terminal, which displays them to the user.

[0855] Step 11:

[0856] The user checks the product details and enters the final decision, "I will purchase this smartphone."

[0857] Step 12:

[0858] The terminal sends the purchase decision information to the server, which generates an order form and asks the user to enter credit card information and shipping address information.

[0859] Step 13:

[0860] The terminal displays an order procedure form to the user, and the user enters the necessary information, after which the terminal sends the information to the server.

[0861] Step 14:

[0862] The server checks the order information and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (usage instructions, warranty information) and sends them to the user.

[0863] Step 15:

[0864] The user receives the purchased item, sets up and uses the smartphone based on the after-sales information, and sends their satisfaction and feedback to the server via the channel.

[0865] Step 16:

[0866] The server stores user feedback in a database and uses it for future marketing and product planning. It also generates targeted advertisements based on user data and delivers them appropriately.

[0867] Example 1

[0868] 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."

[0869] Conventional e-commerce systems have the problem that it takes a great deal of time and effort for users to clearly understand their needs and select the most suitable products and services. It is also difficult to effectively extract hidden needs that users have and appropriately suggest products and services that meet those needs. This can lead to a decrease in user satisfaction on e-commerce sites, and there has been a demand for an improved purchasing experience.

[0870] 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.

[0871] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs using a product model, means for searching a database for optimal products and services based on the hidden needs, means for presenting the search results to the user, and means for collecting additional request information from the user via an interactive interface. This makes it possible to effectively extract hidden needs based on the user's request information and quickly propose optimal products and services that meet those needs.

[0872] "User" means an individual or corporation that accesses the EC site and provides information about products and services.

[0873] "Product and service requirement information" refers to information such as the features, functions, and budget of a specific product or service that the user wants.

[0874] "Product modeling" is a technology that uses AI to analyze user input information and generate new insights and data.

[0875] "Hidden needs" refer to requests and desires that users have not explicitly recognized but that they implicitly desire.

[0876] A "database" is an organized collection of information, an information system designed to be easy to search and retrieve.

[0877] "Search results" are a list of suitable products and services retrieved from a database based on the user's request information and hidden needs.

[0878] An "interactive interface" refers to interactive software for communicating with users and gathering information through questions and answers.

[0879] "Additional requested information" refers to the user providing more detailed information or specific questions in addition to the initial requested information.

[0880] The present invention relates to an e-commerce concierge system that collects user request information, analyzes the requests to discover the user's hidden needs, and proposes optimal products and services. This system is specifically implemented using the following steps.

[0881] First, a user accesses an e-commerce site using a device such as a smartphone or PC (hereafter referred to as a terminal) and inputs request information, such as "I want a new smartphone." This request information reflects detailed needs based on the user's usage and preferences.

[0882] The device then immediately sends this input information to the server. This communication uses a common security protocol (e.g., HTTPS), ensuring secure data transfer. The server then analyzes the received request information using a product model (e.g., OpenAI's GPT-4). This analysis extracts not only the user's explicit needs, but also their latent and hidden needs.

[0883] For example, if a user's request information includes specific needs such as "gaming use, high-performance camera, budget under 50,000 yen," the server will search the database to suggest the most suitable products and services based on this.The databases used here are those of large-scale e-commerce sites such as Amazon and Rakuten Ichiba, and are accessed via APIs.

[0884] The server organizes the search results and creates a list of products that are most suitable for the user. This list is then presented to the user via their device. The user reviews the presented list of products, and if they want more detailed information, they can enter, for example, "Please tell me more about the camera performance of this smartphone." This input information is also sent from the device to the server, and the product model operates again to generate a detailed answer.

[0885] The system also provides detailed information and purchase assistance. When a user indicates their intention to purchase, the server provides a purchase procedure form via their terminal, and the user enters the necessary information. The server confirms the order information and sends the user an order confirmation email and after-sales information (such as instructions for use and warranty information). At this time, the server can also take into account the user's past purchase history and browsing history to make personalized product and service suggestions.

[0886] Examples of prompt statements

[0887] Below are some examples of prompt sentences.

[0888] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0889] 2. Server: "What's your budget?"

[0890] 3. User: "It's under $500."

[0891] 4. Server: "Then, we recommend the following smartphones." (Shows search results)

[0892] 5. User: "Can you tell me more about the camera performance of this phone?"

[0893] 6. Server: "The camera performance of this smartphone is..." (provides more information)

[0894] In this way, the present invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[0895] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0896] Step 1:

[0897] A user accesses an EC site using a terminal and inputs request information such as "I want a new smartphone." The request information "I want a new smartphone" is obtained as input. This input data is sent from the terminal to the server.

[0898] Step 2:

[0899] The terminal immediately sends the user's input information to the server. Specifically, this input information is transferred to the server using a security protocol (e.g., HTTPS). As data processing, the user's request information is encoded and converted into a secure format. As output, the encoded request information is sent to the server.

[0900] Step 3:

[0901] The server calls a generative AI model (e.g., OpenAI's GPT-4) to analyze the received request information. It receives the encoded request information as input. The output obtained using the generative AI model generates questions to further collect the user's detailed request information (e.g., "What is the purpose?", "What features and price range do you want?").

[0902] Step 4:

[0903] The server sends an interface containing the generated question to the device. As input, it has the output of the generative AI model. As output, it sends the interface to the device.

[0904] Step 5:

[0905] The user answers additional questions through the chatbot interface. For example, they input detailed requirements such as "for gaming, high-performance camera, budget under 50,000 yen." The user's answer is received as input. This answer is then sent from the device to the server.

[0906] Step 6:

[0907] The terminal sends the user's answer back to the server. As input, there is the user's additional request information. As output, this is sent to the server.

[0908] Step 7:

[0909] The server uses a generative AI model to analyze the collected detailed user request information and extract the user's hidden needs. The detailed request information is given as input. The generative AI model processes the data by analyzing the request information and clarifying the hidden needs. The hidden needs are obtained as output.

[0910] Step 8:

[0911] The server searches the database based on the extracted hidden needs and identifies the most suitable products and services. The hidden needs are given as input. Specifically, it executes a search query against the e-commerce site's database using an API. The output is the search results.

[0912] Step 9:

[0913] The server generates an interface for presenting the search results to the user and sends it to the terminal. The search results are used as input, and the interface is sent to the terminal as output.

[0914] Step 10:

[0915] The user selects an item that interests them from the displayed items, and if they want more information, they input a specific question. For example, they might input, "Please tell me more about the camera performance of this smartphone." The user's specific question is obtained as input.

[0916] Step 11:

[0917] The terminal sends the user's specific question to the server. As input, there is the user's specific question. As output, this is sent to the server.

[0918] Step 12:

[0919] The server generates detailed information about the selected product using a generative AI model. The input is a specific question from the user. Data processing involves retrieving detailed product information from a database and organizing and generating it using the generative AI model. The output is detailed information.

[0920] Step 13:

[0921] The server sends the generated detailed information to the terminal. As input, there is detailed information. As output, the interface is sent to the terminal.

[0922] Step 14:

[0923] The user checks the detailed information and, if he / she wishes to purchase, inputs "I will purchase this smartphone." The user's intention to purchase is obtained as input.

[0924] Step 15:

[0925] The terminal transmits the user's purchase intention to the server. As input, there is the purchase intention. As output, this is transmitted to the server.

[0926] Step 16:

[0927] The server generates a purchase procedure form and provides it to the user. The input is the intention to purchase. The output is a purchase procedure form that is generated and sent to the terminal.

[0928] Step 17:

[0929] The user enters the required information into the form (shipping address, payment information, etc.) and presses the submit button. The required information is obtained as input.

[0930] Step 18:

[0931] The terminal sends the transmitted order information to the server. As input, there is the order information. As output, this is sent to the server.

[0932] Step 19:

[0933] The server verifies and confirms the order, sends an order confirmation email and follow-up information (such as instructions for use and warranty information) to the user, and collects user ratings and feedback. The input is order information. The output is a confirmation email and follow-up information sent to the user. A feedback form is also provided.

[0934] (Application example 1)

[0935] 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."

[0936] In conventional e-commerce systems, users often have difficulty identifying their specific needs or selecting the most suitable product from the vast number of options available. Furthermore, general search functions are unable to fully uncover users' hidden needs, making it difficult to recommend the products and services they truly desire. Furthermore, response times are long when users request detailed information, making it difficult to support a smooth purchasing process. To address these issues, a system is needed that provides more personalized recommendations tailored to user needs and a smooth dialogue interface.

[0937] 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.

[0938] In this invention, the server includes a means for receiving product and service request information from a user, a means for analyzing the request information and extracting the user's hidden needs, a means for searching for optimal products and services from a product database based on the hidden needs, a means for presenting the search results to the user, and a means for providing an interactive interface with the user using a generative AI model. This makes it possible to effectively uncover the user's hidden needs and quickly propose optimal products and services. Furthermore, it is possible to improve the user's experience and smoothly support the purchasing process.

[0939] "User" means any individual or entity that provides requested information regarding goods or services.

[0940] "Requirement information" refers to the specific specifications and desired conditions of the product or service that the user is looking for.

[0941] "Hidden needs" are latent requirements and expectations that users have not explicitly communicated but that become apparent through analysis.

[0942] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate dialogue with users. Specifically, it has the ability to generate natural language based on input data and respond to users.

[0943] A "conversational interface" is an interface that allows a user to interact with a system, allowing the user to enter questions and receive immediate answers.

[0944] A "product database" is a database that stores information about various products and services. Each product includes detailed specifications, prices, user reviews, and other information.

[0945] "Search means" refers to the functions and algorithms used to find the most suitable products and services based on the user's requirements from a product database.

[0946] A "presentation mechanism" is a method or system for presenting search results to a user visually or audibly.

[0947] The present invention relates to an e-commerce concierge system that collects request information from users, analyzes those requests to discover the users' hidden needs, and proposes optimal products and services. This system provides a chatbot interface using a generative AI model via devices such as smartphones and tablets, and grasps the users' needs through dialogue with them and proposes products. Details of embodiments of the present invention are described below.

[0948] First, a user accesses the system through a device such as a smartphone or tablet. A chatbot application using a generative AI model is installed on the device. This application provides an interface for receiving requested information from the user.

[0949] The server receives and analyzes the request information sent by the user. Specifically, it analyzes the request information using a generative AI model (e.g., GPT-4) to extract hidden needs that the user has not explicitly communicated. This analysis uses natural language processing technology to identify potential needs based on the user's input.

[0950] Next, the server searches the product database for the most suitable products and services based on the extracted needs. For example, if a user inputs "I want a new smartphone," and further answers "one that can play games, has a high-performance camera, and a budget of less than 50,000 yen," the server searches the database for products that meet these conditions. A database search algorithm is used for the search.

[0951] The search results are displayed on the screen of a smartphone or tablet. The user can review the displayed search results and select the product that interests them. If the user wants more detailed information, they can ask the chatbot an additional question. For example, they can enter a question such as, "Please tell me more about the camera performance of this smartphone." The server receives this request, generates detailed information about the selected product, and provides it to the user.

[0952] Once the user decides to purchase, the chatbot interface will help them through the purchase process smoothly. A purchase form will be generated and the order will be confirmed by entering the necessary information. After the purchase, after-sales information (e.g., how to use, warranty details, etc.) will also be provided.

[0953] Below are some examples and prompts:

[0954] Specific examples

[0955] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0956] Terminal: Sends this information to the server.

[0957] Server: Uses generative AI to ask the user, "What is your budget?"

[0958] User: "It's under 50,000 yen."

[0959] Server: Searches for smartphones that meet the criteria and presents candidates to the user.

[0960] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[0961] Server: Generates detailed information about the camera capabilities of a specified smartphone and provides it to the user.

[0962] User: Makes the final decision: "I will buy this smartphone."

[0963] Server: Generates the checkout form and confirms the order information.

[0964] Prompt Sentence Examples

[0965] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[0966] Prompt: "To find out what kind of smartphone you're looking for, ask yourself these questions: 1. What will you use it for? 2. What features are important to you? 3. What is your budget?"

[0967] This invention makes it possible to effectively uncover hidden user needs and quickly propose optimal products and services. It also provides a smooth interactive interface, significantly improving the user's purchasing experience.

[0968] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0969] Step 1:

[0970] A user launches a chatbot application on their smartphone or tablet and inputs information about a product or service they require. For example, they might input, "I want a new smartphone." This input information is sent from the device to the server. To process the input information, the device collects the user's text data and transfers it to the server.

[0971] Step 2:

[0972] The server analyzes the received request information using a generative AI model (e.g., GPT-4). Specifically, it performs natural language processing to analyze the user's request information and extract hidden needs. For example, from a request such as "I want a new smartphone," specific needs such as "for gaming, with a high-performance camera, and a budget of less than 50,000 yen" can be extracted. The server generates the analysis results and prepares for the next step.

[0973] Step 3:

[0974] The server searches the product database for the most suitable products and services based on the extracted hidden needs. This search uses a database search algorithm. For example, it finds smartphones from the database that meet the criteria of "for gaming," "high-performance camera," and "budget under 50,000 yen." The server generates data on the search results and prepares for the next step.

[0975] Step 4:

[0976] The server sends data to the terminal to present the search results to the user. Specifically, it generates an optimal product list to meet the user's request information and provides it to the terminal. The terminal receives this data and displays it to the user through the chatbot interface. The user can then check the presented product list.

[0977] Step 5:

[0978] The user selects a product of interest from the presented product list and requests further details. For example, they might type, "Tell me more about the camera performance of this smartphone." The device then sends this request to the server. The system accurately collects the user's input and processes it for transmission to the server.

[0979] Step 6:

[0980] In response to a user's request, the server provides detailed information about the selected product. Specifically, it uses a generative AI model to extract detailed information from a product database and generate text to answer the user's question. For example, it may provide detailed information about the specifications and features of a smartphone's camera performance. The server then sends the generated detailed information to the device.

[0981] Step 7:

[0982] The terminal receives the detailed information sent from the server and displays it to the user, who can then review the presented details and make a final purchasing decision. The detailed information is visually displayed via a chatbot interface.

[0983] Step 8:

[0984] If the user decides to make a purchase, the terminal generates a purchase procedure form and presents it to the user. The user enters the necessary purchase information and confirms the order. The purchase information is then accurately collected and sent to the server.

[0985] Step 9:

[0986] The server receives the purchase information sent by the user and confirms the order. Specifically, it checks the order details, processes the payment, and generates an order confirmation email to send to the user. It also provides after-sales follow-up information after the purchase and collects data to improve user satisfaction. It processes the user's purchase information and generates after-sales follow-up data.

[0987] Through the above steps, the EC concierge system of the present invention effectively uncovers the user's hidden needs and quickly suggests optimal products and services, thereby realizing a smooth purchasing experience.

[0988] 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.

[0989] This invention relates to an e-commerce concierge system that collects user request information, discovers the user's hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes the user's emotions, more personalized proposals can be made.

[0990] System Overview

[0991] The system consists of the following main components:

[0992] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[0993] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[0994] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[0995] 4. Emotion engine: An engine for recognizing user emotions and tailoring suggestions based on those emotions.

[0996] Program processing flow

[0997] Step 1: Gather user needs

[0998] A user accesses an e-commerce site via a device and inputs their request, such as "I want a new smartphone," into the chatbot interface. The device then sends this information to the server. The server then activates the chatbot interface, which uses generative AI, and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[0999] Step 2: Emotion recognition and needs analysis

[1000] The server analyzes the request information collected from the user using a generative AI and an emotion engine. The generative AI analyzes the request information and extracts the user's hidden needs. Meanwhile, the emotion engine recognizes emotions (e.g., excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation. Based on this information, the server can more accurately understand the user's needs.

[1001] Step 3: Find and adjust product offers

[1002] The server searches the e-commerce site's database for products that match the user's needs and the emotions recognized. It also adjusts its suggestions by taking into account the emotional information provided by the emotion engine. For example, if the user is unsure, it may emphasize reviews and warranty information. The adjusted search results are then presented to the user.

[1003] Step 4: Provide details and purchase assistance

[1004] When a user selects an item that interests them from the displayed items and requests more information about it, the device sends this request to the server. The server generates detailed information about the selected item and provides it to the user, taking into account the emotion engine data. For example, if the user is excited, the server adds an additional comment such as "This is a popular model."

[1005] Step 5: Purchase decision and follow-up

[1006] The user checks the product details and enters the final decision, saying, "I'll buy this smartphone." The device then sends the purchase decision to the server. The server then generates an order form, collects the necessary information, and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (such as instructions for use and warranty information) and sends them to the user.

[1007] Specific examples

[1008] Below are some specific examples of how this system can be used.

[1009] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[1010] 2. Device: Sends this information to the server.

[1011] 3. Server: Using generative AI, ask the user, "What's your budget?" The emotion engine analyzes the user's input and recognizes that the user is excited.

[1012] 4. User: "It's under 50,000 yen."

[1013] 5. Server: Based on the user's needs, the server searches the e-commerce site's database for smartphones that fit the criteria, and then adjusts the search results by taking into account the analysis results of the emotion engine. For example, the server may add a comment to the suggestion, such as "This smartphone is particularly good at gaming."

[1014] 6. User: Asks a specific question, such as "Tell me more about the camera performance of this phone." The emotion engine recognizes the user's interest.

[1015] 7. Server: Generates detailed information about the camera performance of the specified smartphone, and based on data from the emotion engine, provides information to further pique the user's interest, such as "This smartphone has camera performance that is well-received on social media."

[1016] 8. User: Makes the final decision: "I will buy this smartphone."

[1017] 9. Server: Generates an order form and confirms the order information. Based on the data from the emotion engine, the server sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this smartphone."

[1018] This invention takes into account not only the user's hidden needs but also their current emotions, enabling more personalized product suggestions and significantly improving the purchasing experience on e-commerce sites.

[1019] The processing flow will be explained below.

[1020] Step 1:

[1021] The user accesses the e-commerce site using a device and enters "I want a new smartphone" into the chatbot interface.

[1022] Step 2:

[1023] The device sends the user's request information to the server, which then launches a chatbot interface using generative AI to generate additional questions for the user, such as "purpose, desired features, price range, etc."

[1024] Step 3:

[1025] The user confirms the additional questions displayed on the device and answers, "I need a high-performance camera for gaming, and my budget is less than 50,000 yen."

[1026] Step 4:

[1027] The device sends the user's response to the server. The emotion engine analyzes the user's response and recognizes, for example, that the user is "excited."

[1028] Step 5:

[1029] The server uses generative AI to analyze the user's detailed needs, extracting specific needs such as "gaming use," "high-performance camera," and "budget under 50,000 yen" as a result of the analysis.

[1030] Step 6:

[1031] The server takes into account the analysis results of the emotion engine and adjusts the search results based on the user's excitement level, for example generating a comment such as "A smartphone that offers an exciting gaming experience."

[1032] Step 7:

[1033] The server searches the e-commerce site's database for products that match the criteria, and uses generative AI to generate a list of optimal products, sending it to the device along with a message suggesting the product to the user. The device then displays this list to the user.

[1034] Step 8:

[1035] The user selects one of the presented smartphones and enters a follow-up question: "What are the camera specs of this smartphone?" The device then sends this request to the server.

[1036] Step 9:

[1037] The server retrieves detailed information about the selected smartphone from a database and uses an emotion engine to process the information to further enhance the user's excitement. For example, it might add a comment such as, "This smartphone's camera function is a hot topic on social media."

[1038] Step 10:

[1039] The server sends the generated details to the terminal, which displays them to the user.

[1040] Step 11:

[1041] The user confirms the details and enters the final decision, saying, "I will purchase this smartphone." The device then sends this purchase decision information to the server.

[1042] Step 12:

[1043] The server generates a purchase procedure form and sends it to the terminal to collect the necessary information (credit card information, shipping address information, etc.). The terminal displays this form to the user, who then enters the necessary information.

[1044] Step 13:

[1045] The terminal sends the entered purchase information to the server, which then verifies the order and confirms the order.

[1046] Step 14:

[1047] The server generates an order confirmation email and uses the emotion engine to send it to the user with a positive message, such as "Thank you for your purchase! Many users are happy with this smartphone."

[1048] Step 15:

[1049] The user receives the smartphone they purchased, sets it up using the after-sales service information, and enters their satisfaction and reviews.

[1050] Step 16:

[1051] The device sends user review information to a server, which stores this information in a database and uses it for future marketing and product improvements. The server also generates targeted advertisements based on the collected data and delivers them to users at the appropriate time.

[1052] Example 2

[1053] 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."

[1054] Conventional product recommendation systems for e-commerce sites have difficulty making appropriate recommendations based on user requirements. In particular, it is difficult to make product recommendations that take user emotions into consideration, resulting in problems that do not improve the user experience. Furthermore, since it is not possible to accurately grasp users' hidden needs, there is a risk of user satisfaction decreasing.

[1055] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1056] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs, means for recognizing the user's emotions and adjusting the request information based on the emotions, means for searching for optimal products and services from a product database based on the hidden needs and emotion information, and means for presenting the search results to the user, thereby enabling personalized product suggestions that take the user's hidden needs and emotions into consideration.

[1057] "User" means an individual or corporation that uses the EC site to provide information about products and services.

[1058] "Request information" is detailed information about the product or service desired by the user, including, for example, the purpose, desired functions, price range, and the like.

[1059] "Analysis" is the process in which the server uses the generated AI model to analyze the user's request information and extract the user's hidden needs.

[1060] "Hidden needs" are potential requirements or desires that are important to users but are not explicitly requested by the user, and are extracted by the generative AI model.

[1061] "Emotion recognition" means that the emotion engine analyzes and recognizes emotions (excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation.

[1062] "Adjusting request information based on emotions" means adjusting the product proposal method and expression according to the recognized user emotions.

[1063] A "product database" is a database that stores information about various products and services offered by an e-commerce site.

[1064] "Searching for products and services" means finding the most suitable products and services from a product database based on the user's hidden needs and emotional information.

[1065] "Detailed information" refers to specific product information that interests users, such as product specifications, features, reviews, and warranty information.

[1066] The "additional questions" are questions that are asked to dig deeper into the requested information that the user provided initially.

[1067] "Adjustment" refers to optimizing the content of product suggestions and detailed information presented based on the user's emotional data.

[1068] "Past purchase history and browsing history" refers to data on purchases and browsing that a user has made on an e-commerce site.

[1069] "Recommending personalized products and services" means individually proposing optimal products and services by taking into account a user's past purchasing history, browsing history, hidden needs, and emotional information.

[1070] MODE FOR CARRYING OUT THE INVENTION

[1071] This invention relates to an e-commerce concierge system that collects user request information, discovers hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes user emotions, more personalized proposals can be made.

[1072] The system consists of the following main components:

[1073] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[1074] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[1075] 3. Server: A computer system that receives information from users, analyzes it using a generative AI model and emotion engine, and makes product suggestions.

[1076] Specifically, a user accesses an e-commerce site through a device and inputs request information, such as "I want a new smartphone," into the chatbot interface. This information is sent to the server via the device. The server uses a generative AI model to generate follow-up questions for the user, and presents prompts to the user, such as "What is the purpose, desired features, and price range?" Examples of such prompts are as follows:

[1077] What is your budget?

[1078] If the user responds with, "I want a smartphone that can play games and has a high-performance camera," the device sends this information back to the server. The server then uses a generative AI model and emotion engine to analyze the collected request information and extract the user's hidden needs. For example, it might discover that the user is looking for a smartphone specialized for gaming.

[1079] Furthermore, the emotion engine recognizes emotions from the user's responses. For example, if the server senses that the user is excited, it adjusts its suggestions based on this emotional information. Specifically, it searches the e-commerce site's database for products that meet the user's needs and adjusts the suggestions based on the emotion engine's information. For example, it adds a comment such as, "This smartphone is particularly good at games."

[1080] When a user selects a product that interests them from the suggested items and requests more information, the device sends this request to the server. The server uses a generative AI model to generate detailed product information and provides it to the user, taking into account data from the emotion engine. For example, if the user is excited, the server adds an additional comment such as, "This smartphone has a camera that is highly acclaimed on social media."

[1081] Once the user finally decides to purchase, the device sends this information to the server. The server generates an order form, collects the necessary information, and then confirms the order. The server also uses a generative AI model to generate an order confirmation email and follow-up information, which are then sent to the user. For example, the email may contain a positive message such as, "Thank you for your purchase! Many users are delighted with this smartphone."

[1082] This system enables personalized suggestions that take into account users' hidden needs and emotions, dramatically improving the shopping experience on e-commerce sites.

[1083] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1084] Step 1:

[1085] User: Accesses the e-commerce site using a device and inputs "I want a new smartphone" into the chatbot interface. This becomes the input information and is treated as the user's initial request information.

[1086] Terminal: Sends user input information to the server. The input here is the user's request information, and the output is the request data sent to the server.

[1087] Step 2:

[1088] Server: Launches the generative AI model and generates follow-up questions based on the user's initial request information. Specifically, the generative AI model inputs prompts such as "What are the intended use, desired features, and price range?" to create optimal follow-up questions for the user. The input is the user's request information, and the output is the follow-up questions.

[1089] Server: Sends the generated follow-up questions to the terminal. The input here is the follow-up questions, and the output is sent to the terminal.

[1090] Terminal: Display a follow-up question to the user.

[1091] Step 3:

[1092] User: In response to the follow-up question, answers, "I want something that can play games and has a good camera." This becomes new input information.

[1093] Terminal: Sends the user's answer to the server. The input is the user's answer and the output is what is sent to the server.

[1094] Step 4:

[1095] Server: Analyzes the user's responses using a generative AI model and emotion engine. The generative AI model extracts hidden needs, such as the specific features the user desires (game performance, camera performance). The emotion engine identifies the user's emotional state (excitement, etc.) from the user's responses. The input is the user's detailed request information, and the output is hidden needs and emotion information.

[1096] Step 5:

[1097] Server: Searches for the optimal smartphone from a product database based on the user's hidden needs and emotional information. Specifically, it extracts products from the database that meet hidden needs (gaming performance, camera performance), and adjusts the suggestions based on the emotional information. For example, for a user who is feeling excited, it emphasizes "the latest gaming-specific model." The input is the hidden needs and emotional information, and the output is a list of adjusted products.

[1098] Server: Sends the adjusted search results to the device.

[1099] Step 6:

[1100] Device: Presents search results to the user. The input here is a tailored list of products, and the output is a display to the user, such as "This phone is great for gaming and has a great camera."

[1101] Step 7:

[1102] User: Selects a suggested product that interests them and requests more information about it. For example, they might type, "Tell me more about the camera performance of this phone."

[1103] Terminal: Sends this request to the server. The input is the user's request for more information, and the output is the transmission to the server.

[1104] Step 8:

[1105] Server: Generates detailed information about the specified product based on the user's request. Using a generative AI model, it creates detailed information such as product specifications, features, and reviews, and adjusts the information taking into account data from the emotion engine. For example, if the user is excited, it adds an additional comment such as "This smartphone has a camera performance that is highly rated on social media." The input is the user's detailed request information, and the output is detailed information.

[1106] Server: Sends detailed information to the device.

[1107] Step 9:

[1108] Terminal: Presents detailed information to the user. The input is the adjusted detailed information, and the output is what is displayed to the user.

[1109] Step 10:

[1110] User: Checks the product details and enters the final decision, "I'll buy this smartphone."

[1111] Terminal: Sends purchase decision information to the server. The input is the user's purchase decision information, and the output is the transmission to the server.

[1112] Step 11:

[1113] Server: Generates the checkout form and collects the necessary information. The input is the user's purchasing decision information, and the output is the checkout form.

[1114] Server: Confirms the order. Additionally, the generative AI model is used to create an order confirmation email and after-sales information, and an emotional engine is used to add a positive message. For example, "Thank you for your purchase! Many users are pleased with this smartphone." The input is the purchase decision information and the content generated by the generative AI model, and the output is a confirmation email.

[1115] Step 12:

[1116] Server: Sends an order confirmation email to the terminal and provides it to the user. The input is the confirmation email and the output is what is sent to the user.

[1117] (Application example 2)

[1118] 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."

[1119] Conventional e-commerce sites typically use systems that suggest products and services based on user requirements, but it has been difficult to provide personalized suggestions that take into account the user's hidden needs and emotions. As a result, users have difficulty finding the products and services they truly want, resulting in a poor quality shopping experience. The present invention aims to solve these problems by comprehensively analyzing users' emotions and requirements, discovering their hidden needs, and suggesting optimal products and services.

[1120] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving request information regarding products and services from a user, means for analyzing the request information and analyzing the user's hidden needs and emotions, means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions, and means for presenting the search results to the user and providing supplemental information according to the emotions. This makes it possible to make personalized product suggestions that take into account the user's hidden needs and emotions.

[1121] The "means for receiving requested information about products and services from users" refers to an interface and communication means for collecting the user's input desires and needs for products and services.

[1122] The "means for analyzing the request information and extracting the user's hidden needs" refers to an algorithm that uses a generative AI model to derive the user's latent requests and needs based on information explicitly provided by the user.

[1123] The "means for analyzing the hidden needs and the user's emotions" is an engine that has the function of complementing the user's hidden needs with an emotion engine and analyzing the user's emotional state (e.g., excitement, anxiety, joy, etc.).

[1124] The "means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions" refers to an algorithm and search engine that selects the most suitable products and services from a database based on the user's needs and emotional information.

[1125] "Means for presenting search results to the user and providing supplementary information according to their emotions" refers to an interface that displays information about the searched products or services to the user and has the function of adding additional explanations or related information according to the user's emotional state.

[1126] The "means for adjusting the content of the proposal based on the emotion" is an algorithm that dynamically changes the content of the proposed products and services in response to the user's emotion recognized by the emotion engine.

[1127] "Means for recommending personalized products and services by taking into account a user's past purchasing history and browsing history" refers to an algorithm that retrieves a user's past purchasing history and browsing history from a database and recommends personalized products and services accordingly.

[1128] The "means for optimizing product proposals using the emotion data" is an algorithm for optimizing the selection and presentation method of products to be proposed using emotion data obtained from the emotion engine.

[1129] This invention provides an e-commerce system that proposes optimal products and services based on user requirements and emotional data. The system consists of the following main components:

[1130] A means of receiving product and service request information from users

[1131] A means for analyzing the request information and analyzing the user's hidden needs and feelings.

[1132] A means for searching for the most suitable products and services from a product database based on said hidden needs and recognized emotions.

[1133] A means of presenting search results to users and providing supplementary information according to their emotions

[1134] means for adjusting the content of the proposal based on said emotion;

[1135] A means of recommending personalized products and services that take into account a user's past purchasing and browsing history

[1136] means for optimizing product recommendations using said emotion data;

[1137] Natural language description of the program:

[1138] 1. User Interface:

[1139] The server provides an interface for users to input their request information about products and services. Users access the chatbot interface via their smartphone or PC and input their request information, such as "I want a new smartphone." This operation causes the device to send the request information to the server.

[1140] 2. Requirements information collection and analysis:

[1141] Once the server receives the request information, it analyzes it using a generative AI model. Through this analysis, it extracts the user's hidden needs. The emotion engine also analyzes the user's input content and tone for emotional data (e.g., excitement, anxiety, joy, etc.). As a result of this analysis, the user's specific needs and emotional state are revealed.

[1142] 3. Product search and sentiment-based tailoring:

[1143] The server searches for the most suitable products and services from a product database based on the hidden needs and emotion data. The emotion engine adjusts the content of the suggestions based on the user's emotions. For example, if the user is feeling anxious, the server may highlight the reliability of the product and review information.

[1144] 4. Presenting search results and providing supplementary information:

[1145] The server presents the user with search results for the most suitable products. If the user is interested, it provides further details. At this time, it supports the user's purchasing decision by displaying supplemental information according to the user's emotions (e.g., "This smartphone is very popular").

[1146] 5. Personalization based on past history:

[1147] The server takes into account the user's past purchasing and browsing history and uses it to recommend personalized products and services. Using emotion data, the recommendations can be further personalized, increasing user satisfaction.

[1148] Examples:

[1149] Below are some concrete examples of how the system can be used:

[1150] User: "I want a new smartphone that can play games and has a good camera," types the chatbot.

[1151] Server: Uses a generative AI model to ask the user, "What's your budget?" The emotion engine recognizes the user's excitement.

[1152] User: "Under $50,000."

[1153] Server: Searches for products from the database based on the user's needs and adjusts the search results taking into account the analysis results of the sentiment engine. For example, it adds comments to the suggestions, such as "This phone is particularly good at gaming."

[1154] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[1155] Server: Generates detailed information about the camera performance of the specified smartphone and provides information to further pique the user's interest, such as "This smartphone has a camera performance that is well-received on social media."

[1156] User: Makes the final decision to "purchase this smartphone."

[1157] Server: Generates the order checkout form and sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this phone."

[1158] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1159] Step 1:

[1160] The user inputs their request information about a product or service through the chatbot interface. For example, the input information might be something like, "I want a new smartphone. I want one that can play games and has a high-performance camera." The device then sends this information to the server. In this process, the user's request information becomes input data, and this data is sent to the server.

[1161] Step 2:

[1162] The request information received by the server is analyzed using a generative AI model. Specifically, the chatbot generates a follow-up question, such as "What is your budget?", and sends it to the user. The user responds, for example, "Less than 50,000 yen." In this step, the user's specific request information is collected and stored in a database.

[1163] Step 3:

[1164] The server uses an emotion engine to analyze emotion data from the user's input. Here, emotions (excitement, doubt, anxiety, etc.) are recognized from the content of the user's input (e.g., "It's under 50,000 yen") and the tone of the input. The analysis result detects that the user is excited. In this step, the user's text is used as input data, and the user's emotional state is obtained as output data.

[1165] Step 4:

[1166] The server searches the product database for the most suitable products and services based on the request information and emotion data mentioned above. The search results are then reflected in the emotion engine's analysis results, and a comment such as "This smartphone is particularly good at games" is added. The user's request information, emotion data, and database are used as input, and the search results with comments corresponding to the emotion are obtained as output.

[1167] Step 5:

[1168] The server presents the search results to the user. If the user is interested, they may ask a specific question, such as, "Tell me more about the camera performance of this smartphone." The server receives this request and provides detailed information. For example, it may present the result with additional information, such as, "This smartphone has a camera performance that is highly rated on social media." In this step, the additional question from the user becomes input data, and the detailed information based on that question becomes output data.

[1169] Step 6:

[1170] The user finally decides to purchase and enters "I will buy this smartphone." The device sends the purchase decision information to the server. The server generates an order procedure form, collects the necessary information, and then confirms the order. It also sends an order confirmation email with a positive message based on the emotion engine data, such as "Thank you for your purchase! Many users are pleased with this smartphone." In this step, the user's purchase decision information is the input data, and the completion of the order procedure and the confirmation email are the output data.

[1171] Step 7:

[1172] The server takes into account the user's past purchasing and browsing history to optimize future personalized product suggestions. In this step, past purchasing history and emotion data are used as inputs, and personalized suggestion information is the output data.

[1173] 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.

[1174] 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.

[1175] 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.

[1176] [Fourth embodiment]

[1177] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1178] 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.

[1179] 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).

[1180] 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.

[1181] 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.

[1182] 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).

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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."

[1190] This invention relates to an e-commerce concierge system that collects user request information, analyzes those requests to discover hidden needs, and proposes optimal products and services. This system supports users in selecting, purchasing, and following up on products and services when using an e-commerce site through a chatbot interface that uses a generative AI model.

[1191] System Overview

[1192] The system consists of the following main components:

[1193] 1. User: An individual or corporation that accesses an e-commerce site and provides information about products and services.

[1194] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[1195] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[1196] Program processing flow

[1197] Step 1: Gather user needs

[1198] A user accesses an e-commerce site through a device and inputs their request, such as "I want a new smartphone." The device then sends this information to a server. The server then launches a chatbot interface using generative AI and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[1199] Step 2: Needs analysis and product proposal

[1200] The server uses a generative AI model to analyze the request information collected from the user and extract the user's hidden needs. For example, it clarifies specific needs such as "gaming use, high-performance camera, budget under 50,000 yen." Based on the results of this analysis, the server searches the e-commerce site's database for relevant products and identifies the most suitable products and services. It then presents these search results to the user.

[1201] Step 3: Provide details and purchase assistance

[1202] If the user selects an item that interests them from the displayed items and requests more detailed information, the terminal sends this request to the server. The server generates detailed information about the selected item (specs, features, price, etc.) and provides it to the user. If the user indicates their intention to purchase the item, a purchase procedure form is displayed through the terminal, and the user enters the necessary information.

[1203] Step 4: Confirm your purchase and follow-up

[1204] Once the user has completed the purchase process, the terminal sends the order information to the server. The server confirms the order and sends the user an order confirmation email and post-purchase follow-up information (such as instructions for use and warranty information). The server also collects user ratings and feedback, which it uses as data for future product suggestions and service improvements.

[1205] Specific examples

[1206] Below are some specific examples of how this system can be used.

[1207] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[1208] 2. Device: Sends this information to the server.

[1209] 3. Server: Uses generative AI to ask the user, "What is your budget?"

[1210] 4. User: "It's under 50,000 yen."

[1211] 5. Server: Based on the recommendation, the server searches the EC site's database for smartphones that meet the criteria and presents candidates to the user.

[1212] 6. User: Ask a specific question like, "Can you tell me more about the camera performance of this phone?"

[1213] 7. Server: Generates and provides detailed information about the camera capabilities of the specified smartphone to the user.

[1214] 8. User: Makes the final decision: "I will buy this smartphone."

[1215] 9. Server: Generates the checkout form and confirms the order information.

[1216] This invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[1217] The processing flow will be explained below.

[1218] Step 1:

[1219] The user accesses the e-commerce site using a device and enters the requested information into the chatbot interface, such as "I want a new smartphone."

[1220] Step 2:

[1221] The device sends the user's request information to the server, which then uses generative AI to activate a chatbot and begin a dialogue with the user.

[1222] Step 3:

[1223] The server uses generative AI to analyze the user's request information and generate additional questions such as "What do you want to use it for?", "What features do you want?", and "What is your budget?"

[1224] Step 4:

[1225] The device displays additional questions to the user, who answers, "I need a high-performance camera for gaming, and my budget is under 50,000 yen."

[1226] Step 5:

[1227] The device sends the user's answers to the server, which uses generative AI to analyze the user's detailed needs. It identifies the user's hidden needs and extracts the requirements of "gaming use," "high-performance camera," and "budget under 50,000 yen."

[1228] Step 6:

[1229] The server searches the e-commerce site's database for products that match the requirements, and uses generative AI to generate a list of the most suitable smartphones.

[1230] Step 7:

[1231] The server sends the generated product list to the terminal along with a message suggesting the product to the user, and the terminal displays the list to the user.

[1232] Step 8:

[1233] The user selects one of the suggested products and requests more information about it, for example, by entering "What are the camera specs of this smartphone?"

[1234] Step 9:

[1235] The device sends the user's request to the server, which retrieves detailed information about the selected product (such as the camera's specs, performance, and price) from a database and compiles it into a form that is easy for the generative AI to understand.

[1236] Step 10:

[1237] The server sends the generated details to the terminal, which displays them to the user.

[1238] Step 11:

[1239] The user checks the product details and enters the final decision, "I will purchase this smartphone."

[1240] Step 12:

[1241] The terminal sends the purchase decision information to the server, which generates an order form and asks the user to enter credit card information and shipping address information.

[1242] Step 13:

[1243] The terminal displays an order procedure form to the user, and the user enters the necessary information, after which the terminal sends the information to the server.

[1244] Step 14:

[1245] The server checks the order information and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (usage instructions, warranty information) and sends them to the user.

[1246] Step 15:

[1247] The user receives the purchased item, sets up and uses the smartphone based on the after-sales information, and sends their satisfaction and feedback to the server via the channel.

[1248] Step 16:

[1249] The server stores user feedback in a database and uses it for future marketing and product planning. It also generates targeted advertisements based on user data and delivers them appropriately.

[1250] Example 1

[1251] 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."

[1252] Conventional e-commerce systems have the problem that it takes a great deal of time and effort for users to clearly understand their needs and select the most suitable products and services. It is also difficult to effectively extract hidden needs that users have and appropriately suggest products and services that meet those needs. This can lead to a decrease in user satisfaction on e-commerce sites, and there has been a demand for an improved purchasing experience.

[1253] 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.

[1254] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs using a product model, means for searching a database for optimal products and services based on the hidden needs, means for presenting the search results to the user, and means for collecting additional request information from the user via an interactive interface. This makes it possible to effectively extract hidden needs based on the user's request information and quickly propose optimal products and services that meet those needs.

[1255] "User" means an individual or corporation that accesses the EC site and provides information about products and services.

[1256] "Product and service requirement information" refers to information such as the features, functions, and budget of a specific product or service that the user wants.

[1257] "Product modeling" is a technology that uses AI to analyze user input information and generate new insights and data.

[1258] "Hidden needs" refer to requests and desires that users have not explicitly recognized but that they implicitly desire.

[1259] A "database" is an organized collection of information, an information system designed to be easy to search and retrieve.

[1260] "Search results" are a list of suitable products and services retrieved from a database based on the user's request information and hidden needs.

[1261] An "interactive interface" refers to interactive software for communicating with users and gathering information through questions and answers.

[1262] "Additional requested information" refers to the user providing more detailed information or specific questions in addition to the initial requested information.

[1263] The present invention relates to an e-commerce concierge system that collects user request information, analyzes the requests to discover the user's hidden needs, and proposes optimal products and services. This system is specifically implemented using the following steps.

[1264] First, a user accesses an e-commerce site using a device such as a smartphone or PC (hereafter referred to as a terminal) and inputs request information, such as "I want a new smartphone." This request information reflects detailed needs based on the user's usage and preferences.

[1265] The device then immediately sends this input information to the server. This communication uses a common security protocol (e.g., HTTPS), ensuring secure data transfer. The server then analyzes the received request information using a product model (e.g., OpenAI's GPT-4). This analysis extracts not only the user's explicit needs, but also their latent and hidden needs.

[1266] For example, if a user's request information includes specific needs such as "gaming use, high-performance camera, budget under 50,000 yen," the server will search the database to suggest the most suitable products and services based on this.The databases used here are those of large-scale e-commerce sites such as Amazon and Rakuten Ichiba, and are accessed via APIs.

[1267] The server organizes the search results and creates a list of products that are most suitable for the user. This list is then presented to the user via their device. The user reviews the presented list of products, and if they want more detailed information, they can enter, for example, "Please tell me more about the camera performance of this smartphone." This input information is also sent from the device to the server, and the product model operates again to generate a detailed answer.

[1268] The system also provides detailed information and purchase assistance. When a user indicates their intention to purchase, the server provides a purchase procedure form via their terminal, and the user enters the necessary information. The server confirms the order information and sends the user an order confirmation email and after-sales information (such as instructions for use and warranty information). At this time, the server can also take into account the user's past purchase history and browsing history to make personalized product and service suggestions.

[1269] Examples of prompt statements

[1270] Below are some examples of prompt sentences.

[1271] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[1272] 2. Server: "What's your budget?"

[1273] 3. User: "It's under $500."

[1274] 4. Server: "Then, we recommend the following smartphones." (Shows search results)

[1275] 5. User: "Can you tell me more about the camera performance of this phone?"

[1276] 6. Server: "The camera performance of this smartphone is..." (provides more information)

[1277] In this way, the present invention significantly improves the purchasing experience on e-commerce sites by effectively uncovering users' hidden needs and quickly proposing optimal products and services.

[1278] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1279] Step 1:

[1280] A user accesses an EC site using a terminal and inputs request information such as "I want a new smartphone." The request information "I want a new smartphone" is obtained as input. This input data is sent from the terminal to the server.

[1281] Step 2:

[1282] The terminal immediately sends the user's input information to the server. Specifically, this input information is transferred to the server using a security protocol (e.g., HTTPS). As data processing, the user's request information is encoded and converted into a secure format. As output, the encoded request information is sent to the server.

[1283] Step 3:

[1284] The server calls a generative AI model (e.g., OpenAI's GPT-4) to analyze the received request information. It receives the encoded request information as input. The output obtained using the generative AI model generates questions to further collect the user's detailed request information (e.g., "What is the purpose?", "What features and price range do you want?").

[1285] Step 4:

[1286] The server sends an interface containing the generated question to the device. As input, it has the output of the generative AI model. As output, it sends the interface to the device.

[1287] Step 5:

[1288] The user answers additional questions through the chatbot interface. For example, they input detailed requirements such as "for gaming, high-performance camera, budget under 50,000 yen." The user's answer is received as input. This answer is then sent from the device to the server.

[1289] Step 6:

[1290] The terminal sends the user's answer back to the server. As input, there is the user's additional request information. As output, this is sent to the server.

[1291] Step 7:

[1292] The server uses a generative AI model to analyze the collected detailed user request information and extract the user's hidden needs. The detailed request information is given as input. The generative AI model processes the data by analyzing the request information and clarifying the hidden needs. The hidden needs are obtained as output.

[1293] Step 8:

[1294] The server searches the database based on the extracted hidden needs and identifies the most suitable products and services. The hidden needs are given as input. Specifically, it executes a search query against the e-commerce site's database using an API. The output is the search results.

[1295] Step 9:

[1296] The server generates an interface for presenting the search results to the user and sends it to the terminal. The search results are used as input, and the interface is sent to the terminal as output.

[1297] Step 10:

[1298] The user selects an item that interests them from the displayed items, and if they want more information, they input a specific question. For example, they might input, "Please tell me more about the camera performance of this smartphone." The user's specific question is obtained as input.

[1299] Step 11:

[1300] The terminal sends the user's specific question to the server. As input, there is the user's specific question. As output, this is sent to the server.

[1301] Step 12:

[1302] The server generates detailed information about the selected product using a generative AI model. The input is a specific question from the user. Data processing involves retrieving detailed product information from a database and organizing and generating it using the generative AI model. The output is detailed information.

[1303] Step 13:

[1304] The server sends the generated detailed information to the terminal. As input, there is detailed information. As output, the interface is sent to the terminal.

[1305] Step 14:

[1306] The user checks the detailed information and, if he / she wishes to purchase, inputs "I will purchase this smartphone." The user's intention to purchase is obtained as input.

[1307] Step 15:

[1308] The terminal transmits the user's purchase intention to the server. As input, there is the purchase intention. As output, this is transmitted to the server.

[1309] Step 16:

[1310] The server generates a purchase procedure form and provides it to the user. The input is the intention to purchase. The output is a purchase procedure form that is generated and sent to the terminal.

[1311] Step 17:

[1312] The user enters the required information into the form (shipping address, payment information, etc.) and presses the submit button. The required information is obtained as input.

[1313] Step 18:

[1314] The terminal sends the transmitted order information to the server. As input, there is the order information. As output, this is sent to the server.

[1315] Step 19:

[1316] The server verifies and confirms the order, sends an order confirmation email and follow-up information (such as instructions for use and warranty information) to the user, and collects user ratings and feedback. The input is order information. The output is a confirmation email and follow-up information sent to the user. A feedback form is also provided.

[1317] (Application example 1)

[1318] 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."

[1319] In conventional e-commerce systems, users often have difficulty identifying their specific needs or selecting the most suitable product from the vast number of options available. Furthermore, general search functions are unable to fully uncover users' hidden needs, making it difficult to recommend the products and services they truly desire. Furthermore, response times are long when users request detailed information, making it difficult to support a smooth purchasing process. To address these issues, a system is needed that provides more personalized recommendations tailored to user needs and a smooth dialogue interface.

[1320] 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.

[1321] In this invention, the server includes a means for receiving product and service request information from a user, a means for analyzing the request information and extracting the user's hidden needs, a means for searching for optimal products and services from a product database based on the hidden needs, a means for presenting the search results to the user, and a means for providing an interactive interface with the user using a generative AI model. This makes it possible to effectively uncover the user's hidden needs and quickly propose optimal products and services. Furthermore, it is possible to improve the user's experience and smoothly support the purchasing process.

[1322] "User" means any individual or entity that provides requested information regarding goods or services.

[1323] "Requirement information" refers to the specific specifications and desired conditions of the product or service that the user is looking for.

[1324] "Hidden needs" are latent requirements and expectations that users have not explicitly communicated but that become apparent through analysis.

[1325] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate dialogue with users. Specifically, it has the ability to generate natural language based on input data and respond to users.

[1326] A "conversational interface" is an interface that allows a user to interact with a system, allowing the user to enter questions and receive immediate answers.

[1327] A "product database" is a database that stores information about various products and services. Each product includes detailed specifications, prices, user reviews, and other information.

[1328] "Search means" refers to the functions and algorithms used to find the most suitable products and services based on the user's requirements from a product database.

[1329] A "presentation mechanism" is a method or system for presenting search results to a user visually or audibly.

[1330] The present invention relates to an e-commerce concierge system that collects request information from users, analyzes those requests to discover the users' hidden needs, and proposes optimal products and services. This system provides a chatbot interface using a generative AI model via devices such as smartphones and tablets, and grasps the users' needs through dialogue with them and proposes products. Details of embodiments of the present invention are described below.

[1331] First, a user accesses the system through a device such as a smartphone or tablet. A chatbot application using a generative AI model is installed on the device. This application provides an interface for receiving requested information from the user.

[1332] The server receives and analyzes the request information sent by the user. Specifically, it analyzes the request information using a generative AI model (e.g., GPT-4) to extract hidden needs that the user has not explicitly communicated. This analysis uses natural language processing technology to identify potential needs based on the user's input.

[1333] Next, the server searches the product database for the most suitable products and services based on the extracted needs. For example, if a user inputs "I want a new smartphone," and further answers "one that can play games, has a high-performance camera, and a budget of less than 50,000 yen," the server searches the database for products that meet these conditions. A database search algorithm is used for the search.

[1334] The search results are displayed on the screen of a smartphone or tablet. The user can review the displayed search results and select the product that interests them. If the user wants more detailed information, they can ask the chatbot an additional question. For example, they can enter a question such as, "Please tell me more about the camera performance of this smartphone." The server receives this request, generates detailed information about the selected product, and provides it to the user.

[1335] Once the user decides to purchase, the chatbot interface will help them through the purchase process smoothly. A purchase form will be generated and the order will be confirmed by entering the necessary information. After the purchase, after-sales information (e.g., how to use, warranty details, etc.) will also be provided.

[1336] Below are some examples and prompts:

[1337] Specific examples

[1338] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[1339] Terminal: Sends this information to the server.

[1340] Server: Uses generative AI to ask the user, "What is your budget?"

[1341] User: "It's under 50,000 yen."

[1342] Server: Searches for smartphones that meet the criteria and presents candidates to the user.

[1343] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[1344] Server: Generates detailed information about the camera capabilities of a specified smartphone and provides it to the user.

[1345] User: Makes the final decision: "I will buy this smartphone."

[1346] Server: Generates the checkout form and confirms the order information.

[1347] Prompt Sentence Examples

[1348] User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[1349] Prompt: "To find out what kind of smartphone you're looking for, ask yourself these questions: 1. What will you use it for? 2. What features are important to you? 3. What is your budget?"

[1350] This invention makes it possible to effectively uncover hidden user needs and quickly propose optimal products and services. It also provides a smooth interactive interface, significantly improving the user's purchasing experience.

[1351] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1352] Step 1:

[1353] A user launches a chatbot application on their smartphone or tablet and inputs information about a product or service they require. For example, they might input, "I want a new smartphone." This input information is sent from the device to the server. To process the input information, the device collects the user's text data and transfers it to the server.

[1354] Step 2:

[1355] The server analyzes the received request information using a generative AI model (e.g., GPT-4). Specifically, it performs natural language processing to analyze the user's request information and extract hidden needs. For example, from a request such as "I want a new smartphone," specific needs such as "for gaming, with a high-performance camera, and a budget of less than 50,000 yen" can be extracted. The server generates the analysis results and prepares for the next step.

[1356] Step 3:

[1357] The server searches the product database for the most suitable products and services based on the extracted hidden needs. This search uses a database search algorithm. For example, it finds smartphones from the database that meet the criteria of "for gaming," "high-performance camera," and "budget under 50,000 yen." The server generates data on the search results and prepares for the next step.

[1358] Step 4:

[1359] The server sends data to the terminal to present the search results to the user. Specifically, it generates an optimal product list to meet the user's request information and provides it to the terminal. The terminal receives this data and displays it to the user through the chatbot interface. The user can then check the presented product list.

[1360] Step 5:

[1361] The user selects a product of interest from the presented product list and requests further details. For example, they might type, "Tell me more about the camera performance of this smartphone." The device then sends this request to the server. The system accurately collects the user's input and processes it for transmission to the server.

[1362] Step 6:

[1363] In response to a user's request, the server provides detailed information about the selected product. Specifically, it uses a generative AI model to extract detailed information from a product database and generate text to answer the user's question. For example, it may provide detailed information about the specifications and features of a smartphone's camera performance. The server then sends the generated detailed information to the device.

[1364] Step 7:

[1365] The terminal receives the detailed information sent from the server and displays it to the user, who can then review the presented details and make a final purchasing decision. The detailed information is visually displayed via a chatbot interface.

[1366] Step 8:

[1367] If the user decides to make a purchase, the terminal generates a purchase procedure form and presents it to the user. The user enters the necessary purchase information and confirms the order. The purchase information is then accurately collected and sent to the server.

[1368] Step 9:

[1369] The server receives the purchase information sent by the user and confirms the order. Specifically, it checks the order details, processes the payment, and generates an order confirmation email to send to the user. It also provides after-sales follow-up information after the purchase and collects data to improve user satisfaction. It processes the user's purchase information and generates after-sales follow-up data.

[1370] Through the above steps, the EC concierge system of the present invention effectively uncovers the user's hidden needs and quickly suggests optimal products and services, thereby realizing a smooth purchasing experience.

[1371] 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.

[1372] This invention relates to an e-commerce concierge system that collects user request information, discovers the user's hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes the user's emotions, more personalized proposals can be made.

[1373] System Overview

[1374] The system consists of the following main components:

[1375] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[1376] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[1377] 3. Server: A computer system that receives information from users and uses a generative AI model to analyze and propose products.

[1378] 4. Emotion engine: An engine for recognizing user emotions and tailoring suggestions based on those emotions.

[1379] Program processing flow

[1380] Step 1: Gather user needs

[1381] A user accesses an e-commerce site via a device and inputs their request, such as "I want a new smartphone," into the chatbot interface. The device then sends this information to the server. The server then activates the chatbot interface, which uses generative AI, and asks the user additional questions (such as "purpose, desired features, price range, etc."). Detailed request information about the user is collected through the answers to these questions.

[1382] Step 2: Emotion recognition and needs analysis

[1383] The server analyzes the request information collected from the user using a generative AI and an emotion engine. The generative AI analyzes the request information and extracts the user's hidden needs. Meanwhile, the emotion engine recognizes emotions (e.g., excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation. Based on this information, the server can more accurately understand the user's needs.

[1384] Step 3: Find and adjust product offers

[1385] The server searches the e-commerce site's database for products that match the user's needs and the emotions recognized. It also adjusts its suggestions by taking into account the emotional information provided by the emotion engine. For example, if the user is unsure, it may emphasize reviews and warranty information. The adjusted search results are then presented to the user.

[1386] Step 4: Provide details and purchase assistance

[1387] When a user selects an item that interests them from the displayed items and requests more information about it, the device sends this request to the server. The server generates detailed information about the selected item and provides it to the user, taking into account the emotion engine data. For example, if the user is excited, the server adds an additional comment such as "This is a popular model."

[1388] Step 5: Purchase decision and follow-up

[1389] The user checks the product details and enters the final decision, saying, "I'll buy this smartphone." The device then sends the purchase decision to the server. The server then generates an order form, collects the necessary information, and confirms the order. It also uses generative AI to generate an order confirmation email and after-sales information (such as instructions for use and warranty information) and sends them to the user.

[1390] Specific examples

[1391] Below are some specific examples of how this system can be used.

[1392] 1. User: "I'm looking for a new smartphone. I want one that can play games and has a good camera."

[1393] 2. Device: Sends this information to the server.

[1394] 3. Server: Using generative AI, ask the user, "What's your budget?" The emotion engine analyzes the user's input and recognizes that the user is excited.

[1395] 4. User: "It's under 50,000 yen."

[1396] 5. Server: Based on the user's needs, the server searches the e-commerce site's database for smartphones that fit the criteria, and then adjusts the search results by taking into account the analysis results of the emotion engine. For example, the server may add a comment to the suggestion, such as "This smartphone is particularly good at gaming."

[1397] 6. User: Asks a specific question, such as "Tell me more about the camera performance of this phone." The emotion engine recognizes the user's interest.

[1398] 7. Server: Generates detailed information about the camera performance of the specified smartphone, and based on data from the emotion engine, provides information to further pique the user's interest, such as "This smartphone has camera performance that is well-received on social media."

[1399] 8. User: Makes the final decision: "I will buy this smartphone."

[1400] 9. Server: Generates an order form and confirms the order information. Based on the data from the emotion engine, the server sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this smartphone."

[1401] This invention takes into account not only the user's hidden needs but also their current emotions, enabling more personalized product suggestions and significantly improving the purchasing experience on e-commerce sites.

[1402] The processing flow will be explained below.

[1403] Step 1:

[1404] The user accesses the e-commerce site using a device and enters "I want a new smartphone" into the chatbot interface.

[1405] Step 2:

[1406] The device sends the user's request information to the server, which then launches a chatbot interface using generative AI to generate additional questions for the user, such as "purpose, desired features, price range, etc."

[1407] Step 3:

[1408] The user confirms the additional questions displayed on the device and answers, "I need a high-performance camera for gaming, and my budget is less than 50,000 yen."

[1409] Step 4:

[1410] The device sends the user's response to the server. The emotion engine analyzes the user's response and recognizes, for example, that the user is "excited."

[1411] Step 5:

[1412] The server uses generative AI to analyze the user's detailed needs, extracting specific needs such as "gaming use," "high-performance camera," and "budget under 50,000 yen" as a result of the analysis.

[1413] Step 6:

[1414] The server takes into account the analysis results of the emotion engine and adjusts the search results based on the user's excitement level, for example generating a comment such as "A smartphone that offers an exciting gaming experience."

[1415] Step 7:

[1416] The server searches the e-commerce site's database for products that match the criteria, and uses generative AI to generate a list of optimal products, sending it to the device along with a message suggesting the product to the user. The device then displays this list to the user.

[1417] Step 8:

[1418] The user selects one of the presented smartphones and enters a follow-up question: "What are the camera specs of this smartphone?" The device then sends this request to the server.

[1419] Step 9:

[1420] The server retrieves detailed information about the selected smartphone from a database and uses an emotion engine to process the information to further enhance the user's excitement. For example, it might add a comment such as, "This smartphone's camera function is a hot topic on social media."

[1421] Step 10:

[1422] The server sends the generated details to the terminal, which displays them to the user.

[1423] Step 11:

[1424] The user confirms the details and enters the final decision, saying, "I will purchase this smartphone." The device then sends this purchase decision information to the server.

[1425] Step 12:

[1426] The server generates a purchase procedure form and sends it to the terminal to collect the necessary information (credit card information, shipping address information, etc.). The terminal displays this form to the user, who then enters the necessary information.

[1427] Step 13:

[1428] The terminal sends the entered purchase information to the server, which then verifies the order and confirms the order.

[1429] Step 14:

[1430] The server generates an order confirmation email and uses the emotion engine to send it to the user with a positive message, such as "Thank you for your purchase! Many users are happy with this smartphone."

[1431] Step 15:

[1432] The user receives the smartphone they purchased, sets it up using the after-sales service information, and enters their satisfaction and reviews.

[1433] Step 16:

[1434] The device sends user review information to a server, which stores this information in a database and uses it for future marketing and product improvements. The server also generates targeted advertisements based on the collected data and delivers them to users at the appropriate time.

[1435] Example 2

[1436] 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."

[1437] Conventional product recommendation systems for e-commerce sites have difficulty making appropriate recommendations based on user requirements. In particular, it is difficult to make product recommendations that take user emotions into consideration, resulting in problems that do not improve the user experience. Furthermore, since it is not possible to accurately grasp users' hidden needs, there is a risk of user satisfaction decreasing.

[1438] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1439] In this invention, the server includes means for receiving product and service request information from a user, means for analyzing the request information and extracting the user's hidden needs, means for recognizing the user's emotions and adjusting the request information based on the emotions, means for searching for optimal products and services from a product database based on the hidden needs and emotion information, and means for presenting the search results to the user, thereby enabling personalized product suggestions that take the user's hidden needs and emotions into consideration.

[1440] "User" means an individual or corporation that uses the EC site to provide information about products and services.

[1441] "Request information" is detailed information about the product or service desired by the user, including, for example, the purpose, desired functions, price range, and the like.

[1442] "Analysis" is the process in which the server uses the generated AI model to analyze the user's request information and extract the user's hidden needs.

[1443] "Hidden needs" are potential requirements or desires that are important to users but are not explicitly requested by the user, and are extracted by the generative AI model.

[1444] "Emotion recognition" means that the emotion engine analyzes and recognizes emotions (excitement, doubt, anxiety, etc.) from the user's input and the tone of the conversation.

[1445] "Adjusting request information based on emotions" means adjusting the product proposal method and expression according to the recognized user emotions.

[1446] A "product database" is a database that stores information about various products and services offered by an e-commerce site.

[1447] "Searching for products and services" means finding the most suitable products and services from a product database based on the user's hidden needs and emotional information.

[1448] "Detailed information" refers to specific product information that interests users, such as product specifications, features, reviews, and warranty information.

[1449] The "additional questions" are questions that are asked to dig deeper into the requested information that the user provided initially.

[1450] "Adjustment" refers to optimizing the content of product suggestions and detailed information presented based on the user's emotional data.

[1451] "Past purchase history and browsing history" refers to data on purchases and browsing that a user has made on an e-commerce site.

[1452] "Recommending personalized products and services" means individually proposing optimal products and services by taking into account a user's past purchasing history, browsing history, hidden needs, and emotional information.

[1453] MODE FOR CARRYING OUT THE INVENTION

[1454] This invention relates to an e-commerce concierge system that collects user request information, discovers hidden needs, and proposes optimal products and services. In particular, by combining it with an emotion engine that recognizes user emotions, more personalized proposals can be made.

[1455] The system consists of the following main components:

[1456] 1. User: An individual or corporation that uses the e-commerce site and provides information about products and services.

[1457] 2. Terminal: A device used by a user (smartphone, PC, tablet, etc.) that communicates between the user and the server.

[1458] 3. Server: A computer system that receives information from users, analyzes it using a generative AI model and emotion engine, and makes product suggestions.

[1459] Specifically, a user accesses an e-commerce site through a device and inputs request information, such as "I want a new smartphone," into the chatbot interface. This information is sent to the server via the device. The server uses a generative AI model to generate follow-up questions for the user, and presents prompts to the user, such as "What is the purpose, desired features, and price range?" Examples of such prompts are as follows:

[1460] What is your budget?

[1461] If the user responds with, "I want a smartphone that can play games and has a high-performance camera," the device sends this information back to the server. The server then uses a generative AI model and emotion engine to analyze the collected request information and extract the user's hidden needs. For example, it might discover that the user is looking for a smartphone specialized for gaming.

[1462] Furthermore, the emotion engine recognizes emotions from the user's responses. For example, if the server senses that the user is excited, it adjusts its suggestions based on this emotional information. Specifically, it searches the e-commerce site's database for products that meet the user's needs and adjusts the suggestions based on the emotion engine's information. For example, it adds a comment such as, "This smartphone is particularly good at games."

[1463] When a user selects a product that interests them from the suggested items and requests more information, the device sends this request to the server. The server uses a generative AI model to generate detailed product information and provides it to the user, taking into account data from the emotion engine. For example, if the user is excited, the server adds an additional comment such as, "This smartphone has a camera that is highly acclaimed on social media."

[1464] Once the user finally decides to purchase, the device sends this information to the server. The server generates an order form, collects the necessary information, and then confirms the order. The server also uses a generative AI model to generate an order confirmation email and follow-up information, which are then sent to the user. For example, the email may contain a positive message such as, "Thank you for your purchase! Many users are delighted with this smartphone."

[1465] This system enables personalized suggestions that take into account users' hidden needs and emotions, dramatically improving the shopping experience on e-commerce sites.

[1466] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1467] Step 1:

[1468] User: Accesses the e-commerce site using a device and inputs "I want a new smartphone" into the chatbot interface. This becomes the input information and is treated as the user's initial request information.

[1469] Terminal: Sends user input information to the server. The input here is the user's request information, and the output is the request data sent to the server.

[1470] Step 2:

[1471] Server: Launches the generative AI model and generates follow-up questions based on the user's initial request information. Specifically, the generative AI model inputs prompts such as "What are the intended use, desired features, and price range?" to create optimal follow-up questions for the user. The input is the user's request information, and the output is the follow-up questions.

[1472] Server: Sends the generated follow-up questions to the terminal. The input here is the follow-up questions, and the output is sent to the terminal.

[1473] Terminal: Display a follow-up question to the user.

[1474] Step 3:

[1475] User: In response to the follow-up question, answers, "I want something that can play games and has a good camera." This becomes new input information.

[1476] Terminal: Sends the user's answer to the server. The input is the user's answer and the output is what is sent to the server.

[1477] Step 4:

[1478] Server: Analyzes the user's responses using a generative AI model and emotion engine. The generative AI model extracts hidden needs, such as the specific features the user desires (game performance, camera performance). The emotion engine identifies the user's emotional state (excitement, etc.) from the user's responses. The input is the user's detailed request information, and the output is hidden needs and emotion information.

[1479] Step 5:

[1480] Server: Searches for the optimal smartphone from a product database based on the user's hidden needs and emotional information. Specifically, it extracts products from the database that meet hidden needs (gaming performance, camera performance), and adjusts the suggestions based on the emotional information. For example, for a user who is feeling excited, it emphasizes "the latest gaming-specific model." The input is the hidden needs and emotional information, and the output is a list of adjusted products.

[1481] Server: Sends the adjusted search results to the device.

[1482] Step 6:

[1483] Device: Presents search results to the user. The input here is a tailored list of products, and the output is a display to the user, such as "This phone is great for gaming and has a great camera."

[1484] Step 7:

[1485] User: Selects a suggested product that interests them and requests more information about it. For example, they might type, "Tell me more about the camera performance of this phone."

[1486] Terminal: Sends this request to the server. The input is the user's request for more information, and the output is the transmission to the server.

[1487] Step 8:

[1488] Server: Generates detailed information about the specified product based on the user's request. Using a generative AI model, it creates detailed information such as product specifications, features, and reviews, and adjusts the information taking into account data from the emotion engine. For example, if the user is excited, it adds an additional comment such as "This smartphone has a camera performance that is highly rated on social media." The input is the user's detailed request information, and the output is detailed information.

[1489] Server: Sends detailed information to the device.

[1490] Step 9:

[1491] Terminal: Presents detailed information to the user. The input is the adjusted detailed information, and the output is what is displayed to the user.

[1492] Step 10:

[1493] User: Checks the product details and enters the final decision, "I'll buy this smartphone."

[1494] Terminal: Sends purchase decision information to the server. The input is the user's purchase decision information, and the output is the transmission to the server.

[1495] Step 11:

[1496] Server: Generates the checkout form and collects the necessary information. The input is the user's purchasing decision information, and the output is the checkout form.

[1497] Server: Confirms the order. Additionally, the generative AI model is used to create an order confirmation email and after-sales information, and an emotional engine is used to add a positive message. For example, "Thank you for your purchase! Many users are pleased with this smartphone." The input is the purchase decision information and the content generated by the generative AI model, and the output is a confirmation email.

[1498] Step 12:

[1499] Server: Sends an order confirmation email to the terminal and provides it to the user. The input is the confirmation email and the output is what is sent to the user.

[1500] (Application example 2)

[1501] 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."

[1502] Conventional e-commerce sites typically use systems that suggest products and services based on user requirements, but it has been difficult to provide personalized suggestions that take into account the user's hidden needs and emotions. As a result, users have difficulty finding the products and services they truly want, resulting in a poor quality shopping experience. The present invention aims to solve these problems by comprehensively analyzing users' emotions and requirements, discovering their hidden needs, and suggesting optimal products and services.

[1503] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving request information regarding products and services from a user, means for analyzing the request information and analyzing the user's hidden needs and emotions, means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions, and means for presenting the search results to the user and providing supplemental information according to the emotions. This makes it possible to make personalized product suggestions that take into account the user's hidden needs and emotions.

[1504] The "means for receiving requested information about products and services from users" refers to an interface and communication means for collecting the user's input desires and needs for products and services.

[1505] The "means for analyzing the request information and extracting the user's hidden needs" refers to an algorithm that uses a generative AI model to derive the user's latent requests and needs based on information explicitly provided by the user.

[1506] The "means for analyzing the hidden needs and the user's emotions" is an engine that has the function of complementing the user's hidden needs with an emotion engine and analyzing the user's emotional state (e.g., excitement, anxiety, joy, etc.).

[1507] The "means for searching for optimal products and services from a product database based on the hidden needs and recognized emotions" refers to an algorithm and search engine that selects the most suitable products and services from a database based on the user's needs and emotional information.

[1508] "Means for presenting search results to the user and providing supplementary information according to their emotions" refers to an interface that displays information about the searched products or services to the user and has the function of adding additional explanations or related information according to the user's emotional state.

[1509] The "means for adjusting the content of the proposal based on the emotion" is an algorithm that dynamically changes the content of the proposed products and services in response to the user's emotion recognized by the emotion engine.

[1510] "Means for recommending personalized products and services by taking into account a user's past purchasing history and browsing history" refers to an algorithm that retrieves a user's past purchasing history and browsing history from a database and recommends personalized products and services accordingly.

[1511] The "means for optimizing product proposals using the emotion data" is an algorithm for optimizing the selection and presentation method of products to be proposed using emotion data obtained from the emotion engine.

[1512] This invention provides an e-commerce system that proposes optimal products and services based on user requirements and emotional data. The system consists of the following main components:

[1513] A means of receiving product and service request information from users

[1514] A means for analyzing the request information and analyzing the user's hidden needs and feelings.

[1515] A means for searching for the most suitable products and services from a product database based on said hidden needs and recognized emotions.

[1516] A means of presenting search results to users and providing supplementary information according to their emotions

[1517] means for adjusting the content of the proposal based on said emotion;

[1518] A means of recommending personalized products and services that take into account a user's past purchasing and browsing history

[1519] means for optimizing product recommendations using said emotion data;

[1520] Natural language description of the program:

[1521] 1. User Interface:

[1522] The server provides an interface for users to input their request information about products and services. Users access the chatbot interface via their smartphone or PC and input their request information, such as "I want a new smartphone." This operation causes the device to send the request information to the server.

[1523] 2. Requirements information collection and analysis:

[1524] Once the server receives the request information, it analyzes it using a generative AI model. Through this analysis, it extracts the user's hidden needs. The emotion engine also analyzes the user's input content and tone for emotional data (e.g., excitement, anxiety, joy, etc.). As a result of this analysis, the user's specific needs and emotional state are revealed.

[1525] 3. Product search and sentiment-based tailoring:

[1526] The server searches for the most suitable products and services from a product database based on the hidden needs and emotion data. The emotion engine adjusts the content of the suggestions based on the user's emotions. For example, if the user is feeling anxious, the server may highlight the reliability of the product and review information.

[1527] 4. Presenting search results and providing supplementary information:

[1528] The server presents the user with search results for the most suitable products. If the user is interested, it provides further details. At this time, it supports the user's purchasing decision by displaying supplemental information according to the user's emotions (e.g., "This smartphone is very popular").

[1529] 5. Personalization based on past history:

[1530] The server takes into account the user's past purchasing and browsing history and uses it to recommend personalized products and services. Using emotion data, the recommendations can be further personalized, increasing user satisfaction.

[1531] Examples:

[1532] Below are some concrete examples of how the system can be used:

[1533] User: "I want a new smartphone that can play games and has a good camera," types the chatbot.

[1534] Server: Uses a generative AI model to ask the user, "What's your budget?" The emotion engine recognizes the user's excitement.

[1535] User: "Under $50,000."

[1536] Server: Searches for products from the database based on the user's needs and adjusts the search results taking into account the analysis results of the sentiment engine. For example, it adds comments to the suggestions, such as "This phone is particularly good at gaming."

[1537] User: Ask a specific question like, "Tell me more about the camera performance of this phone."

[1538] Server: Generates detailed information about the camera performance of the specified smartphone and provides information to further pique the user's interest, such as "This smartphone has a camera performance that is well-received on social media."

[1539] User: Makes the final decision to "purchase this smartphone."

[1540] Server: Generates the order checkout form and sends an order confirmation email with a positive message such as "Thank you for your purchase! Many users are happy with this phone."

[1541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1542] Step 1:

[1543] The user inputs their request information about a product or service through the chatbot interface. For example, the input information might be something like, "I want a new smartphone. I want one that can play games and has a high-performance camera." The device then sends this information to the server. In this process, the user's request information becomes input data, and this data is sent to the server.

[1544] Step 2:

[1545] The request information received by the server is analyzed using a generative AI model. Specifically, the chatbot generates a follow-up question, such as "What is your budget?", and sends it to the user. The user responds, for example, "Less than 50,000 yen." In this step, the user's specific request information is collected and stored in a database.

[1546] Step 3:

[1547] The server uses an emotion engine to analyze emotion data from the user's input. Here, emotions (excitement, doubt, anxiety, etc.) are recognized from the content of the user's input (e.g., "It's under 50,000 yen") and the tone of the input. The analysis result detects that the user is excited. In this step, the user's text is used as input data, and the user's emotional state is obtained as output data.

[1548] Step 4:

[1549] The server searches the product database for the most suitable products and services based on the request information and emotion data mentioned above. The search results are then reflected in the emotion engine's analysis results, and a comment such as "This smartphone is particularly good at games" is added. The user's request information, emotion data, and database are used as input, and the search results with comments corresponding to the emotion are obtained as output.

[1550] Step 5:

[1551] The server presents the search results to the user. If the user is interested, they may ask a specific question, such as, "Tell me more about the camera performance of this smartphone." The server receives this request and provides detailed information. For example, it may present the result with additional information, such as, "This smartphone has a camera performance that is highly rated on social media." In this step, the additional question from the user becomes input data, and the detailed information based on that question becomes output data.

[1552] Step 6:

[1553] The user finally decides to purchase and enters "I will buy this smartphone." The device sends the purchase decision information to the server. The server generates an order procedure form, collects the necessary information, and then confirms the order. It also sends an order confirmation email with a positive message based on the emotion engine data, such as "Thank you for your purchase! Many users are pleased with this smartphone." In this step, the user's purchase decision information is the input data, and the completion of the order procedure and the confirmation email are the output data.

[1554] Step 7:

[1555] The server takes into account the user's past purchasing and browsing history to optimize future personalized product suggestions. In this step, past purchasing history and emotion data are used as inputs, and personalized suggestion information is the output data.

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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).

[1563] 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.

[1564] 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."

[1565] 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.

[1566] 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).

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] The following is further disclosed regarding the above embodiment.

[1578] (Claim 1)

[1579] means for receiving product or service request information from a user;

[1580] means for analyzing the request information and extracting hidden needs of users;

[1581] A means for searching for optimal products and services from a product database based on the hidden needs;

[1582] means for presenting search results to a user;

[1583] A system including:

[1584] (Claim 2)

[1585] The system according to claim 1, further comprising means for providing detailed information about the products or services in the search results, the detailed information being generated in response to a follow-up question from the user.

[1586] (Claim 3)

[1587] 10. The system of claim 1, further comprising means for recommending personalized products and services taking into account a user's past purchasing and browsing history. 【...

Claims

1. means for receiving product or service request information from a user; means for analyzing the request information and extracting hidden needs of users; A means for searching for optimal products and services from a product database based on the hidden needs; means for presenting search results to a user; A system including:

2. The system according to claim 1 , further comprising means for providing detailed information about the searched product or service, the detailed information being generated in response to a follow-up question from the user.

3. The system of claim 1 , further comprising means for recommending personalized products and services taking into account a user's past purchasing and browsing history.

4. means for receiving purchase decision information from the user and completing the order process; As a means of providing after-sales support after purchase, we provide information on how to use the product and warranty information. A means of collecting user ratings and feedback; The system of claim 1 further comprising:

5. A means of providing product manufacturers or service providers with the insights they need for marketing and planning based on collected user data; The system of claim 1 further comprising means for generating and delivering targeted advertisements.

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