System

Abstract

A system is provided.SOLUTION: Means for allowing a seller to input detailed information of a product or service or upload a document, means for allowing a buyer to input requirements of a desired product or service or upload a document, and a natural language processing engine to analyze the uploaded information; A system, comprising: means for extracting product characteristics of sellers and requirements of buyers; means for applying a AI matching algorithm to calculate matching degrees based on the extracted characteristics and requirements; means for listing optimal matches of sellers and buyers based on the calculated matching degrees; means for notifying sellers and buyers of recommendation results; and means for collecting evaluation information from sellers and buyers and updating reputation scores.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] In conventional e-commerce systems, matching is based solely on price, and other important factors such as performance, quality, and delivery time are not fully considered, making it difficult for sellers and buyers to find the optimal trading partner. This can lead to a decline in transaction quality and satisfaction, making it difficult to achieve efficient and effective commerce. [Means for solving the problem]

[0005] The present invention provides a means for sellers to input detailed product or service information or upload documents, and a means for buyers to input requirements for desired products or services or upload documents. It also includes a means for analyzing the uploaded information using a natural language processing engine and extracting the seller's product characteristics and buyer requirements. It also provides a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements, and a means for listing optimal matches between sellers and buyers based on the calculated degree of match. Additionally, the system includes a means for notifying sellers and buyers of the recommended results, and a means for collecting evaluation information from sellers and buyers and updating reputation scores, thereby achieving optimal matching that takes into account multiple factors other than price.

[0006] A "seller" is a user who provides a product or service.

[0007] A "buyer" is a user who wants a product or service.

[0008] "Detailed information" refers to data such as specifications, manuals, brochures, and price lists about products and services.

[0009] "Document" refers to information represented in a digital file, such as text or PDF format.

[0010] "Upload" is the act of a user sending data from their own terminal to a server.

[0011] A "natural language processing engine" refers to an algorithm that analyzes input text data and extracts meaning and structure.

[0012] "Product characteristics" are specific elements of a product or service, such as performance, quality, price, and delivery time.

[0013] "Requirements" are the specific conditions and desires of the buyer for the product or service they desire.

[0014] The "AI matching algorithm" is an algorithm that calculates the degree of match between sellers and buyers based on extracted characteristics and requirements.

[0015] "Match degree" is an index that indicates how closely the seller's product characteristics match the buyer's requirements.

[0016] "Listing" is the act of displaying a list of recommended sellers or buyers based on the calculated degree of match.

[0017] "Recommended results" are specific suggestions made when notifying the matching results between sellers and buyers.

[0018] "Notification" is the act of the system sending information to the user to inform them.

[0019] "Evaluation information" is data on the evaluation a user gives to the other party after a transaction.

[0020] The "reputation score" is a numerical value that indicates the reliability or evaluation of a user, calculated based on the evaluation information. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[0043] Input and upload

[0044] Seller Input and Upload

[0045] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. Here, the seller can easily enter detailed information about their products and services. Specifically, the system provides the ability to upload documents such as product specifications, manuals, brochures, and price lists. There is also a form for manually entering detailed information.

[0046] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. No analysis of the information is performed at this stage.

[0047] Buyer Input and Upload

[0048] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[0049] Server: The entered requirements and uploaded documents are also received by the server and stored in a database.

[0050] Data analysis and matching

[0051] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[0052] Server: Next, an AI matching algorithm is applied. Based on the extracted characteristics and requirements, the degree of match between the seller and the buyer is calculated. The AI ​​matching algorithm takes into account multiple factors (performance, quality, price, delivery time, etc.) to calculate the overall degree of match.

[0053] Recommendations and Notifications

[0054] Server: Based on the calculated match score, a list of the best sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notification.

[0055] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[0056] Ratings and Feedback

[0057] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of each other into the system.

[0058] Server: The server receives the entered rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm, resulting in more accurate matching.

[0059] Specific examples

[0060] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system, which stores detailed information about the product's performance and quality in a database.

[0061] Example 2 (Buyer): Manufacturer B enters requirements such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database.

[0062] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that Manufacturer A's electronic components are the best fit for Manufacturer B's requirements.

[0063] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Manufacturer B receives the notification, checks the details, and starts the transaction.

[0064] This embodiment realizes optimal matching that takes into account the multifaceted needs of sellers and buyers.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, price lists, etc., or manually enter the details.

[0068] Step 2:

[0069] Server: Receives uploaded documents and manually entered data and stores it in a database.

[0070] Step 3:

[0071] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[0072] Step 4:

[0073] Server: Receives input requirements and uploaded documents and stores them in a database.

[0074] Step 5:

[0075] Server: Analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. Extracts characteristics such as performance, quality, price, and delivery time from the seller's product information, and extracts the required performance, quality, price, delivery time, and other conditions from the buyer's requirements information.

[0076] Step 6:

[0077] Server: Based on the extracted characteristics and conditions, an AI matching algorithm is applied, taking into account various factors such as performance, quality, price, and delivery time to calculate the degree of match between sellers and buyers.

[0078] Step 7:

[0079] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[0080] Step 8:

[0081] User (Buyer): After receiving the notification, the user logs in to the system to check the recommended seller's information and decides whether to proceed with the transaction based on the recommendation.

[0082] Step 9:

[0083] User (seller / buyer): After the transaction is completed, the user enters their evaluation of the other party into the system.

[0084] Step 10:

[0085] Server: Receives the entered rating information and updates each user's reputation score. This is reflected in the next matching algorithm, achieving even more accurate matching.

[0086] Through these steps, the system achieves optimal matching that takes into account the multifaceted needs of sellers and buyers.

[0087] Example 1

[0088] 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."

[0089] In e-commerce, it has been difficult for sellers and buyers to find the best partner to meet their respective needs using conventional methods. In particular, it has been difficult to achieve effective matching because detailed product or service information and requirements cannot be accurately communicated, and evaluation information from both parties cannot be reflected in the transaction. This has led to issues such as reduced transaction efficiency and a loss of satisfaction for both sellers and buyers.

[0090] 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.

[0091] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for saving the uploaded information in a database; a means for analyzing the saved information with a natural language processing engine and extracting product characteristics and buyer requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for generating a list of sellers optimal for the buyer based on the calculated degree of match; a means for notifying the buyer of the generated list by email or via an app notification function; and a means for collecting evaluation information from sellers and buyers and updating reputation scores. This enables optimal matching based on the needs of sellers and buyers, enabling efficient and satisfying transactions.

[0092] "Seller" refers to a company or individual that offers a product or service.

[0093] "Buyer" refers to a company or individual who wants a product or service.

[0094] "Input" refers to the act of manually registering information into the system.

[0095] "Upload" refers to the act of transferring a data file, such as a document, to the system.

[0096] A "means" refers to a method or device used to achieve a particular purpose.

[0097] A "natural language processing engine" refers to software that analyzes natural language, understands its meaning, and extracts the necessary information.

[0098] "Product characteristics" refers to attributes that indicate the product's performance, quality, specifications, etc.

[0099] "Buyer requirements" refers to the specific conditions and wishes of the buyer regarding the product or service they desire.

[0100] "AI matching algorithm" refers to an algorithm that uses artificial intelligence to calculate the optimal combination between sellers and buyers.

[0101] "Match" refers to an indicator of how well the products or services offered by the seller meet the buyer's requirements.

[0102] "Database" refers to a system in which information is organized and stored so that it can be searched and accessed as needed.

[0103] A "list" refers to a collection of items enumerated chronologically or sequentially.

[0104] "Email" refers to a means of sending and receiving text messages and files over the Internet.

[0105] "App notification function" refers to the function that allows an application to provide information to the user in real time.

[0106] "Evaluation information" refers to evaluation data that a seller or buyer makes of the other party after a transaction is completed.

[0107] "Reputation score" refers to a numerical value that indicates the trustworthiness and reliability of a user, calculated based on evaluation information.

[0108] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[0109] Hardware and software used

[0110] Server: The central part of the system, containing the database management system (DBMS), natural language processing engine (NLP engine), and computing resources to run the AI ​​matching algorithm.

[0111] Terminal: Provides an interface for sellers and buyers to input or upload information. Specifically, a PC, tablet, smartphone, etc. is used.

[0112] System Structure

[0113] 1. User Authentication

[0114] Users (sellers and buyers) log in to the system and enter their ID and password. The server queries the database for this information and performs authentication. If authentication is successful, the user is redirected to the trading dashboard.

[0115] 2. Seller's product information input and upload

[0116] The seller accesses the "Product / Service Registration" screen through the terminal and enters detailed product or service information or uploads a document. The terminal sends the entered information to the server, which stores this data in a database.

[0117] 3. Buyer's requirements input and upload

[0118] Buyers access the "Requirements Registration" screen through their terminal and enter their requirements for the product or service they are looking for, or upload a document summarizing their requirements. The terminal then sends the entered information to the server, which then stores this data in a database.

[0119] 4. Data Analysis

[0120] The server analyzes the stored information using a natural language processing engine (NLP engine), extracting elements such as product characteristics, quality, and delivery dates from the document content and re-saving them as structured data.

[0121] 5. Applying the Matching Algorithm

[0122] The server applies an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements, comprehensively evaluating multiple factors (performance, quality, price, delivery time, etc.) to determine suitability.

[0123] 6. Generation and notification of recommendation results

[0124] The server generates a list of optimal sellers based on the calculated match score, and the generated list is notified to the buyer via email or the app's notification function.

[0125] 7. Ratings and Feedback

[0126] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The server collects this rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm.

[0127] Specific examples

[0128] Example 1 (Seller):

[0129] Manufacturers upload specifications and brochures for newly developed electronic components to the system, which then stores detailed product performance and quality information in a database.

[0130] Example 2 (Buyer):

[0131] A manufacturer enters requirements for durable, heat-resistant electronic components that can be delivered within 30 days. The requirements are entered in natural language and stored in a database.

[0132] Example 3 (matching):

[0133] The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[0134] Example 4 (notification):

[0135] The server sends a notification to the manufacturer recommending the manufacturer's electronic parts. The manufacturer receives the notification, checks the details, and starts the transaction.

[0136] Prompt Sentence Examples

[0137] Example of prompt for manufacturer upload:

[0138] "Enter the details of your newly developed electronic components into the system. Upload specifications, manuals, brochures, and price lists."

[0139] Example prompt for manufacturer to enter requirements:

[0140] "Please enter your requirements for the electronic components you are looking for. Please be specific, such as high durability, heat resistance, delivery within 30 days, etc."

[0141] In this way, the system accurately grasps the needs of sellers and buyers and achieves optimal matching, thereby enabling effective and efficient e-commerce.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: User authentication

[0144] The user accesses the system and enters his or her ID and password.

[0145] The server performs authentication by querying the received ID and password against a database. The input is the user's ID and password, and the output is the success or failure of the authentication. If the authentication is successful, the server starts a session and redirects the user to the trading dashboard.

[0146] Step 2: Enter and upload seller's product information

[0147] The user (seller) uses a terminal to access the "Product / Service Registration" screen and manually enters detailed product or service information (e.g., specifications and price lists) or uploads a document file.

[0148] The terminal sends the information entered or uploaded by the seller to the server. The input is product information or document files, and the output is stored in a database.

[0149] The server stores the received data in a database. No analysis of the information is performed at this stage.

[0150] Step 3: Enter and upload buyer requirements

[0151] The user (buyer) uses a terminal to access the "Requirements Registration" screen and inputs the requirements for the product or service he or she desires, or uploads a document summarizing the requirements.

[0152] The terminal sends the information entered or uploaded by the buyer to the server. The input is requirement information or document files, and the output is saved in a database.

[0153] The server stores the received data in a database.

[0154] Step 4: Data analysis

[0155] The server analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. The input is the stored product information and requirement information, and the output is the analyzed structured data.

[0156] The server extracts elements such as product characteristics, quality, and delivery date from the document contents and re-stores them as structured data.

[0157] Step 5: Applying the matching algorithm

[0158] The server runs an AI matching algorithm based on the characteristics and requirements extracted by the NLP engine. The input is the extracted characteristics and requirements, and the output is the calculated match score.

[0159] The server comprehensively evaluates multiple factors (performance, quality, price, delivery time, etc.) and calculates the degree of match between the seller and the buyer.

[0160] Step 6: Generate and communicate recommendations

[0161] The server generates a list of optimal sellers based on the calculated match scores. The input is the match score evaluation result, and the output is a recommended list of sellers.

[0162] The server notifies the buyer of the generated listing via email or the app's notification function.

[0163] Step 7: Review the recommendations and trade

[0164] The user (buyer) receives the notification and logs into the system to check the information of the recommended seller. The input is the notified seller information, and the output is the decision to start a transaction.

[0165] The terminal displays an interface for the buyer to check the recommendation results.

[0166] Step 8: Rating and Feedback

[0167] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The input is rating information, and the output is an update of the reputation score.

[0168] The server stores the rating information in a database and updates each user's reputation score, which is then reflected in the next matching algorithm.

[0169] (Application example 1)

[0170] 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."

[0171] In conventional e-commerce systems, matching between sellers and buyers was not fully optimized, making it difficult for buyers to quickly and accurately find the products they wanted. Furthermore, notifying buyers of matching results and reflecting their ratings after a transaction often took time, leaving room for improvement in the user experience.

[0172] 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.

[0173] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for listing optimal matches between sellers and buyers based on the calculated degree of match; a means for notifying the seller and buyer of the recommended results; a means for collecting evaluation information from sellers and buyers and updating reputation scores; and a means for an application installed on a smartphone to register product information and input requirements for a desired product using AI to recommend optimal products. This allows for quick and accurate matching between sellers and buyers, and reflects evaluation information after a transaction in the next match, improving the user experience.

[0174] "Sellers" are users who register their details in the system to offer products or services.

[0175] A "Buyer" is a user who inputs or uploads their requirements for the product or service they want into the system and searches for the most suitable product or service.

[0176] "Detailed product or service information" refers to information that indicates the characteristics of the products or services provided by the seller, such as product specifications, manuals, brochures, and price lists.

[0177] "Requirements" are information that indicates the specific conditions and specifications for the product or service that a buyer desires.

[0178] A "natural language processing engine" is a program that analyzes input documents or text and extracts characteristics and requirements from its content.

[0179] An "AI matching algorithm" is a calculation method that calculates the optimal match between sellers and buyers based on extracted characteristics and requirements.

[0180] "Match degree" is an evaluation index that indicates how well the seller's product characteristics match the buyer's requirements.

[0181] "Notification method" refers to the method used to notify sellers and buyers of matching results, and typically functions as an email or app notification.

[0182] A "reputation score" is a number that indicates each user's trustworthiness and transaction history, updated based on evaluation information collected from sellers and buyers.

[0183] A "smartphone application" is a software application that is installed on a smartphone and allows users to register products and enter requirements.

[0184] This invention describes a system that utilizes an AI matching algorithm that uses information on sellers and buyers to provide optimal products and services. The following is a specific embodiment of this system.

[0185] System Configuration

[0186] The system mainly consists of the following elements:

[0187] 1. A way for sellers to enter product or service details or upload documents

[0188] Sellers use a smartphone application to input or upload product or service specifications, manuals, brochures, price lists, etc. This data is sent to a server and stored in a database.

[0189] 2. A way for buyers to input or upload documents regarding their desired product or service requirements

[0190] Similarly, buyers use a smartphone application to input or upload detailed requirements for the products or services they want, which is also sent to the server and stored in a database.

[0191] 3. Means of analyzing information

[0192] The server analyzes the uploaded information using a natural language processing engine (e.g., NLTK library) to extract the seller's product characteristics and the buyer's requirements. NLTK is used for tokenizing and analyzing text.

[0193] 4. AI Matching Algorithm

[0194] Based on the extracted features and requirements, the server applies an AI matching algorithm to calculate the match degree, which uses the Scikit-learn library to calculate the TF-IDF vectorizer and cosine similarity.

[0195] 5. Notification of recommended results

[0196] Based on the calculated match score, the server lists the best matches between sellers and buyers and notifies the buyer of the recommended results, usually via email or app push notification.

[0197] 6. How reputation information is collected and reputation scores are updated

[0198] The server collects the rating information entered by the seller and buyer after the transaction, and updates each user's reputation score, which is reflected in the next matching algorithm.

[0199] explanation

[0200] The server uses a web framework based on Flask and performs a series of processes, from receiving information to storing, analyzing, and notifying users. Buyers and sellers input and upload information through a smartphone application, enabling fast and accurate matching.

[0201] Specific examples

[0202] Sellers upload detailed information about durable electronic components via a smartphone application. Buyers input requirements such as, "I'm looking for durable electronic components that can be delivered within 30 days." The server analyzes this information and calculates the cosine similarity based on the characteristics and requirements. As a result, it notifies the buyer of products with a high degree of match.

[0203] Prompt Sentence Examples

[0204] Below are some example prompts to input to the AI ​​model:

[0205] "Please enter your ruggedized electronics details."

[0206] Using these prompts, a generative AI model helps users enter the appropriate product information.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] Sellers enter product or service details or upload documents through a smartphone application. The entered information can include product specifications, manuals, brochures, and price lists. The input data can be text or PDF, which is then formatted and sent to the server.

[0210] Step 2:

[0211] Through a smartphone application, buyers input detailed requirements for the product or service they are looking for, such as "high-durability parts" or "delivery within 30 days," which are also sent in text format to the server.

[0212] Step 3:

[0213] The server stores the data sent by the seller and buyer in a database. At this stage, the data is stored in its raw form and has not yet been parsed.

[0214] Step 4:

[0215] The server uses a natural language processing engine (NLTK library) to parse the seller's product information and the buyer's requirements, tokenizing the text and extracting important keywords and phrases. The input is the stored text data, and the output is structured characteristics and requirements data.

[0216] Step 5:

[0217] The server uses the structured characteristic and requirement data to apply an AI matching algorithm (Scikit-learn library) to calculate the match degree. The input is characteristic data and requirement data, and the output is a numerical value of the match degree between each seller and buyer. Specifically, the text is vectorized using a TF-IDF vectorizer and cosine similarity is calculated.

[0218] Step 6:

[0219] The server lists the best matches between sellers and buyers based on the degree of match, and the listed matching information is compiled into a ranking of recommended products and their degree of match.

[0220] Step 7:

[0221] The server notifies the buyer of the best match. Notification methods include email and push notifications on smartphone applications. For example, a notification message such as "We've found a product that matches your requirements" is sent.

[0222] Step 8:

[0223] After a transaction, sellers and buyers enter their evaluation information through a smartphone application. The evaluation information includes satisfaction with the transaction and reliability. This information is then sent back to the server.

[0224] Step 9:

[0225] The server updates the user's reputation score based on the collected rating information. The reputation score is reflected in the next matching algorithm to further improve matching accuracy. Specifically, the average rating is calculated and reflected in the user profile.

[0226] 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.

[0227] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine perform optimal matching based on that information. An embodiment of this system will be described in detail.

[0228] Input and upload

[0229] Seller Input and Upload

[0230] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. They upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[0231] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. In addition, the emotion engine recognizes and records the seller's emotions when entering or uploading documents and text.

[0232] Buyer Input and Upload

[0233] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[0234] Server: The server receives the input requirements and uploaded documents and stores them in a database. The emotion engine recognizes and records the buyer's emotions when they input or upload them from voice or text.

[0235] Data analysis and matching

[0236] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[0237] Server: Next, an AI matching algorithm is applied. It calculates the degree of match between sellers and buyers by taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine. The AI ​​matching algorithm calculates a comprehensive degree of match, taking into account various factors such as performance, quality, price, delivery time, and the user's emotional state.

[0238] Recommendations and Notifications

[0239] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[0240] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[0241] Ratings and Feedback

[0242] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[0243] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[0244] Specific examples

[0245] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed product performance and quality information in a database. The seller's feelings at the time of uploading (e.g., confidence) are recorded.

[0246] Example 2 (Buyer): Manufacturer B enters requirements such as a highly durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database. The buyer's emotions (e.g., impatience) at the time of entry are recorded.

[0247] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics, requirements, and sentiment information. As a result, it is determined that Manufacturer A's electronic components are optimal for Manufacturer B's requirements.

[0248] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. Manufacturer B receives the notification, checks the details, and starts the transaction.

[0249] This embodiment allows for optimal matching that takes into account the multifaceted needs and emotional states of sellers and buyers.

[0250] The processing flow will be explained below.

[0251] Step 1:

[0252] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, price lists, etc., or manually enter the details.

[0253] Step 2:

[0254] Server: Receives uploaded documents and manually entered data and stores them in a database. In addition, it uses an emotion engine to analyze the voice and text entered or uploaded by the seller, recognizes and records the seller's emotions.

[0255] Step 3:

[0256] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[0257] Step 4:

[0258] Server: Receives input requirements and uploaded documents and stores them in a database. At this time, the emotion engine analyzes the voice and text input and uploads made by the buyer, recognizes and records the buyer's emotions.

[0259] Step 5:

[0260] Server: The seller and buyer information stored in the database is analyzed using a natural language processing (NLP) engine. The NLP engine extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[0261] Step 6:

[0262] Server: Next, the AI ​​matching algorithm is applied, including the emotional information recognized by the emotion engine, to calculate the degree of match between sellers and buyers based on the extracted characteristics and requirements, as well as the emotional information.

[0263] Step 7:

[0264] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[0265] Step 8:

[0266] User (Buyer): After receiving the notification, the buyer logs into the system to check the information of the recommended seller and decides whether to proceed with the transaction based on the recommended information.

[0267] Step 9:

[0268] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their evaluations of each other into the system, and also record their emotional state during the transaction.

[0269] Step 10:

[0270] Server: Receives the input rating and emotion information and updates each user's reputation score. The rating and emotion information is reflected in the next matching algorithm, achieving even more accurate matching.

[0271] Through these steps, the system achieves optimal matching between sellers and buyers, taking into account the multifaceted needs and emotional information of both parties.

[0272] Example 2

[0273] 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."

[0274] Conventional e-commerce systems have difficulty efficiently matching the needs and requirements of sellers and buyers, and do not provide optimal recommendations that take into account the emotional state of the buyer during the transaction. As a result, transaction satisfaction and efficiency are low, and the evaluations of both sellers and buyers are often not reflected. Furthermore, there is a problem in that there are insufficient means to notify both sellers and buyers in a timely manner.

[0275] 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.

[0276] In this invention, the server includes: a means for sellers to input detailed information about products or services or upload documents; a means for buyers to input requirements for desired products or services or upload documents; a means for saving the input or uploaded information in a database and recognizing and recording emotions at the time of input; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm based on the extracted characteristics and requirements to calculate the degree of match and taking emotional information into consideration; a means for listing optimal matches between sellers and buyers based on the calculated degree of match and notifying the user of recommended results based on the user's emotional state; and a means for collecting evaluation information from sellers and buyers, recording their emotional states during the transaction, and updating the reputation score. This enables optimal matching that takes into consideration the multifaceted needs and emotional states of sellers and buyers.

[0277] "Goods and Services" refers to any physical goods or intangible services provided by the Seller.

[0278] "Seller" refers to a user who provides goods or services.

[0279] "Buyer" refers to a user who is seeking a product or service.

[0280] "Detailed Information" refers to information such as descriptions, specifications, and prices of products or services.

[0281] "Requirements" refers to the conditions and wishes of the buyer regarding the goods or services they are looking for.

[0282] "Document" refers to electronic documents such as PDF, Word, Excel, etc.

[0283] "Database" refers to an electronic structure for systematically storing and managing input or uploaded information.

[0284] "Emotion" refers to the emotional state (e.g., joy, sadness, impatience, confidence, etc.) sensed at the time of user input or upload.

[0285] A "natural language processing engine" is a program that analyzes text data and understands human language.

[0286] "AI matching algorithm" refers to an artificial intelligence program that calculates the optimal combination based on the seller's product characteristics and the buyer's requirements.

[0287] "Match degree" refers to the degree to which the requirements and characteristics of the seller and buyer match.

[0288] "Recommended result" refers to the combination between a seller and a buyer that is determined to be optimal based on the calculated match degree.

[0289] "Notification" refers to the notification of recommended results sent via email or in-app message.

[0290] "Rating Information" refers to the ratings and feedback that sellers and buyers give to each other after a transaction.

[0291] "Reputation score" refers to a numerical value that indicates user reliability and satisfaction, calculated based on collected evaluation information.

[0292] System Overview

[0293] This invention relates to an e-commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine use this information to perform optimal matching. The program processing of this system is explained in detail below.

[0294] Hardware and Software Used

[0295] Examples of hardware used include servers, terminals, and network devices, while software uses natural language processing engines, AI matching algorithms, emotion engines, and database management systems.

[0296] Specific processing of the program

[0297] Seller login and data entry

[0298] User (Seller): The seller logs into the system using a dedicated terminal or web browser, opens the "Product / Service Registration" screen, and uploads documents such as product specifications, manuals, brochures, and price lists, or manually enters the details.

[0299] Server: The server receives documents uploaded and information entered by sellers and stores them in a database. In addition, an emotion engine recognizes and records the seller's emotions when entering information from voice and text.

[0300] Buyer login and requirements input

[0301] User (Buyer): The buyer also logs in, opens the "Requirements Registration" screen, and enters the requirements for the product or service they are looking for in natural language, or uploads a table summarizing their requirements.

[0302] Server: The server stores the information entered or uploaded by the buyer in a database. At the same time, the emotion engine recognizes and records the buyer's emotions at the time of input.

[0303] Data analysis and matching

[0304] Server: The information stored in the database is analyzed by a natural language processing engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[0305] Next, an AI matching algorithm is applied, taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine, to calculate the degree of match between the seller and the buyer. The AI ​​matching algorithm calculates a comprehensive match that takes into account multiple factors such as performance, quality, price, delivery time, and the user's emotional state.

[0306] Recommendations and Notifications

[0307] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[0308] User (Buyer): Upon receiving the notification, the buyer logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[0309] Ratings and Feedback

[0310] Users (sellers / buyers): After completing a transaction, sellers and buyers input their ratings of each other. Their emotional state during the transaction is also recorded and added to the ratings.

[0311] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[0312] Specific examples

[0313] Example 1 (Seller): A manufacturer uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed information about the product's performance and quality in a database. The emotion (e.g., confidence) at the time of uploading is recorded.

[0314] Example 2 (Buyer): A manufacturing company enters requirements, such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language, which is also stored in the database. The buyer's emotion (e.g., impatience) is recorded.

[0315] Example 3 (matching): The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the match degree based on these characteristics, requirements, and sentiment information. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[0316] Example 4 (Notification): The server sends a notification to a manufacturer recommending the manufacturer's electronic components. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. The manufacturer receives the notification, checks the details, and starts the transaction.

[0317] Prompt Sentence Examples

[0318] "A manufacturing company is looking for durable electronic components that can be delivered within 30 days. Can you recommend the best seller for the system?"

[0319] This embodiment takes into account the multifaceted needs and emotional states of sellers and buyers to achieve optimal matching.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1: Seller login and data entry

[0322] User (seller): The seller logs into the system using a dedicated terminal or web browser. After logging in, the seller opens the "Product / Service Registration" screen. Specific actions include uploading documents such as product specifications, manuals, brochures, and price lists, or manually entering detailed information. Input data includes the product name, description, price, and features.

[0323] Input: Product or service details entered by the seller or documents uploaded.

[0324] Output: Seller offers stored in the database.

[0325] Server: The server receives uploaded documents and manually entered data and stores them in a database. Specifically, the server processes and stores the data it receives. Furthermore, the emotion engine recognizes and records emotions from the voice and text input.

[0326] Input: Data entered or uploaded by the seller.

[0327] Output: Information stored in a database along with emotion recognition.

[0328] Step 2: Buyer login and input requirements

[0329] User (Buyer): Similarly, the buyer logs in to the system and opens the "Requirements Registration" screen. Specifically, the buyer enters the requirements for the desired product or service in natural language or uploads a table summarizing the requirements. The input data includes the desired product's features, delivery date, price range, etc.

[0330] Input: Product or service requirements entered by the buyer or uploaded documents.

[0331] Output: Buyer requirement information stored in the database.

[0332] Server: The server receives input requirements and uploaded documents and stores them in a database. At the same time, the emotion engine recognizes and records emotions from the buyer's voice and text input.

[0333] Input: Data entered or uploaded by the buyer.

[0334] Output: Information stored in a database along with emotion recognition.

[0335] Step 3: Data analysis and matching

[0336] Server: The information stored in the database is analyzed by a natural language processing (NLP) engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[0337] Input: Seller and buyer information stored in a database.

[0338] Output: Structured data from the natural language processing engine.

[0339] Specifically, the server calls the NLP engine to extract specific keywords and phrases from the document, such as "high durability" or "delivery within 30 days."

[0340] Server: Based on the extracted characteristics and requirements, an AI matching algorithm is applied, which comprehensively evaluates performance, quality, price, delivery time, and emotional information to calculate the degree of match.

[0341] Input: Data structured by the NLP engine.

[0342] Output: The calculated match score.

[0343] Specifically, the server runs a matching algorithm to compare and evaluate the characteristics and requirements to calculate a score.

[0344] Step 4: Generate and communicate recommendations

[0345] Server: Based on the calculated match score, the server generates a list of optimal sellers for the buyer. The generated recommendation results are communicated to the buyer via email or app notification. The recommendation results are adjusted based on the user's emotional state and notified at the appropriate time.

[0346] Input: The calculated match score.

[0347] Output: Notification of recommendation results sent to buyer.

[0348] Specifically, when the server sends emails or in-app notifications, it takes the user's emotional state into consideration to determine the optimal timing and content.

[0349] Step 5: Rating and feedback

[0350] Users (sellers / buyers): After completing a transaction, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[0351] Input: Rating information from sellers and buyers.

[0352] Output: Rating and sentiment information stored in a database.

[0353] Server: Receives the input rating information and updates each user's reputation score. This rating information and sentiment information are reflected in the next matching algorithm.

[0354] Input: Rating information from the user.

[0355] Output: The updated reputation score.

[0356] Specifically, the server stores the rating information in a database and calculates and updates the reputation score, which improves the accuracy of the next matching process.

[0357] (Application example 2)

[0358] 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."

[0359] Existing e-commerce systems simply match the product or service requirements of sellers and buyers, without taking into account the user's emotional state, making it difficult to achieve optimal matching. Even if an appropriate match is made, if the timing or method of receiving the information is inappropriate, the transaction is unlikely to be successful. Furthermore, feedback from post-transaction evaluations does not take into account emotions, making it difficult to fully utilize the feedback for the next match.

[0360] 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 sellers to input detailed information about products or services or upload documents; means for buyers to input requirements for desired products or services or upload documents; an emotion engine that recognizes emotions from user text or voice; means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; means for listing optimal matches between sellers and buyers based on the calculated degree of match and emotion information; means for notifying the seller and buyer of the recommendation results with optimal timing and content based on the emotion; means for collecting evaluation information and emotion information from sellers and buyers and updating reputation scores; and means for storing the input or uploaded information in a database. This enables optimal matching that takes the user's emotional state into consideration and notification at the appropriate time, improving the success rate of transactions and the accuracy of the next match.

[0361] A "Seller" is any person or company that accesses the system to provide details of a product or service.

[0362] A "buyer" is a person or company that enters requirements for a desired product or service into the system.

[0363] A "natural language processing engine" is a technology that analyzes input or uploaded text data and extracts product characteristics and requirements from the content.

[0364] The "AI matching algorithm" is a technology that calculates the degree of match between sellers and buyers based on extracted product characteristics and requirements, and performs optimal matching.

[0365] An "emotion engine" is a technology that recognizes emotions from text or voice input by the user, and records and uses them as data.

[0366] "Match degree" is an index showing the degree of match between the product characteristics provided by the seller and the requirements of the buyer.

[0367] "Recommended results" are results that list the best matches between sellers and buyers based on match degree and sentiment information.

[0368] A "reputation score" is a trust index calculated based on post-transaction evaluation information and sentiment information of sellers and buyers.

[0369] "Database" means a digital storage for storing information entered or uploaded by sellers and buyers.

[0370] The "notification means" is a technology that notifies sellers and buyers of the recommendation results at an appropriate time based on the calculated match degree and sentiment information.

[0371] System Configuration

[0372] The system for realizing this invention mainly comprises a server, terminals, and users. Terminals include smartphones, personal computers, tablets, etc., which users use to access the system.

[0373] Program processing overview

[0374] Entering seller and buyer information

[0375] Sellers and buyers access the system using terminals. Sellers enter details of goods or services or upload documents. For example, they can upload files such as product specifications or brochures. Buyers enter requirements for the products or services they want or upload documents in the same way. The server receives this information and stores it in a database.

[0376] emotion recognition

[0377] The server uses an emotion engine to recognize emotions from the text or voice input by the user. Specifically, it calls the emotion recognition engine (API) and analyzes the input text or voice data. Through this analysis, the user's emotional information is recorded as data.

[0378] Natural Language Processing

[0379] The server uses a natural language processing engine to analyze uploaded documents and input text data to extract sellers' product characteristics and buyers' requirements. The extracted information is stored in a database as structured data.

[0380] Applying AI matching algorithms

[0381] The server applies an AI matching algorithm based on structured data and sentiment information to calculate the degree of match between sellers and buyers, taking into account multiple factors such as performance, quality, price, delivery time, and sentiment.

[0382] Notification of recommended results

[0383] Based on the calculated match rate and emotion information, the server lists the best recommended results and notifies the seller and buyer. Notifications are given at the appropriate time based on the emotion information. For example, if the buyer is in a hurry, a notification is given immediately.

[0384] Ratings and Feedback

[0385] After the transaction is completed, the seller and buyer input their ratings of each other using their terminals. The server collects these ratings and sentiment information and updates the reputation score. The updated ratings are reflected in the next matching algorithm.

[0386] Hardware and software used

[0387] Emotion Recognition Engine: Emotion Recognition API

[0388] Natural Language Processing Engine: NLP (Natural Language Processing) Engine

[0389] Database: NoSQL or relational database

[0390] Matching algorithm: AI algorithm based on machine learning model

[0391] Specific examples

[0392] A seller uploads specifications and brochures for a newly developed electronic component. The server receives these files and stores them in a database as text information. At the same time, the seller's emotions (e.g., confidence) at the time of uploading are recorded.

[0393] A buyer enters that they are looking for durable, heat-resistant electronic components that can be delivered within 30 days. The server receives the requirements and stores them in a database. The buyer's emotion (e.g., impatience) is recorded.

[0394] The server analyzes the information uploaded by the seller and the requirements entered by the buyer, and calculates the degree of match using an AI matching algorithm, which determines that the seller's electronic components are the best fit for the buyer's requirements.

[0395] Notifications are instantaneous and based on the buyer's emotional state. Buyers receive notifications, review details, and initiate transactions.

[0396] Prompt Sentence Examples

[0397] Please recognize the sentiment in the following documents:

[0398] "It's a high-performance smartphone. It's equipped with the latest technology."

[0399] Express your emotions.

[0400]

[0401] Identify the following requirements and identify the emotions:

[0402] "I'm looking for a durable smartphone that costs under 100,000 yen."

[0403] Please output your requirements and feelings.

[0404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0405] Step 1: User logs into the system

[0406] A user (seller or buyer) accesses the system from a device (smartphone, PC, etc.) and enters authentication information to log in. The server verifies the authentication information and starts a user session, which allows the seller or buyer to enter information individually.

[0407] Step 2: Seller enters or uploads product information

[0408] Sellers use terminals to upload documents such as product specifications and brochures to the system, or manually enter detailed information. The server receives this information and stores it in a database. In addition, the emotion engine recognizes emotions from the text and voice data uploaded or entered, and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and corresponding emotion data is generated.

[0409] Step 3: Buyer enters or uploads requirements information

[0410] Buyers use a terminal to input their requirements for the desired product or service in natural language, or upload a document summarizing their requirements. The server receives this information and stores it in a database. The emotion engine recognizes emotions from the input text or voice and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and the corresponding emotion data is generated.

[0411] Step 4: Analyze data using natural language processing

[0412] The server uses a natural language processing engine to analyze uploaded documents and entered text data. Through this analysis, the seller's product characteristics and the buyer's requirements are extracted and stored in a database as structured data. Specifically, specific attributes (e.g., high durability, low price, etc.) are extracted through text analysis, and each attribute is recorded in the corresponding field.

[0413] Step 5: Applying AI matching algorithms

[0414] The server applies an AI matching algorithm based on the structured data and emotional data extracted through natural language processing. This calculates the degree of match between the seller and buyer. The calculation process takes into account multiple factors, including performance, quality, price, delivery time, and emotional state. The higher the degree of match, the better the compatibility between the seller and buyer.

[0415] Step 6: List and notify recommended results

[0416] The server lists the best recommended results based on the calculated match rate and emotional information. The recommended results are stored in a database and are notified to sellers and buyers at the appropriate time based on emotional information (e.g., immediate notification if the buyer is in a hurry). A notification is displayed on the terminal, allowing the user to check the details.

[0417] Step 7: Post-trade evaluation and feedback

[0418] After completing a transaction, sellers and buyers use their terminals to input their ratings of the other party into the system. The server receives the entered rating and sentiment information and stores it in a database. Each user's reputation score is updated and reflected in the next matching algorithm. This improves the matching accuracy of the entire system.

[0419] Step 8: Reflecting on the next match

[0420] The server then incorporates the collected evaluation and sentiment information into the next matching algorithm, enabling even more accurate matching based on past transaction data and sentiment data, thereby continuously improving the user experience.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Second embodiment]

[0425] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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."

[0437] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[0438] Input and upload

[0439] Seller Input and Upload

[0440] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. Here, the seller can easily enter detailed information about their products and services. Specifically, the system provides the ability to upload documents such as product specifications, manuals, brochures, and price lists. There is also a form for manually entering detailed information.

[0441] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. No analysis of the information is performed at this stage.

[0442] Buyer Input and Upload

[0443] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[0444] Server: The entered requirements and uploaded documents are also received by the server and stored in a database.

[0445] Data analysis and matching

[0446] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[0447] Server: Next, an AI matching algorithm is applied. Based on the extracted characteristics and requirements, the degree of match between the seller and the buyer is calculated. The AI ​​matching algorithm takes into account multiple factors (performance, quality, price, delivery time, etc.) to calculate the overall degree of match.

[0448] Recommendations and Notifications

[0449] Server: Based on the calculated match score, a list of the best sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notification.

[0450] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[0451] Ratings and Feedback

[0452] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of each other into the system.

[0453] Server: The server receives the entered rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm, resulting in more accurate matching.

[0454] Specific examples

[0455] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system, which stores detailed information about the product's performance and quality in a database.

[0456] Example 2 (Buyer): Manufacturer B enters requirements such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database.

[0457] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that Manufacturer A's electronic components are the best fit for Manufacturer B's requirements.

[0458] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Manufacturer B receives the notification, checks the details, and starts the transaction.

[0459] This embodiment realizes optimal matching that takes into account the multifaceted needs of sellers and buyers.

[0460] The processing flow will be explained below.

[0461] Step 1:

[0462] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[0463] Step 2:

[0464] Server: Receives uploaded documents and manually entered data and stores it in a database.

[0465] Step 3:

[0466] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[0467] Step 4:

[0468] Server: Receives input requirements and uploaded documents and stores them in a database.

[0469] Step 5:

[0470] Server: Analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. Extracts characteristics such as performance, quality, price, and delivery time from the seller's product information, and extracts the required performance, quality, price, delivery time, and other conditions from the buyer's requirements information.

[0471] Step 6:

[0472] Server: Based on the extracted characteristics and conditions, an AI matching algorithm is applied, taking into account various factors such as performance, quality, price, and delivery time to calculate the degree of match between sellers and buyers.

[0473] Step 7:

[0474] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[0475] Step 8:

[0476] User (Buyer): After receiving the notification, the user logs in to the system to check the recommended seller's information and decides whether to proceed with the transaction based on the recommendation.

[0477] Step 9:

[0478] User (seller / buyer): After the transaction is completed, the user enters their evaluation of the other party into the system.

[0479] Step 10:

[0480] Server: Receives the entered rating information and updates each user's reputation score. This is reflected in the next matching algorithm, achieving even more accurate matching.

[0481] Through these steps, the system achieves optimal matching that takes into account the multifaceted needs of sellers and buyers.

[0482] Example 1

[0483] 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."

[0484] In e-commerce, it has been difficult for sellers and buyers to find the best partner to meet their respective needs using conventional methods. In particular, it has been difficult to achieve effective matching because detailed product or service information and requirements cannot be accurately communicated, and evaluation information from both parties cannot be reflected in the transaction. This has led to issues such as reduced transaction efficiency and a loss of satisfaction for both sellers and buyers.

[0485] 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.

[0486] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for saving the uploaded information in a database; a means for analyzing the saved information with a natural language processing engine and extracting product characteristics and buyer requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for generating a list of sellers optimal for the buyer based on the calculated degree of match; a means for notifying the buyer of the generated list by email or via an app notification function; and a means for collecting evaluation information from sellers and buyers and updating reputation scores. This enables optimal matching based on the needs of sellers and buyers, enabling efficient and satisfying transactions.

[0487] "Seller" refers to a company or individual that offers a product or service.

[0488] "Buyer" refers to a company or individual who wants a product or service.

[0489] "Input" refers to the act of manually registering information into the system.

[0490] "Upload" refers to the act of transferring a data file, such as a document, to the system.

[0491] A "means" refers to a method or device used to achieve a particular purpose.

[0492] A "natural language processing engine" refers to software that analyzes natural language, understands its meaning, and extracts the necessary information.

[0493] "Product characteristics" refers to attributes that indicate the product's performance, quality, specifications, etc.

[0494] "Buyer requirements" refers to the specific conditions and wishes of the buyer regarding the product or service they desire.

[0495] "AI matching algorithm" refers to an algorithm that uses artificial intelligence to calculate the optimal combination between sellers and buyers.

[0496] "Match degree" refers to an indicator that shows how well the products or services offered by the seller meet the buyer's requirements.

[0497] "Database" refers to a system in which information is organized and stored so that it can be searched and accessed as needed.

[0498] A "list" refers to a collection of items enumerated chronologically or sequentially.

[0499] "Email" refers to a means of sending and receiving text messages and files over the Internet.

[0500] "App notification function" refers to the function that allows an application to provide information to the user in real time.

[0501] "Evaluation information" refers to evaluation data that a seller or buyer makes of the other party after a transaction is completed.

[0502] "Reputation score" refers to a numerical value that indicates the trustworthiness and reliability of a user, calculated based on evaluation information.

[0503] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[0504] Hardware and software used

[0505] Server: The central part of the system, containing the database management system (DBMS), natural language processing engine (NLP engine), and computing resources to run the AI ​​matching algorithm.

[0506] Terminal: Provides an interface for sellers and buyers to input or upload information. Specifically, a PC, tablet, smartphone, etc. is used.

[0507] System Structure

[0508] 1. User Authentication

[0509] Users (sellers and buyers) log in to the system and enter their ID and password. The server queries the database for this information and performs authentication. If authentication is successful, the user is redirected to the trading dashboard.

[0510] 2. Seller's product information input and upload

[0511] The seller accesses the "Product / Service Registration" screen through the terminal and enters the details of the product or service or uploads a document. The terminal sends the entered information to the server, which stores this data in a database.

[0512] 3. Buyer's requirements input and upload

[0513] Buyers access the "Requirements Registration" screen through their terminal and enter their requirements for the product or service they are looking for, or upload a document summarizing their requirements. The terminal then sends the entered information to the server, which then stores this data in a database.

[0514] 4. Data Analysis

[0515] The server analyzes the stored information using a natural language processing engine (NLP engine), extracting elements such as product characteristics, quality, and delivery dates from the document content and re-saving them as structured data.

[0516] 5. Applying the Matching Algorithm

[0517] The server applies an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements, comprehensively evaluating multiple factors (performance, quality, price, delivery time, etc.) to determine suitability.

[0518] 6. Generation and notification of recommendation results

[0519] The server generates a list of optimal sellers based on the calculated match score, and the generated list is notified to the buyer via email or the app's notification function.

[0520] 7. Ratings and Feedback

[0521] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The server collects this rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm.

[0522] Specific examples

[0523] Example 1 (Seller):

[0524] Manufacturers upload specifications and brochures for newly developed electronic components to the system, which then stores detailed product performance and quality information in a database.

[0525] Example 2 (Buyer):

[0526] A manufacturer enters requirements for durable, heat-resistant electronic components that can be delivered within 30 days. The requirements are entered in natural language and stored in a database.

[0527] Example 3 (matching):

[0528] The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[0529] Example 4 (notification):

[0530] The server sends a notification to the manufacturer recommending the manufacturer's electronic parts. The manufacturer receives the notification, checks the details, and starts the transaction.

[0531] Prompt Sentence Examples

[0532] Example of prompt for manufacturer upload:

[0533] "Enter the details of your newly developed electronic components into the system. Upload specifications, manuals, brochures, and price lists."

[0534] Example prompt for manufacturer to enter requirements:

[0535] "Please enter your requirements for the electronic components you are looking for. Please be specific, such as high durability, heat resistance, delivery within 30 days, etc."

[0536] In this way, the system accurately grasps the needs of sellers and buyers and achieves optimal matching, thereby enabling effective and efficient e-commerce.

[0537] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0538] Step 1: User authentication

[0539] The user accesses the system and enters his or her ID and password.

[0540] The server performs authentication by querying the received ID and password against a database. The input is the user's ID and password, and the output is the success or failure of the authentication. If the authentication is successful, the server starts a session and redirects the user to the trading dashboard.

[0541] Step 2: Enter and upload seller's product information

[0542] The user (seller) uses a terminal to access the "Product / Service Registration" screen and manually enters detailed product or service information (e.g., specifications and price lists) or uploads a document file.

[0543] The terminal sends the information entered or uploaded by the seller to the server. The input is product information or document files, and the output is stored in a database.

[0544] The server stores the received data in a database. No analysis of the information is performed at this stage.

[0545] Step 3: Enter and upload buyer requirements

[0546] The user (buyer) uses a terminal to access the "Requirements Registration" screen and inputs the requirements for the product or service he or she desires, or uploads a document summarizing the requirements.

[0547] The terminal sends the information entered or uploaded by the buyer to the server. The input is requirement information or document files, and the output is saved in a database.

[0548] The server stores the received data in a database.

[0549] Step 4: Data analysis

[0550] The server analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. The input is the stored product information and requirement information, and the output is the analyzed structured data.

[0551] The server extracts elements such as product characteristics, quality, and delivery date from the document contents and re-stores them as structured data.

[0552] Step 5: Applying the matching algorithm

[0553] The server runs an AI matching algorithm based on the characteristics and requirements extracted by the NLP engine. The input is the extracted characteristics and requirements, and the output is the calculated match score.

[0554] The server comprehensively evaluates multiple factors (performance, quality, price, delivery time, etc.) and calculates the degree of match between the seller and the buyer.

[0555] Step 6: Generate and communicate recommendations

[0556] The server generates a list of optimal sellers based on the calculated match scores. The input is the match score evaluation result, and the output is a recommended list of sellers.

[0557] The server notifies the buyer of the generated listing via email or the app's notification function.

[0558] Step 7: Review the recommendations and trade

[0559] The user (buyer) receives the notification and logs into the system to check the information of the recommended seller. The input is the notified seller information, and the output is the decision to start a transaction.

[0560] The terminal displays an interface for the buyer to check the recommendation results.

[0561] Step 8: Rating and Feedback

[0562] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The input is rating information, and the output is an update of the reputation score.

[0563] The server stores the rating information in a database and updates each user's reputation score, which is then reflected in the next matching algorithm.

[0564] (Application example 1)

[0565] 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."

[0566] In conventional e-commerce systems, matching between sellers and buyers was not fully optimized, making it difficult for buyers to quickly and accurately find the products they wanted. Furthermore, notifying buyers of matching results and reflecting their ratings after a transaction often took time, leaving room for improvement in the user experience.

[0567] 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.

[0568] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for listing optimal matches between sellers and buyers based on the calculated degree of match; a means for notifying the seller and buyer of the recommended results; a means for collecting evaluation information from sellers and buyers and updating reputation scores; and a means for an application installed on a smartphone to register product information and input requirements for a desired product using AI to recommend optimal products. This allows for quick and accurate matching between sellers and buyers, and reflects evaluation information after a transaction in the next match, improving the user experience.

[0569] "Sellers" are users who register their details in the system to offer products or services.

[0570] A "Buyer" is a user who inputs or uploads their requirements for the product or service they want into the system and searches for the most suitable product or service.

[0571] "Detailed product or service information" refers to information that indicates the characteristics of the products or services provided by the seller, such as product specifications, manuals, brochures, and price lists.

[0572] "Requirements" are information that indicates the specific conditions and specifications for the product or service that a buyer desires.

[0573] A "natural language processing engine" is a program that analyzes input documents or text and extracts characteristics and requirements from its content.

[0574] An "AI matching algorithm" is a calculation method that calculates the optimal match between sellers and buyers based on extracted characteristics and requirements.

[0575] "Match degree" is an evaluation index that indicates how well the seller's product characteristics match the buyer's requirements.

[0576] "Notification method" refers to the method used to notify sellers and buyers of matching results, and typically functions as an email or app notification.

[0577] A "reputation score" is a number that indicates each user's trustworthiness and transaction history, updated based on evaluation information collected from sellers and buyers.

[0578] A "smartphone application" is a software application that is installed on a smartphone and allows users to register products and enter requirements.

[0579] This invention describes a system that utilizes an AI matching algorithm that uses information on sellers and buyers to provide optimal products and services. The following is a specific embodiment of this system.

[0580] System Configuration

[0581] The system mainly consists of the following elements:

[0582] 1. A way for sellers to enter product or service details or upload documents

[0583] Sellers use a smartphone application to input or upload product or service specifications, manuals, brochures, price lists, etc. This data is sent to a server and stored in a database.

[0584] 2. A way for buyers to input or upload documents regarding their desired product or service requirements

[0585] Similarly, buyers use a smartphone application to input or upload detailed requirements for the products or services they want, which is also sent to the server and stored in a database.

[0586] 3. Means of analyzing information

[0587] The server analyzes the uploaded information using a natural language processing engine (e.g., NLTK library) to extract the seller's product characteristics and the buyer's requirements. NLTK is used for tokenizing and analyzing text.

[0588] 4. AI Matching Algorithm

[0589] Based on the extracted features and requirements, the server applies an AI matching algorithm to calculate the match degree, which uses the Scikit-learn library to calculate the TF-IDF vectorizer and cosine similarity.

[0590] 5. Notification of recommended results

[0591] Based on the calculated match score, the server lists the best possible matches between buyers and sellers and notifies the buyer of the recommended results, usually via email or app push notification.

[0592] 6. How reputation information is collected and reputation scores are updated

[0593] The server collects the rating information entered by the seller and buyer after the transaction, and updates each user's reputation score, which is reflected in the next matching algorithm.

[0594] explanation

[0595] The server uses a web framework based on Flask and performs a series of processes, from receiving information to storing, analyzing, and notifying users. Buyers and sellers input and upload information through a smartphone application, enabling fast and accurate matching.

[0596] Specific examples

[0597] Sellers upload detailed information about durable electronic components via a smartphone application. Buyers input requirements such as, "I'm looking for durable electronic components that can be delivered within 30 days." The server analyzes this information and calculates the cosine similarity based on the characteristics and requirements. As a result, it notifies the buyer of products with a high degree of match.

[0598] Prompt Sentence Examples

[0599] Below are some example prompts to input to the AI ​​model:

[0600] "Please enter your ruggedized electronics details."

[0601] Using these prompts, a generative AI model helps users enter the appropriate product information.

[0602] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0603] Step 1:

[0604] Sellers enter product or service details or upload documents through a smartphone application. The entered information can include product specifications, manuals, brochures, and price lists. The input data can be text or PDF, which is then formatted and sent to the server.

[0605] Step 2:

[0606] Through a smartphone application, buyers input detailed requirements for the product or service they are looking for, such as "high-durability parts" or "delivery within 30 days," which are also sent in text format to the server.

[0607] Step 3:

[0608] The server stores the data sent by the seller and buyer in a database. At this stage, the data is stored in its raw form and has not yet been parsed.

[0609] Step 4:

[0610] The server uses a natural language processing engine (NLTK library) to parse the seller's product information and the buyer's requirements, tokenizing the text and extracting important keywords and phrases. The input is the stored text data, and the output is structured characteristics and requirements data.

[0611] Step 5:

[0612] The server uses the structured characteristic and requirement data to apply an AI matching algorithm (Scikit-learn library) to calculate the match degree. The input is characteristic data and requirement data, and the output is a numerical value of the match degree between each seller and buyer. Specifically, the text is vectorized using a TF-IDF vectorizer and cosine similarity is calculated.

[0613] Step 6:

[0614] The server lists the best matches between sellers and buyers based on the degree of match, and the listed matching information is compiled into a ranking of recommended products and their degree of match.

[0615] Step 7:

[0616] The server notifies the buyer of the best match. Notification methods include email and push notifications on smartphone applications. For example, a notification message such as "We've found a product that matches your requirements" is sent.

[0617] Step 8:

[0618] After a transaction, sellers and buyers enter their evaluation information through a smartphone application. The evaluation information includes satisfaction with the transaction and reliability. This information is then sent back to the server.

[0619] Step 9:

[0620] The server updates the user's reputation score based on the collected rating information. The reputation score is reflected in the next matching algorithm to further improve matching accuracy. Specifically, the average rating is calculated and reflected in the user profile.

[0621] 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.

[0622] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine perform optimal matching based on that information. An embodiment of this system will be described in detail.

[0623] Input and upload

[0624] Seller Input and Upload

[0625] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. They upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[0626] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. In addition, the emotion engine recognizes and records the seller's emotions when entering or uploading documents and text.

[0627] Buyer Input and Upload

[0628] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[0629] Server: The server receives the input requirements and uploaded documents and stores them in a database. The emotion engine recognizes and records the buyer's emotions when they input or upload them from voice or text.

[0630] Data analysis and matching

[0631] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[0632] Server: Next, an AI matching algorithm is applied. It calculates the degree of match between sellers and buyers by taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine. The AI ​​matching algorithm calculates a comprehensive degree of match, taking into account various factors such as performance, quality, price, delivery time, and the user's emotional state.

[0633] Recommendations and Notifications

[0634] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[0635] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[0636] Ratings and Feedback

[0637] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[0638] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[0639] Specific examples

[0640] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed product performance and quality information in a database. The seller's feelings at the time of uploading (e.g., confidence) are recorded.

[0641] Example 2 (Buyer): Manufacturer B enters requirements such as a highly durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database. The buyer's emotions (e.g., impatience) at the time of entry are recorded.

[0642] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics, requirements, and sentiment information. As a result, it is determined that Manufacturer A's electronic components are optimal for Manufacturer B's requirements.

[0643] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. Manufacturer B receives the notification, checks the details, and starts the transaction.

[0644] This embodiment allows for optimal matching that takes into account the multifaceted needs and emotional states of sellers and buyers.

[0645] The processing flow will be explained below.

[0646] Step 1:

[0647] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[0648] Step 2:

[0649] Server: Receives uploaded documents and manually entered data and stores them in a database. In addition, it uses an emotion engine to analyze the voice and text entered or uploaded by the seller, recognizes and records the seller's emotions.

[0650] Step 3:

[0651] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[0652] Step 4:

[0653] Server: Receives input requirements and uploaded documents and stores them in a database. At this time, the emotion engine analyzes the voice and text input and uploads made by the buyer, recognizes and records the buyer's emotions.

[0654] Step 5:

[0655] Server: The seller and buyer information stored in the database is analyzed using a natural language processing (NLP) engine. The NLP engine extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[0656] Step 6:

[0657] Server: Next, the AI ​​matching algorithm is applied, including the emotional information recognized by the emotion engine, to calculate the degree of match between sellers and buyers based on the extracted characteristics and requirements, as well as the emotional information.

[0658] Step 7:

[0659] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[0660] Step 8:

[0661] User (Buyer): After receiving the notification, the buyer logs into the system to check the information of the recommended seller and decides whether to proceed with the transaction based on the recommended information.

[0662] Step 9:

[0663] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their evaluations of each other into the system, and also record their emotional state during the transaction.

[0664] Step 10:

[0665] Server: Receives the input rating and emotion information and updates each user's reputation score. The rating and emotion information is reflected in the next matching algorithm, achieving even more accurate matching.

[0666] Through these steps, the system achieves optimal matching between sellers and buyers, taking into account the multifaceted needs and emotional information of both parties.

[0667] Example 2

[0668] 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."

[0669] Conventional e-commerce systems have difficulty efficiently matching the needs and requirements of sellers and buyers, and do not provide optimal recommendations that take into account the emotional state of the buyer during the transaction. As a result, transaction satisfaction and efficiency are low, and the evaluations of both sellers and buyers are often not reflected. Furthermore, there is a problem in that there are insufficient means to notify both sellers and buyers in a timely manner.

[0670] 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.

[0671] In this invention, the server includes: a means for sellers to input detailed information about products or services or upload documents; a means for buyers to input requirements for desired products or services or upload documents; a means for saving the input or uploaded information in a database and recognizing and recording emotions at the time of input; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm based on the extracted characteristics and requirements to calculate the degree of match and taking emotional information into consideration; a means for listing optimal matches between sellers and buyers based on the calculated degree of match and notifying the user of recommended results based on the user's emotional state; and a means for collecting evaluation information from sellers and buyers, recording their emotional states during the transaction, and updating the reputation score. This enables optimal matching that takes into consideration the multifaceted needs and emotional states of sellers and buyers.

[0672] "Goods and Services" refers to any physical goods or intangible services provided by the Seller.

[0673] "Seller" refers to a user who provides goods or services.

[0674] "Buyer" refers to a user who is seeking a product or service.

[0675] "Detailed Information" refers to information such as descriptions, specifications, and prices of products or services.

[0676] "Requirements" refers to the conditions and wishes of the buyer regarding the goods or services they are looking for.

[0677] "Document" refers to electronic documents such as PDF, Word, Excel, etc.

[0678] "Database" refers to an electronic structure for systematically storing and managing input or uploaded information.

[0679] "Emotion" refers to the emotional state (e.g., joy, sadness, impatience, confidence, etc.) sensed at the time of user input or upload.

[0680] A "natural language processing engine" is a program that analyzes text data and understands human language.

[0681] "AI matching algorithm" refers to an artificial intelligence program that calculates the optimal combination based on the seller's product characteristics and the buyer's requirements.

[0682] "Match degree" refers to the degree to which the requirements and characteristics of the seller and buyer match.

[0683] "Recommended result" refers to the combination between a seller and a buyer that is determined to be optimal based on the calculated match degree.

[0684] "Notification" refers to the notification of recommended results sent via email or in-app message.

[0685] "Rating Information" refers to the ratings and feedback that sellers and buyers give to each other after a transaction.

[0686] "Reputation score" refers to a numerical value that indicates user reliability and satisfaction, calculated based on collected evaluation information.

[0687] System Overview

[0688] This invention relates to an e-commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine use this information to perform optimal matching. The program processing of this system is explained in detail below.

[0689] Hardware and Software Used

[0690] Examples of hardware used include servers, terminals, and network devices, while software uses natural language processing engines, AI matching algorithms, emotion engines, and database management systems.

[0691] Specific processing of the program

[0692] Seller login and data entry

[0693] User (Seller): The seller logs into the system using a dedicated terminal or web browser, opens the "Product / Service Registration" screen, and uploads documents such as product specifications, manuals, brochures, and price lists, or manually enters the details.

[0694] Server: The server receives documents uploaded and information entered by sellers and stores them in a database. In addition, an emotion engine recognizes and records the seller's emotions when entering information from voice and text.

[0695] Buyer login and requirements input

[0696] User (Buyer): The buyer also logs in, opens the "Requirements Registration" screen, and enters the requirements for the product or service they are looking for in natural language, or uploads a table summarizing their requirements.

[0697] Server: The server stores the information entered or uploaded by the buyer in a database. At the same time, the emotion engine recognizes and records the buyer's emotions at the time of input.

[0698] Data analysis and matching

[0699] Server: The information stored in the database is analyzed by a natural language processing engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[0700] Next, an AI matching algorithm is applied, taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine, to calculate the degree of match between the seller and the buyer. The AI ​​matching algorithm calculates a comprehensive match that takes into account multiple factors such as performance, quality, price, delivery time, and the user's emotional state.

[0701] Recommendations and Notifications

[0702] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[0703] User (Buyer): Upon receiving the notification, the buyer logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[0704] Ratings and Feedback

[0705] Users (sellers / buyers): After completing a transaction, sellers and buyers input their ratings of each other. Their emotional state during the transaction is also recorded and added to the ratings.

[0706] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[0707] Specific examples

[0708] Example 1 (Seller): A manufacturer uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed information about the product's performance and quality in a database. The emotion (e.g., confidence) at the time of uploading is recorded.

[0709] Example 2 (Buyer): A manufacturing company enters requirements, such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language, which is also stored in the database. The buyer's emotion (e.g., impatience) is recorded.

[0710] Example 3 (matching): The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the match degree based on these characteristics, requirements, and sentiment information. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[0711] Example 4 (Notification): The server sends a notification to a manufacturer recommending the manufacturer's electronic components. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. The manufacturer receives the notification, checks the details, and starts the transaction.

[0712] Prompt Sentence Examples

[0713] "A manufacturing company is looking for durable electronic components that can be delivered within 30 days. Can you recommend the best seller for the system?"

[0714] This embodiment takes into account the multifaceted needs and emotional states of sellers and buyers to achieve optimal matching.

[0715] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0716] Step 1: Seller login and data entry

[0717] User (seller): The seller logs into the system using a dedicated terminal or web browser. After logging in, the seller opens the "Product / Service Registration" screen. Specific actions include uploading documents such as product specifications, manuals, brochures, and price lists, or manually entering detailed information. Input data includes the product name, description, price, and features.

[0718] Input: Product or service details entered by the seller or documents uploaded.

[0719] Output: Seller offers stored in the database.

[0720] Server: The server receives uploaded documents and manually entered data and stores them in a database. Specifically, the server processes and stores the data it receives. Furthermore, the emotion engine recognizes and records emotions from the voice and text input.

[0721] Input: Data entered or uploaded by the seller.

[0722] Output: Information stored in a database along with emotion recognition.

[0723] Step 2: Buyer login and input requirements

[0724] User (Buyer): Similarly, the buyer logs in to the system and opens the "Requirements Registration" screen. Specifically, the buyer enters the requirements for the desired product or service in natural language or uploads a table summarizing the requirements. The input data includes the desired product's features, delivery date, price range, etc.

[0725] Input: Product or service requirements entered by the buyer or uploaded documents.

[0726] Output: Buyer requirement information stored in the database.

[0727] Server: The server receives input requirements and uploaded documents and stores them in a database. At the same time, the emotion engine recognizes and records emotions from the buyer's voice and text input.

[0728] Input: Data entered or uploaded by the buyer.

[0729] Output: Information stored in a database along with emotion recognition.

[0730] Step 3: Data analysis and matching

[0731] Server: The information stored in the database is analyzed by a natural language processing (NLP) engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[0732] Input: Seller and buyer information stored in a database.

[0733] Output: Structured data from the natural language processing engine.

[0734] Specifically, the server calls the NLP engine to extract specific keywords and phrases from the document, such as "high durability" or "delivery within 30 days."

[0735] Server: Based on the extracted characteristics and requirements, an AI matching algorithm is applied, which comprehensively evaluates performance, quality, price, delivery time, and emotional information to calculate the degree of match.

[0736] Input: Data structured by the NLP engine.

[0737] Output: The calculated match score.

[0738] Specifically, the server runs a matching algorithm to compare and evaluate the characteristics and requirements to calculate a score.

[0739] Step 4: Generate and communicate recommendations

[0740] Server: Based on the calculated match score, the server generates a list of optimal sellers for the buyer. The generated recommendation results are communicated to the buyer via email or app notification. The recommendation results are adjusted based on the user's emotional state and notified at the appropriate time.

[0741] Input: The calculated match score.

[0742] Output: Notification of recommendation results sent to buyer.

[0743] Specifically, when the server sends emails or in-app notifications, it takes the user's emotional state into consideration to determine the optimal timing and content.

[0744] Step 5: Rating and feedback

[0745] Users (sellers / buyers): After completing a transaction, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[0746] Input: Rating information from sellers and buyers.

[0747] Output: Rating and sentiment information stored in a database.

[0748] Server: Receives the input rating information and updates each user's reputation score. This rating information and sentiment information are reflected in the next matching algorithm.

[0749] Input: User rating information.

[0750] Output: The updated reputation score.

[0751] Specifically, the server stores the rating information in a database and calculates and updates the reputation score, which improves the accuracy of the next matching process.

[0752] (Application example 2)

[0753] 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."

[0754] Existing e-commerce systems simply match the product or service requirements of sellers and buyers, without taking into account the user's emotional state, making it difficult to achieve optimal matching. Even if an appropriate match is made, if the timing or method of receiving the information is inappropriate, the transaction is unlikely to be successful. Furthermore, feedback from post-transaction evaluations does not take into account emotions, making it difficult to fully utilize the feedback for the next match.

[0755] 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 sellers to input detailed information about products or services or upload documents; means for buyers to input requirements for desired products or services or upload documents; an emotion engine that recognizes emotions from user text or voice; means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; means for listing optimal matches between sellers and buyers based on the calculated degree of match and emotion information; means for notifying the seller and buyer of the recommendation results with optimal timing and content based on the emotion; means for collecting evaluation information and emotion information from sellers and buyers and updating reputation scores; and means for storing the input or uploaded information in a database. This enables optimal matching that takes the user's emotional state into consideration and notification at the appropriate time, improving the success rate of transactions and the accuracy of the next match.

[0756] A "Seller" is any person or company that accesses the system to provide details of a product or service.

[0757] A "buyer" is a person or company that enters requirements for a desired product or service into the system.

[0758] A "natural language processing engine" is a technology that analyzes input or uploaded text data and extracts product characteristics and requirements from the content.

[0759] The "AI matching algorithm" is a technology that calculates the degree of match between sellers and buyers based on extracted product characteristics and requirements, and performs optimal matching.

[0760] An "emotion engine" is a technology that recognizes emotions from text or voice input by the user, and records and uses them as data.

[0761] "Match degree" is an index showing the degree of match between the product characteristics provided by the seller and the requirements of the buyer.

[0762] "Recommended results" are results that list the best matches between sellers and buyers based on match degree and sentiment information.

[0763] A "reputation score" is a trust index calculated based on post-transaction evaluation information and sentiment information of sellers and buyers.

[0764] "Database" means a digital storage for storing information entered or uploaded by sellers and buyers.

[0765] The "notification means" is a technology that notifies sellers and buyers of the recommendation results at an appropriate time based on the calculated match degree and sentiment information.

[0766] System Configuration

[0767] The system for realizing this invention mainly comprises a server, a terminal, and a user. The terminal includes a smartphone, a PC, a tablet, etc., which the user uses to access the system.

[0768] Program processing overview

[0769] Entering seller and buyer information

[0770] Sellers and buyers access the system using terminals. Sellers enter details of goods or services or upload documents. For example, they can upload files such as product specifications or brochures. Buyers enter requirements for the products or services they want or upload documents in the same way. The server receives this information and stores it in a database.

[0771] emotion recognition

[0772] The server uses an emotion engine to recognize emotions from the text or voice input by the user. Specifically, it calls the emotion recognition engine (API) and analyzes the input text or voice data. Through this analysis, the user's emotional information is recorded as data.

[0773] Natural Language Processing

[0774] The server uses a natural language processing engine to analyze uploaded documents and input text data to extract sellers' product characteristics and buyers' requirements. The extracted information is stored in a database as structured data.

[0775] Applying AI matching algorithms

[0776] The server applies an AI matching algorithm based on structured data and sentiment information to calculate the degree of match between sellers and buyers, taking into account multiple factors such as performance, quality, price, delivery time, and sentiment.

[0777] Notification of recommended results

[0778] Based on the calculated match rate and emotion information, the server lists the best recommended results and notifies the seller and buyer. Notifications are given at the appropriate time based on the emotion information. For example, if the buyer is in a hurry, a notification is given immediately.

[0779] Ratings and Feedback

[0780] After the transaction is completed, the seller and buyer input their ratings of each other using their terminals. The server collects these ratings and sentiment information and updates the reputation score. The updated ratings are reflected in the next matching algorithm.

[0781] Hardware and software used

[0782] Emotion Recognition Engine: Emotion Recognition API

[0783] Natural Language Processing Engine: NLP (Natural Language Processing) Engine

[0784] Database: NoSQL or relational database

[0785] Matching algorithm: AI algorithm based on machine learning model

[0786] Specific examples

[0787] A seller uploads specifications and brochures for a newly developed electronic component. The server receives these files and stores them in a database as text information. At the same time, the seller's emotions (e.g., confidence) at the time of uploading are recorded.

[0788] A buyer enters that they are looking for durable, heat-resistant electronic components that can be delivered within 30 days. The server receives the requirements and stores them in a database. The buyer's emotion (e.g., impatience) is recorded.

[0789] The server analyzes the information uploaded by the seller and the requirements entered by the buyer, and calculates the degree of match using an AI matching algorithm, which determines that the seller's electronic components are the best fit for the buyer's requirements.

[0790] Notifications are instantaneous and based on the buyer's emotional state. Buyers receive notifications, review details, and initiate transactions.

[0791] Prompt Sentence Examples

[0792] Please recognize the sentiment in the following documents:

[0793] "It's a high-performance smartphone. It's equipped with the latest technology."

[0794] Express your emotions.

[0795]

[0796] Identify the following requirements and identify the emotions:

[0797] "I'm looking for a durable smartphone that costs under 100,000 yen."

[0798] Please output your requirements and feelings.

[0799] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0800] Step 1: User logs into the system

[0801] A user (seller or buyer) accesses the system from a device (smartphone, PC, etc.) and enters authentication information to log in. The server verifies the authentication information and starts a user session, which allows the seller or buyer to enter information individually.

[0802] Step 2: Seller enters or uploads product information

[0803] Sellers use terminals to upload documents such as product specifications and brochures to the system, or manually enter detailed information. The server receives this information and stores it in a database. In addition, the emotion engine recognizes emotions from the text and voice data uploaded or entered, and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and corresponding emotion data is generated.

[0804] Step 3: Buyer enters or uploads requirements information

[0805] Buyers use a terminal to input their requirements for the desired product or service in natural language, or upload a document summarizing their requirements. The server receives this information and stores it in a database. The emotion engine recognizes emotions from the input text or voice and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and the corresponding emotion data is generated.

[0806] Step 4: Analyze data using natural language processing

[0807] The server uses a natural language processing engine to analyze uploaded documents and entered text data. Through this analysis, the seller's product characteristics and the buyer's requirements are extracted and stored in a database as structured data. Specifically, specific attributes (e.g., high durability, low price, etc.) are extracted through text analysis, and each attribute is recorded in the corresponding field.

[0808] Step 5: Applying AI matching algorithms

[0809] The server applies an AI matching algorithm based on the structured data and emotional data extracted through natural language processing. This calculates the degree of match between the seller and buyer. The calculation process takes into account multiple factors, including performance, quality, price, delivery time, and emotional state. The higher the degree of match, the better the compatibility between the seller and buyer.

[0810] Step 6: List and notify recommended results

[0811] The server lists the best recommended results based on the calculated match rate and emotional information. The recommended results are stored in a database and are notified to sellers and buyers at the appropriate time based on emotional information (e.g., immediate notification if the buyer is in a hurry). A notification is displayed on the terminal, allowing the user to check the details.

[0812] Step 7: Post-trade evaluation and feedback

[0813] After completing a transaction, sellers and buyers use their terminals to input their ratings of the other party into the system. The server receives the entered rating and sentiment information and stores it in a database. Each user's reputation score is updated and reflected in the next matching algorithm. This improves the matching accuracy of the entire system.

[0814] Step 8: Reflecting on the next match

[0815] The server then incorporates the collected evaluation and sentiment information into the next matching algorithm, enabling even more accurate matching based on past transaction data and sentiment data, thereby continuously improving the user experience.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] [Third embodiment]

[0820] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0821] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0822] 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).

[0823] 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.

[0824] 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.

[0825] 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).

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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."

[0832] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[0833] Input and upload

[0834] Seller Input and Upload

[0835] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. Here, the seller can easily enter detailed information about their products and services. Specifically, the system provides the ability to upload documents such as product specifications, manuals, brochures, and price lists. There is also a form for manually entering detailed information.

[0836] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. No analysis of the information is performed at this stage.

[0837] Buyer Input and Upload

[0838] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[0839] Server: The entered requirements and uploaded documents are also received by the server and stored in a database.

[0840] Data analysis and matching

[0841] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[0842] Server: Next, an AI matching algorithm is applied. Based on the extracted characteristics and requirements, the degree of match between the seller and the buyer is calculated. The AI ​​matching algorithm takes into account multiple factors (performance, quality, price, delivery time, etc.) to calculate the overall degree of match.

[0843] Recommendations and Notifications

[0844] Server: Based on the calculated match score, a list of the best sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notification.

[0845] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[0846] Ratings and Feedback

[0847] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of each other into the system.

[0848] Server: The server receives the entered rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm, resulting in more accurate matching.

[0849] Specific examples

[0850] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system, which stores detailed information about the product's performance and quality in a database.

[0851] Example 2 (Buyer): Manufacturer B enters requirements such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database.

[0852] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that Manufacturer A's electronic components are the best fit for Manufacturer B's requirements.

[0853] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Manufacturer B receives the notification, checks the details, and starts the transaction.

[0854] This embodiment realizes optimal matching that takes into account the multifaceted needs of sellers and buyers.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[0858] Step 2:

[0859] Server: Receives uploaded documents and manually entered data and stores it in a database.

[0860] Step 3:

[0861] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[0862] Step 4:

[0863] Server: Receives input requirements and uploaded documents and stores them in a database.

[0864] Step 5:

[0865] Server: Analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. Extracts characteristics such as performance, quality, price, and delivery time from the seller's product information, and extracts the required performance, quality, price, delivery time, and other conditions from the buyer's requirements information.

[0866] Step 6:

[0867] Server: Based on the extracted characteristics and conditions, an AI matching algorithm is applied, taking into account various factors such as performance, quality, price, and delivery time to calculate the degree of match between sellers and buyers.

[0868] Step 7:

[0869] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[0870] Step 8:

[0871] User (Buyer): After receiving the notification, the user logs in to the system to check the recommended seller's information and decides whether to proceed with the transaction based on the recommendation.

[0872] Step 9:

[0873] User (seller / buyer): After the transaction is completed, the user enters their evaluation of the other party into the system.

[0874] Step 10:

[0875] Server: Receives the entered rating information and updates each user's reputation score. This is reflected in the next matching algorithm, achieving even more accurate matching.

[0876] Through these steps, the system achieves optimal matching that takes into account the multifaceted needs of sellers and buyers.

[0877] Example 1

[0878] 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."

[0879] In e-commerce, it has been difficult for sellers and buyers to find the best partner to meet their respective needs using conventional methods. In particular, it has been difficult to achieve effective matching because detailed product or service information and requirements cannot be accurately communicated, and evaluation information from both parties cannot be reflected in the transaction. This has led to issues such as reduced transaction efficiency and a loss of satisfaction for both sellers and buyers.

[0880] 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.

[0881] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for saving the uploaded information in a database; a means for analyzing the saved information with a natural language processing engine and extracting product characteristics and buyer requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for generating a list of sellers optimal for the buyer based on the calculated degree of match; a means for notifying the buyer of the generated list by email or via an app notification function; and a means for collecting evaluation information from sellers and buyers and updating reputation scores. This enables optimal matching based on the needs of sellers and buyers, enabling efficient and satisfying transactions.

[0882] "Seller" refers to a company or individual that offers a product or service.

[0883] "Buyer" refers to a company or individual who wants a product or service.

[0884] "Input" refers to the act of manually registering information into the system.

[0885] "Upload" refers to the act of transferring a data file, such as a document, to the system.

[0886] A "means" refers to a method or device used to achieve a particular purpose.

[0887] A "natural language processing engine" refers to software that analyzes natural language, understands its meaning, and extracts the necessary information.

[0888] "Product characteristics" refers to attributes that indicate the product's performance, quality, specifications, etc.

[0889] "Buyer requirements" refers to the specific conditions and wishes of the buyer regarding the product or service they desire.

[0890] "AI matching algorithm" refers to an algorithm that uses artificial intelligence to calculate the optimal combination between sellers and buyers.

[0891] "Match degree" refers to an indicator that shows how well the products or services offered by the seller meet the buyer's requirements.

[0892] "Database" refers to a system in which information is organized and stored so that it can be searched and accessed as needed.

[0893] A "list" refers to a collection of items enumerated chronologically or sequentially.

[0894] "Email" refers to a means of sending and receiving text messages and files over the Internet.

[0895] "App notification function" refers to the function that allows an application to provide information to the user in real time.

[0896] "Evaluation information" refers to evaluation data that a seller or buyer makes of the other party after a transaction is completed.

[0897] "Reputation score" refers to a numerical value that indicates the trustworthiness and reliability of a user, calculated based on evaluation information.

[0898] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[0899] Hardware and software used

[0900] Server: The central part of the system, containing the database management system (DBMS), natural language processing engine (NLP engine), and computing resources to run the AI ​​matching algorithm.

[0901] Terminal: Provides an interface for sellers and buyers to input or upload information. Specifically, a PC, tablet, smartphone, etc. is used.

[0902] System Structure

[0903] 1. User Authentication

[0904] Users (sellers and buyers) log in to the system and enter their ID and password. The server queries the database for this information and performs authentication. If authentication is successful, the user is redirected to the trading dashboard.

[0905] 2. Seller's product information input and upload

[0906] The seller accesses the "Product / Service Registration" screen through the terminal and enters the details of the product or service or uploads a document. The terminal sends the entered information to the server, which stores this data in a database.

[0907] 3. Buyer's requirements input and upload

[0908] Buyers access the "Requirements Registration" screen through their terminal and enter their requirements for the product or service they are looking for, or upload a document summarizing their requirements. The terminal then sends the entered information to the server, which then stores this data in a database.

[0909] 4. Data Analysis

[0910] The server analyzes the stored information using a natural language processing engine (NLP engine), extracting elements such as product characteristics, quality, and delivery dates from the document content and re-saving them as structured data.

[0911] 5. Applying the Matching Algorithm

[0912] The server applies an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements, comprehensively evaluating multiple factors (performance, quality, price, delivery time, etc.) to determine suitability.

[0913] 6. Generation and notification of recommendation results

[0914] The server generates a list of optimal sellers based on the calculated match score, and the generated list is notified to the buyer via email or the app's notification function.

[0915] 7. Ratings and Feedback

[0916] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The server collects this rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm.

[0917] Specific examples

[0918] Example 1 (Seller):

[0919] Manufacturers upload specifications and brochures for newly developed electronic components to the system, which then stores detailed product performance and quality information in a database.

[0920] Example 2 (Buyer):

[0921] A manufacturer enters requirements for durable, heat-resistant electronic components that can be delivered within 30 days. The requirements are entered in natural language and stored in a database.

[0922] Example 3 (matching):

[0923] The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[0924] Example 4 (notification):

[0925] The server sends a notification to the manufacturer recommending the manufacturer's electronic parts. The manufacturer receives the notification, checks the details, and starts the transaction.

[0926] Prompt Sentence Examples

[0927] Example of prompt for manufacturer upload:

[0928] "Enter the details of your newly developed electronic components into the system. Upload specifications, manuals, brochures, and price lists."

[0929] Example prompt for manufacturer to enter requirements:

[0930] "Please enter your requirements for the electronic components you are looking for. Please be specific, such as high durability, heat resistance, delivery within 30 days, etc."

[0931] In this way, the system accurately grasps the needs of sellers and buyers and achieves optimal matching, thereby enabling effective and efficient e-commerce.

[0932] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0933] Step 1: User authentication

[0934] The user accesses the system and enters his or her ID and password.

[0935] The server performs authentication by querying the received ID and password against a database. The input is the user's ID and password, and the output is the success or failure of the authentication. If the authentication is successful, the server starts a session and redirects the user to the trading dashboard.

[0936] Step 2: Enter and upload seller's product information

[0937] The user (seller) uses a terminal to access the "Product / Service Registration" screen and manually enters detailed product or service information (e.g., specifications and price lists) or uploads a document file.

[0938] The terminal sends the information entered or uploaded by the seller to the server. The input is product information or document files, and the output is stored in a database.

[0939] The server stores the received data in a database. No analysis of the information is performed at this stage.

[0940] Step 3: Enter and upload buyer requirements

[0941] The user (buyer) uses a terminal to access the "Requirements Registration" screen and inputs the requirements for the product or service he or she desires, or uploads a document summarizing the requirements.

[0942] The terminal sends the information entered or uploaded by the buyer to the server. The input is requirement information or document files, and the output is saved in a database.

[0943] The server stores the received data in a database.

[0944] Step 4: Data analysis

[0945] The server analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. The input is the stored product information and requirement information, and the output is the analyzed structured data.

[0946] The server extracts elements such as product characteristics, quality, and delivery date from the document contents and re-stores them as structured data.

[0947] Step 5: Applying the matching algorithm

[0948] The server runs an AI matching algorithm based on the characteristics and requirements extracted by the NLP engine. The input is the extracted characteristics and requirements, and the output is the calculated match score.

[0949] The server comprehensively evaluates multiple factors (performance, quality, price, delivery time, etc.) and calculates the degree of match between the seller and the buyer.

[0950] Step 6: Generate and communicate recommendations

[0951] The server generates a list of optimal sellers based on the calculated match scores. The input is the match score evaluation result, and the output is a recommended list of sellers.

[0952] The server notifies the buyer of the generated listing via email or the app's notification function.

[0953] Step 7: Review the recommendations and trade

[0954] The user (buyer) receives the notification and logs into the system to check the information of the recommended seller. The input is the notified seller information, and the output is the decision to start a transaction.

[0955] The terminal displays an interface for the buyer to check the recommendation results.

[0956] Step 8: Rating and Feedback

[0957] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The input is rating information, and the output is an update of the reputation score.

[0958] The server stores the rating information in a database and updates each user's reputation score, which is then reflected in the next matching algorithm.

[0959] (Application example 1)

[0960] 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."

[0961] In conventional e-commerce systems, matching between sellers and buyers was not fully optimized, making it difficult for buyers to quickly and accurately find the products they wanted. Furthermore, notifying buyers of matching results and reflecting their ratings after a transaction often took time, leaving room for improvement in the user experience.

[0962] 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.

[0963] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for listing optimal matches between sellers and buyers based on the calculated degree of match; a means for notifying the seller and buyer of the recommended results; a means for collecting evaluation information from sellers and buyers and updating reputation scores; and a means for an application installed on a smartphone to register product information and input requirements for a desired product using AI to recommend optimal products. This allows for quick and accurate matching between sellers and buyers, and reflects evaluation information after a transaction in the next match, improving the user experience.

[0964] "Sellers" are users who register their details in the system to offer products or services.

[0965] A "Buyer" is a user who inputs or uploads their requirements for the product or service they want into the system and searches for the most suitable product or service.

[0966] "Detailed product or service information" refers to information that indicates the characteristics of the products or services provided by the seller, such as product specifications, manuals, brochures, and price lists.

[0967] "Requirements" are information that indicates the specific conditions and specifications for the product or service that a buyer desires.

[0968] A "natural language processing engine" is a program that analyzes input documents or text and extracts characteristics and requirements from its content.

[0969] An "AI matching algorithm" is a calculation method that calculates the optimal match between sellers and buyers based on extracted characteristics and requirements.

[0970] "Match degree" is an evaluation index that indicates how well the seller's product characteristics match the buyer's requirements.

[0971] "Notification method" refers to the method used to notify sellers and buyers of matching results, and typically functions as an email or app notification.

[0972] A "reputation score" is a number that indicates each user's trustworthiness and transaction history, updated based on evaluation information collected from sellers and buyers.

[0973] A "smartphone application" is a software application that is installed on a smartphone and allows users to register products and enter requirements.

[0974] This invention describes a system that utilizes an AI matching algorithm that uses information on sellers and buyers to provide optimal products and services. The following is a specific embodiment of this system.

[0975] System Configuration

[0976] The system mainly consists of the following elements:

[0977] 1. A way for sellers to enter product or service details or upload documents

[0978] Sellers use a smartphone application to input or upload product or service specifications, manuals, brochures, price lists, etc. This data is sent to a server and stored in a database.

[0979] 2. A way for buyers to input or upload documents regarding their desired product or service requirements

[0980] Similarly, buyers use a smartphone application to input or upload detailed requirements for the products or services they want, which is also sent to the server and stored in a database.

[0981] 3. Means of analyzing information

[0982] The server analyzes the uploaded information using a natural language processing engine (e.g., NLTK library) to extract the seller's product characteristics and the buyer's requirements. NLTK is used for tokenizing and analyzing text.

[0983] 4. AI Matching Algorithm

[0984] Based on the extracted features and requirements, the server applies an AI matching algorithm to calculate the match degree, which uses the Scikit-learn library to calculate the TF-IDF vectorizer and cosine similarity.

[0985] 5. Notification of recommended results

[0986] Based on the calculated match score, the server lists the best possible matches between buyers and sellers and notifies the buyer of the recommended results, usually via email or app push notification.

[0987] 6. How reputation information is collected and reputation scores are updated

[0988] The server collects the rating information entered by the seller and buyer after the transaction, and updates each user's reputation score, which is reflected in the next matching algorithm.

[0989] explanation

[0990] The server uses a web framework based on Flask and performs a series of processes, from receiving information to storing, analyzing, and notifying users. Buyers and sellers input and upload information through a smartphone application, enabling fast and accurate matching.

[0991] Specific examples

[0992] Sellers upload detailed information about durable electronic components via a smartphone application. Buyers input requirements such as, "I'm looking for durable electronic components that can be delivered within 30 days." The server analyzes this information and calculates the cosine similarity based on the characteristics and requirements. As a result, it notifies the buyer of products with a high degree of match.

[0993] Prompt Sentence Examples

[0994] Below are some example prompts to input to the AI ​​model:

[0995] "Please enter your ruggedized electronics details."

[0996] Using these prompts, a generative AI model helps users enter the appropriate product information.

[0997] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0998] Step 1:

[0999] Sellers enter product or service details or upload documents through a smartphone application. The entered information can include product specifications, manuals, brochures, and price lists. The input data can be text or PDF, which is then formatted and sent to the server.

[1000] Step 2:

[1001] Through a smartphone application, buyers input detailed requirements for the product or service they are looking for, such as "high-durability parts" or "delivery within 30 days," which are also sent in text format to the server.

[1002] Step 3:

[1003] The server stores the data sent by the seller and buyer in a database. At this stage, the data is stored in its raw form and has not yet been parsed.

[1004] Step 4:

[1005] The server uses a natural language processing engine (NLTK library) to parse the seller's product information and the buyer's requirements, tokenizing the text and extracting important keywords and phrases. The input is the stored text data, and the output is structured characteristics and requirements data.

[1006] Step 5:

[1007] The server uses the structured characteristic and requirement data to apply an AI matching algorithm (Scikit-learn library) to calculate the match degree. The input is characteristic data and requirement data, and the output is a numerical value of the match degree between each seller and buyer. Specifically, the text is vectorized using a TF-IDF vectorizer and cosine similarity is calculated.

[1008] Step 6:

[1009] The server lists the best matches between sellers and buyers based on the degree of match, and the listed matching information is compiled into a ranking of recommended products and their degree of match.

[1010] Step 7:

[1011] The server notifies the buyer of the best match. Notification methods include email and push notifications on smartphone applications. For example, a notification message such as "We've found a product that matches your requirements" is sent.

[1012] Step 8:

[1013] After a transaction, sellers and buyers enter their evaluation information through a smartphone application. The evaluation information includes satisfaction with the transaction and reliability. This information is then sent back to the server.

[1014] Step 9:

[1015] The server updates the user's reputation score based on the collected rating information. The reputation score is reflected in the next matching algorithm to further improve matching accuracy. Specifically, the average rating is calculated and reflected in the user profile.

[1016] 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.

[1017] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine perform optimal matching based on that information. An embodiment of this system will be described in detail.

[1018] Input and upload

[1019] Seller Input and Upload

[1020] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. They upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[1021] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. In addition, the emotion engine recognizes and records the seller's emotions when entering or uploading documents and text.

[1022] Buyer Input and Upload

[1023] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[1024] Server: The server receives the input requirements and uploaded documents and stores them in a database. The emotion engine recognizes and records the buyer's emotions when they input or upload them from voice or text.

[1025] Data analysis and matching

[1026] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[1027] Server: Next, an AI matching algorithm is applied. It calculates the degree of match between sellers and buyers by taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine. The AI ​​matching algorithm calculates a comprehensive degree of match, taking into account various factors such as performance, quality, price, delivery time, and the user's emotional state.

[1028] Recommendations and Notifications

[1029] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[1030] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[1031] Ratings and Feedback

[1032] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[1033] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[1034] Specific examples

[1035] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed product performance and quality information in a database. The seller's feelings at the time of uploading (e.g., confidence) are recorded.

[1036] Example 2 (Buyer): Manufacturer B enters requirements such as a highly durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database. The buyer's emotions (e.g., impatience) at the time of entry are recorded.

[1037] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics, requirements, and sentiment information. As a result, it is determined that Manufacturer A's electronic components are optimal for Manufacturer B's requirements.

[1038] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. Manufacturer B receives the notification, checks the details, and starts the transaction.

[1039] This embodiment allows for optimal matching that takes into account the multifaceted needs and emotional states of sellers and buyers.

[1040] The processing flow will be explained below.

[1041] Step 1:

[1042] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[1043] Step 2:

[1044] Server: Receives uploaded documents and manually entered data and stores them in a database. In addition, it uses an emotion engine to analyze the voice and text entered or uploaded by the seller, recognizes and records the seller's emotions.

[1045] Step 3:

[1046] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[1047] Step 4:

[1048] Server: Receives input requirements and uploaded documents and stores them in a database. At this time, the emotion engine analyzes the voice and text input and uploads made by the buyer, recognizes and records the buyer's emotions.

[1049] Step 5:

[1050] Server: The seller and buyer information stored in the database is analyzed using a natural language processing (NLP) engine. The NLP engine extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[1051] Step 6:

[1052] Server: Next, the AI ​​matching algorithm is applied, including the emotional information recognized by the emotion engine, to calculate the degree of match between sellers and buyers based on the extracted characteristics and requirements, as well as the emotional information.

[1053] Step 7:

[1054] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[1055] Step 8:

[1056] User (Buyer): After receiving the notification, the buyer logs into the system to check the information of the recommended seller and decides whether to proceed with the transaction based on the recommended information.

[1057] Step 9:

[1058] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their evaluations of each other into the system, and also record their emotional state during the transaction.

[1059] Step 10:

[1060] Server: Receives the input rating and emotion information and updates each user's reputation score. The rating and emotion information is reflected in the next matching algorithm, achieving even more accurate matching.

[1061] Through these steps, the system achieves optimal matching between sellers and buyers, taking into account the multifaceted needs and emotional information of both parties.

[1062] Example 2

[1063] 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."

[1064] Conventional e-commerce systems have difficulty efficiently matching the needs and requirements of sellers and buyers, and do not provide optimal recommendations that take into account the emotional state of the buyer during the transaction. As a result, transaction satisfaction and efficiency are low, and the evaluations of both sellers and buyers are often not reflected. Furthermore, there is a problem in that there are insufficient means to notify both sellers and buyers in a timely manner.

[1065] 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.

[1066] In this invention, the server includes: a means for sellers to input detailed information about products or services or upload documents; a means for buyers to input requirements for desired products or services or upload documents; a means for saving the input or uploaded information in a database and recognizing and recording emotions at the time of input; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm based on the extracted characteristics and requirements to calculate the degree of match and taking emotional information into consideration; a means for listing optimal matches between sellers and buyers based on the calculated degree of match and notifying the user of recommended results based on the user's emotional state; and a means for collecting evaluation information from sellers and buyers, recording their emotional states during the transaction, and updating the reputation score. This enables optimal matching that takes into consideration the multifaceted needs and emotional states of sellers and buyers.

[1067] "Goods and Services" refers to any physical goods or intangible services provided by the Seller.

[1068] "Seller" refers to a user who provides goods or services.

[1069] "Buyer" refers to a user who is seeking a product or service.

[1070] "Detailed Information" refers to information such as descriptions, specifications, and prices of products or services.

[1071] "Requirements" refers to the conditions and wishes of the buyer regarding the goods or services they are looking for.

[1072] "Document" refers to electronic documents such as PDF, Word, Excel, etc.

[1073] "Database" refers to an electronic structure for systematically storing and managing input or uploaded information.

[1074] "Emotion" refers to the emotional state (e.g., joy, sadness, impatience, confidence, etc.) sensed at the time of user input or upload.

[1075] A "natural language processing engine" is a program that analyzes text data and understands human language.

[1076] "AI matching algorithm" refers to an artificial intelligence program that calculates the optimal combination based on the seller's product characteristics and the buyer's requirements.

[1077] "Match degree" refers to the degree to which the requirements and characteristics of the seller and buyer match.

[1078] "Recommended result" refers to the combination between a seller and a buyer that is determined to be optimal based on the calculated match degree.

[1079] "Notification" refers to the notification of recommended results sent via email or in-app message.

[1080] "Rating Information" refers to the ratings and feedback that sellers and buyers give to each other after a transaction.

[1081] "Reputation score" refers to a numerical value that indicates user reliability and satisfaction, calculated based on collected evaluation information.

[1082] System Overview

[1083] This invention relates to an e-commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine use this information to perform optimal matching. The program processing of this system is explained in detail below.

[1084] Hardware and Software Used

[1085] Examples of hardware used include servers, terminals, and network devices, while software uses natural language processing engines, AI matching algorithms, emotion engines, and database management systems.

[1086] Specific processing of the program

[1087] Seller login and data entry

[1088] User (Seller): The seller logs into the system using a dedicated terminal or web browser, opens the "Product / Service Registration" screen, and uploads documents such as product specifications, manuals, brochures, and price lists, or manually enters the details.

[1089] Server: The server receives documents uploaded and information entered by sellers and stores them in a database. In addition, an emotion engine recognizes and records the seller's emotions when entering information from voice and text.

[1090] Buyer login and requirements input

[1091] User (Buyer): The buyer also logs in, opens the "Requirements Registration" screen, and enters the requirements for the product or service they are looking for in natural language, or uploads a table summarizing their requirements.

[1092] Server: The server stores the information entered or uploaded by the buyer in a database. At the same time, the emotion engine recognizes and records the buyer's emotions at the time of input.

[1093] Data analysis and matching

[1094] Server: The information stored in the database is analyzed by a natural language processing engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[1095] Next, an AI matching algorithm is applied, taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine, to calculate the degree of match between the seller and the buyer. The AI ​​matching algorithm calculates a comprehensive match that takes into account multiple factors such as performance, quality, price, delivery time, and the user's emotional state.

[1096] Recommendations and Notifications

[1097] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[1098] User (Buyer): Upon receiving the notification, the buyer logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[1099] Ratings and Feedback

[1100] Users (sellers / buyers): After completing a transaction, sellers and buyers input their ratings of each other. Their emotional state during the transaction is also recorded and added to the ratings.

[1101] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[1102] Specific examples

[1103] Example 1 (Seller): A manufacturer uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed information about the product's performance and quality in a database. The emotion (e.g., confidence) at the time of uploading is recorded.

[1104] Example 2 (Buyer): A manufacturing company enters requirements, such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language, which is also stored in the database. The buyer's emotion (e.g., impatience) is recorded.

[1105] Example 3 (matching): The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the match degree based on these characteristics, requirements, and sentiment information. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[1106] Example 4 (Notification): The server sends a notification to a manufacturer recommending the manufacturer's electronic components. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. The manufacturer receives the notification, checks the details, and starts the transaction.

[1107] Prompt Sentence Examples

[1108] "A manufacturing company is looking for durable electronic components that can be delivered within 30 days. Can you recommend the best seller for the system?"

[1109] This embodiment takes into account the multifaceted needs and emotional states of sellers and buyers to achieve optimal matching.

[1110] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1111] Step 1: Seller login and data entry

[1112] User (seller): The seller logs into the system using a dedicated terminal or web browser. After logging in, the seller opens the "Product / Service Registration" screen. Specific actions include uploading documents such as product specifications, manuals, brochures, and price lists, or manually entering detailed information. Input data includes the product name, description, price, and features.

[1113] Input: Product or service details entered by the seller or documents uploaded.

[1114] Output: Seller offers stored in the database.

[1115] Server: The server receives uploaded documents and manually entered data and stores them in a database. Specifically, the server processes and stores the data it receives. Furthermore, the emotion engine recognizes and records emotions from the voice and text input.

[1116] Input: Data entered or uploaded by the seller.

[1117] Output: Information stored in a database along with emotion recognition.

[1118] Step 2: Buyer login and input requirements

[1119] User (Buyer): Similarly, the buyer logs in to the system and opens the "Requirements Registration" screen. Specifically, the buyer enters the requirements for the desired product or service in natural language or uploads a table summarizing the requirements. The input data includes the desired product's features, delivery date, price range, etc.

[1120] Input: Product or service requirements entered by the buyer or uploaded documents.

[1121] Output: Buyer requirement information stored in the database.

[1122] Server: The server receives input requirements and uploaded documents and stores them in a database. At the same time, the emotion engine recognizes and records emotions from the buyer's voice and text input.

[1123] Input: Data entered or uploaded by the buyer.

[1124] Output: Information stored in a database along with emotion recognition.

[1125] Step 3: Data analysis and matching

[1126] Server: The information stored in the database is analyzed by a natural language processing (NLP) engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[1127] Input: Seller and buyer information stored in a database.

[1128] Output: Structured data from the natural language processing engine.

[1129] Specifically, the server calls the NLP engine to extract specific keywords and phrases from the document, such as "high durability" or "delivery within 30 days."

[1130] Server: Based on the extracted characteristics and requirements, an AI matching algorithm is applied, which comprehensively evaluates performance, quality, price, delivery time, and emotional information to calculate the degree of match.

[1131] Input: Data structured by the NLP engine.

[1132] Output: The calculated match score.

[1133] Specifically, the server runs a matching algorithm to compare and evaluate the characteristics and requirements to calculate a score.

[1134] Step 4: Generate and communicate recommendations

[1135] Server: Based on the calculated match score, the server generates a list of optimal sellers for the buyer. The generated recommendation results are communicated to the buyer via email or app notification. The recommendation results are adjusted based on the user's emotional state and notified at the appropriate time.

[1136] Input: The calculated match score.

[1137] Output: Notification of recommendation results sent to buyer.

[1138] Specifically, when the server sends emails or in-app notifications, it takes the user's emotional state into consideration to determine the optimal timing and content.

[1139] Step 5: Rating and feedback

[1140] Users (sellers / buyers): After completing a transaction, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[1141] Input: Rating information from sellers and buyers.

[1142] Output: Rating and sentiment information stored in a database.

[1143] Server: Receives the input rating information and updates each user's reputation score. This rating information and sentiment information are reflected in the next matching algorithm.

[1144] Input: User rating information.

[1145] Output: The updated reputation score.

[1146] Specifically, the server stores the rating information in a database and calculates and updates the reputation score, which improves the accuracy of the next matching process.

[1147] (Application example 2)

[1148] 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."

[1149] Existing e-commerce systems simply match the product or service requirements of sellers and buyers, without taking into account the user's emotional state, making it difficult to achieve optimal matching. Even if an appropriate match is made, if the timing or method of receiving the information is inappropriate, the transaction is unlikely to be successful. Furthermore, feedback from post-transaction evaluations does not take into account emotions, making it difficult to fully utilize the feedback for the next match.

[1150] 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 sellers to input detailed information about products or services or upload documents; means for buyers to input requirements for desired products or services or upload documents; an emotion engine that recognizes emotions from user text or voice; means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; means for listing optimal matches between sellers and buyers based on the calculated degree of match and emotion information; means for notifying the seller and buyer of the recommendation results with optimal timing and content based on the emotion; means for collecting evaluation information and emotion information from sellers and buyers and updating reputation scores; and means for storing the input or uploaded information in a database. This enables optimal matching that takes the user's emotional state into consideration and notification at the appropriate time, improving the success rate of transactions and the accuracy of the next match.

[1151] A "Seller" is any person or company that accesses the system to provide details of a product or service.

[1152] A "buyer" is a person or company that enters requirements for a desired product or service into the system.

[1153] A "natural language processing engine" is a technology that analyzes input or uploaded text data and extracts product characteristics and requirements from the content.

[1154] The "AI matching algorithm" is a technology that calculates the degree of match between sellers and buyers based on extracted product characteristics and requirements, and performs optimal matching.

[1155] An "emotion engine" is a technology that recognizes emotions from text or voice input by the user, and records and uses them as data.

[1156] "Match degree" is an index showing the degree of match between the product characteristics provided by the seller and the requirements of the buyer.

[1157] "Recommended results" are results that list the best matches between sellers and buyers based on match degree and sentiment information.

[1158] A "reputation score" is a trust index calculated based on post-transaction evaluation information and sentiment information of sellers and buyers.

[1159] "Database" means a digital storage for storing information entered or uploaded by sellers and buyers.

[1160] The "notification means" is a technology that notifies sellers and buyers of the recommendation results at an appropriate time based on the calculated match degree and sentiment information.

[1161] System Configuration

[1162] The system for realizing this invention mainly comprises a server, a terminal, and a user. The terminal includes a smartphone, a PC, a tablet, etc., which the user uses to access the system.

[1163] Program processing overview

[1164] Entering seller and buyer information

[1165] Sellers and buyers access the system using terminals. Sellers enter details of goods or services or upload documents. For example, they can upload files such as product specifications or brochures. Buyers enter requirements for the products or services they want or upload documents in the same way. The server receives this information and stores it in a database.

[1166] emotion recognition

[1167] The server uses an emotion engine to recognize emotions from the text or voice input by the user. Specifically, it calls the emotion recognition engine (API) and analyzes the input text or voice data. Through this analysis, the user's emotional information is recorded as data.

[1168] Natural Language Processing

[1169] The server uses a natural language processing engine to analyze uploaded documents and input text data to extract sellers' product characteristics and buyers' requirements. The extracted information is stored in a database as structured data.

[1170] Applying AI matching algorithms

[1171] The server applies an AI matching algorithm based on structured data and sentiment information to calculate the degree of match between sellers and buyers, taking into account multiple factors such as performance, quality, price, delivery time, and sentiment.

[1172] Notification of recommended results

[1173] Based on the calculated match rate and emotion information, the server lists the best recommended results and notifies the seller and buyer. Notifications are given at the appropriate time based on the emotion information. For example, if the buyer is in a hurry, a notification is given immediately.

[1174] Ratings and Feedback

[1175] After the transaction is completed, the seller and buyer input their ratings of each other using their terminals. The server collects these ratings and sentiment information and updates the reputation score. The updated ratings are reflected in the next matching algorithm.

[1176] Hardware and software used

[1177] Emotion Recognition Engine: Emotion Recognition API

[1178] Natural Language Processing Engine: NLP (Natural Language Processing) Engine

[1179] Database: NoSQL or relational database

[1180] Matching algorithm: AI algorithm based on machine learning model

[1181] Specific examples

[1182] A seller uploads specifications and brochures for a newly developed electronic component. The server receives these files and stores them in a database as text information. At the same time, the seller's emotions (e.g., confidence) at the time of uploading are recorded.

[1183] A buyer enters that they are looking for durable, heat-resistant electronic components that can be delivered within 30 days. The server receives the requirements and stores them in a database. The buyer's emotion (e.g., impatience) is recorded.

[1184] The server analyzes the information uploaded by the seller and the requirements entered by the buyer, and calculates the degree of match using an AI matching algorithm, which determines that the seller's electronic components are the best fit for the buyer's requirements.

[1185] Notifications are instantaneous and based on the buyer's emotional state. Buyers receive notifications, review details, and initiate transactions.

[1186] Prompt Sentence Examples

[1187] Please recognize the sentiment in the following documents:

[1188] "It's a high-performance smartphone. It's equipped with the latest technology."

[1189] Express your emotions.

[1190]

[1191] Identify the following requirements and identify the emotions:

[1192] "I'm looking for a durable smartphone that costs under 100,000 yen."

[1193] Please output your requirements and feelings.

[1194] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1195] Step 1: User logs into the system

[1196] A user (seller or buyer) accesses the system from a device (smartphone, PC, etc.) and enters authentication information to log in. The server verifies the authentication information and starts a user session, which allows the seller or buyer to enter information individually.

[1197] Step 2: Seller enters or uploads product information

[1198] Sellers use terminals to upload documents such as product specifications and brochures to the system, or manually enter detailed information. The server receives this information and stores it in a database. In addition, the emotion engine recognizes emotions from the text and voice data uploaded or entered, and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and corresponding emotion data is generated.

[1199] Step 3: Buyer enters or uploads requirements information

[1200] Buyers use a terminal to input their requirements for the desired product or service in natural language, or upload a document summarizing their requirements. The server receives this information and stores it in a database. The emotion engine recognizes emotions from the input text or voice and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and the corresponding emotion data is generated.

[1201] Step 4: Analyze data using natural language processing

[1202] The server uses a natural language processing engine to analyze uploaded documents and entered text data. Through this analysis, the seller's product characteristics and the buyer's requirements are extracted and stored in a database as structured data. Specifically, specific attributes (e.g., high durability, low price, etc.) are extracted through text analysis, and each attribute is recorded in the corresponding field.

[1203] Step 5: Applying AI matching algorithms

[1204] The server applies an AI matching algorithm based on the structured data and emotional data extracted through natural language processing. This calculates the degree of match between the seller and buyer. The calculation process takes into account multiple factors, including performance, quality, price, delivery time, and emotional state. The higher the degree of match, the better the compatibility between the seller and buyer.

[1205] Step 6: List and notify recommended results

[1206] The server lists the best recommended results based on the calculated match rate and emotional information. The recommended results are stored in a database and are notified to sellers and buyers at the appropriate time based on emotional information (e.g., immediate notification if the buyer is in a hurry). A notification is displayed on the terminal, allowing the user to check the details.

[1207] Step 7: Post-trade evaluation and feedback

[1208] After completing a transaction, sellers and buyers use their terminals to input their ratings of the other party into the system. The server receives the entered rating and sentiment information and stores it in a database. Each user's reputation score is updated and reflected in the next matching algorithm. This improves the matching accuracy of the entire system.

[1209] Step 8: Reflecting in the next match

[1210] The server then incorporates the collected evaluation and sentiment information into the next matching algorithm, enabling even more accurate matching based on past transaction data and sentiment data, thereby continuously improving the user experience.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] [Fourth embodiment]

[1215] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1216] 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.

[1217] 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).

[1218] 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.

[1219] 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.

[1220] 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).

[1221] 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.

[1222] 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.

[1223] 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.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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."

[1228] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[1229] Input and upload

[1230] Seller Input and Upload

[1231] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. Here, the seller can easily enter detailed information about their products and services. Specifically, the system provides the ability to upload documents such as product specifications, manuals, brochures, and price lists. There is also a form for manually entering detailed information.

[1232] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. No analysis of the information is performed at this stage.

[1233] Buyer Input and Upload

[1234] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[1235] Server: The entered requirements and uploaded documents are also received by the server and stored in a database.

[1236] Data analysis and matching

[1237] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[1238] Server: Next, an AI matching algorithm is applied. Based on the extracted characteristics and requirements, the degree of match between the seller and the buyer is calculated. The AI ​​matching algorithm takes into account multiple factors (performance, quality, price, delivery time, etc.) to calculate the overall degree of match.

[1239] Recommendations and Notifications

[1240] Server: Based on the calculated match score, a list of the best sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notification.

[1241] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[1242] Ratings and Feedback

[1243] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of each other into the system.

[1244] Server: The server receives the entered rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm, resulting in more accurate matching.

[1245] Specific examples

[1246] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system, which stores detailed information about the product's performance and quality in a database.

[1247] Example 2 (Buyer): Manufacturer B enters requirements such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database.

[1248] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that Manufacturer A's electronic components are the best fit for Manufacturer B's requirements.

[1249] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Manufacturer B receives the notification, checks the details, and starts the transaction.

[1250] This embodiment realizes optimal matching that takes into account the multifaceted needs of sellers and buyers.

[1251] The processing flow will be explained below.

[1252] Step 1:

[1253] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[1254] Step 2:

[1255] Server: Receives uploaded documents and manually entered data and stores it in a database.

[1256] Step 3:

[1257] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[1258] Step 4:

[1259] Server: Receives input requirements and uploaded documents and stores them in a database.

[1260] Step 5:

[1261] Server: Analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. Extracts characteristics such as performance, quality, price, and delivery time from the seller's product information, and extracts the required performance, quality, price, delivery time, and other conditions from the buyer's requirements information.

[1262] Step 6:

[1263] Server: Based on the extracted characteristics and conditions, an AI matching algorithm is applied, taking into account various factors such as performance, quality, price, and delivery time to calculate the degree of match between sellers and buyers.

[1264] Step 7:

[1265] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[1266] Step 8:

[1267] User (Buyer): After receiving the notification, the user logs in to the system to check the recommended seller's information and decides whether to proceed with the transaction based on the recommendation.

[1268] Step 9:

[1269] User (seller / buyer): After the transaction is completed, the user enters their evaluation of the other party into the system.

[1270] Step 10:

[1271] Server: Receives the entered rating information and updates each user's reputation score. This is reflected in the next matching algorithm, achieving even more accurate matching.

[1272] Through these steps, the system achieves optimal matching that takes into account the multifaceted needs of sellers and buyers.

[1273] Example 1

[1274] 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."

[1275] In e-commerce, it has been difficult for sellers and buyers to find the best partner to meet their respective needs using conventional methods. In particular, it has been difficult to achieve effective matching because detailed product or service information and requirements cannot be accurately communicated, and evaluation information from both parties cannot be reflected in the transaction. This has led to issues such as reduced transaction efficiency and a loss of satisfaction for both sellers and buyers.

[1276] 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.

[1277] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for saving the uploaded information in a database; a means for analyzing the saved information with a natural language processing engine and extracting product characteristics and buyer requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for generating a list of sellers optimal for the buyer based on the calculated degree of match; a means for notifying the buyer of the generated list by email or via an app notification function; and a means for collecting evaluation information from sellers and buyers and updating reputation scores. This enables optimal matching based on the needs of sellers and buyers, enabling efficient and satisfying transactions.

[1278] "Seller" refers to a company or individual that offers a product or service.

[1279] "Buyer" refers to a company or individual who wants a product or service.

[1280] "Input" refers to the act of manually registering information into the system.

[1281] "Upload" refers to the act of transferring a data file, such as a document, to the system.

[1282] A "means" refers to a method or device used to achieve a particular purpose.

[1283] A "natural language processing engine" refers to software that analyzes natural language, understands its meaning, and extracts the necessary information.

[1284] "Product characteristics" refers to attributes that indicate the product's performance, quality, specifications, etc.

[1285] "Buyer requirements" refers to the specific conditions and wishes of the buyer regarding the product or service they desire.

[1286] "AI matching algorithm" refers to an algorithm that uses artificial intelligence to calculate the optimal combination between sellers and buyers.

[1287] "Match degree" refers to an indicator that shows how well the products or services offered by the seller meet the buyer's requirements.

[1288] "Database" refers to a system in which information is organized and stored so that it can be searched and accessed as needed.

[1289] A "list" refers to a collection of items enumerated chronologically or sequentially.

[1290] "Email" refers to a means of sending and receiving text messages and files over the Internet.

[1291] "App notification function" refers to the function that allows an application to provide information to the user in real time.

[1292] "Evaluation information" refers to evaluation data that a seller or buyer makes of the other party after a transaction is completed.

[1293] "Reputation score" refers to a numerical value that indicates the trustworthiness and reliability of a user, calculated based on evaluation information.

[1294] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and AI performs optimal matching based on that information. An embodiment of this system will be described in detail.

[1295] Hardware and software used

[1296] Server: The central part of the system, containing the database management system (DBMS), natural language processing engine (NLP engine), and computing resources to run the AI ​​matching algorithm.

[1297] Terminal: Provides an interface for sellers and buyers to input or upload information. Specifically, a PC, tablet, smartphone, etc. is used.

[1298] System Structure

[1299] 1. User Authentication

[1300] Users (sellers and buyers) log in to the system and enter their ID and password. The server queries the database for this information and performs authentication. If authentication is successful, the user is redirected to the trading dashboard.

[1301] 2. Seller's product information input and upload

[1302] The seller accesses the "Product / Service Registration" screen through the terminal and enters the details of the product or service or uploads a document. The terminal sends the entered information to the server, which stores this data in a database.

[1303] 3. Buyer's requirements input and upload

[1304] Buyers access the "Requirements Registration" screen through their terminal and enter their requirements for the product or service they are looking for, or upload a document summarizing their requirements. The terminal then sends the entered information to the server, which then stores this data in a database.

[1305] 4. Data Analysis

[1306] The server analyzes the stored information using a natural language processing engine (NLP engine), extracting elements such as product characteristics, quality, and delivery dates from the document content and re-saving them as structured data.

[1307] 5. Applying the Matching Algorithm

[1308] The server applies an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements, comprehensively evaluating multiple factors (performance, quality, price, delivery time, etc.) to determine suitability.

[1309] 6. Generation and notification of recommendation results

[1310] The server generates a list of optimal sellers based on the calculated match score, and the generated list is notified to the buyer via email or the app's notification function.

[1311] 7. Ratings and Feedback

[1312] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The server collects this rating information and updates each user's reputation score. This reputation score is reflected in the next matching algorithm.

[1313] Specific examples

[1314] Example 1 (Seller):

[1315] Manufacturers upload specifications and brochures for newly developed electronic components to the system, which then stores detailed product performance and quality information in a database.

[1316] Example 2 (Buyer):

[1317] A manufacturer enters requirements for durable, heat-resistant electronic components that can be delivered within 30 days. The requirements are entered in natural language and stored in a database.

[1318] Example 3 (matching):

[1319] The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the degree of match based on these characteristics and requirements. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[1320] Example 4 (notification):

[1321] The server sends a notification to the manufacturer recommending the manufacturer's electronic parts. The manufacturer receives the notification, checks the details, and starts the transaction.

[1322] Prompt Sentence Examples

[1323] Example of prompt for manufacturer upload:

[1324] "Enter the details of your newly developed electronic components into the system. Upload specifications, manuals, brochures, and price lists."

[1325] Example prompt for manufacturer to enter requirements:

[1326] "Please enter your requirements for the electronic components you are looking for. Please be specific, such as high durability, heat resistance, delivery within 30 days, etc."

[1327] In this way, the system accurately grasps the needs of sellers and buyers and achieves optimal matching, thereby enabling effective and efficient e-commerce.

[1328] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1329] Step 1: User authentication

[1330] The user accesses the system and enters his or her ID and password.

[1331] The server performs authentication by querying the received ID and password against a database. The input is the user's ID and password, and the output is the success or failure of the authentication. If the authentication is successful, the server starts a session and redirects the user to the trading dashboard.

[1332] Step 2: Enter and upload seller's product information

[1333] The user (seller) uses a terminal to access the "Product / Service Registration" screen and manually enters detailed product or service information (e.g., specifications and price lists) or uploads a document file.

[1334] The terminal sends the information entered or uploaded by the seller to the server. The input is product information or document files, and the output is stored in a database.

[1335] The server stores the received data in a database. No analysis of the information is performed at this stage.

[1336] Step 3: Enter and upload buyer requirements

[1337] The user (buyer) uses a terminal to access the "Requirements Registration" screen and inputs the requirements for the product or service he or she desires, or uploads a document summarizing the requirements.

[1338] The terminal sends the information entered or uploaded by the buyer to the server. The input is requirement information or document files, and the output is saved in a database.

[1339] The server stores the received data in a database.

[1340] Step 4: Data analysis

[1341] The server analyzes the seller and buyer information stored in the database using a natural language processing (NLP) engine. The input is the stored product information and requirement information, and the output is the analyzed structured data.

[1342] The server extracts elements such as product characteristics, quality, and delivery date from the document contents and re-stores them as structured data.

[1343] Step 5: Applying the matching algorithm

[1344] The server runs an AI matching algorithm based on the characteristics and requirements extracted by the NLP engine. The input is the extracted characteristics and requirements, and the output is the calculated match score.

[1345] The server comprehensively evaluates multiple factors (performance, quality, price, delivery time, etc.) and calculates the degree of match between the seller and the buyer.

[1346] Step 6: Generate and communicate recommendations

[1347] The server generates a list of optimal sellers based on the calculated match scores. The input is the match score evaluation result, and the output is a recommended list of sellers.

[1348] The server notifies the buyer of the generated listing via email or the app's notification function.

[1349] Step 7: Review the recommendations and trade

[1350] The user (buyer) receives the notification and logs into the system to check the information of the recommended seller. The input is the notified seller information, and the output is the decision to start a transaction.

[1351] The terminal displays an interface for the buyer to check the recommendation results.

[1352] Step 8: Rating and Feedback

[1353] After completing a transaction, users (sellers and buyers) input their ratings of the other party. The input is rating information, and the output is an update of the reputation score.

[1354] The server stores the rating information in a database and updates each user's reputation score, which is then reflected in the next matching algorithm.

[1355] (Application example 1)

[1356] 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."

[1357] In conventional e-commerce systems, matching between sellers and buyers was not fully optimized, making it difficult for buyers to quickly and accurately find the products they wanted. Furthermore, notifying buyers of matching results and reflecting their ratings after a transaction often took time, leaving room for improvement in the user experience.

[1358] 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.

[1359] In this invention, the server includes: a means for a seller to input detailed information about a product or service or upload a document; a means for a buyer to input requirements for a desired product or service or upload a document; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; a means for listing optimal matches between sellers and buyers based on the calculated degree of match; a means for notifying the seller and buyer of the recommended results; a means for collecting evaluation information from sellers and buyers and updating reputation scores; and a means for an application installed on a smartphone to register product information and input requirements for a desired product using AI to recommend optimal products. This allows for quick and accurate matching between sellers and buyers, and reflects evaluation information after a transaction in the next match, improving the user experience.

[1360] "Sellers" are users who register their details in the system to offer products or services.

[1361] A "Buyer" is a user who inputs or uploads their requirements for the product or service they want into the system and searches for the most suitable product or service.

[1362] "Detailed product or service information" refers to information that indicates the characteristics of the products or services provided by the seller, such as product specifications, manuals, brochures, and price lists.

[1363] "Requirements" are information that indicates the specific conditions and specifications for the product or service that a buyer desires.

[1364] A "natural language processing engine" is a program that analyzes input documents or text and extracts characteristics and requirements from its content.

[1365] An "AI matching algorithm" is a calculation method that calculates the optimal match between sellers and buyers based on extracted characteristics and requirements.

[1366] "Match degree" is an evaluation index that indicates how well the seller's product characteristics match the buyer's requirements.

[1367] "Notification method" refers to the method used to notify sellers and buyers of matching results, and typically functions as an email or app notification.

[1368] A "reputation score" is a number that indicates each user's trustworthiness and transaction history, updated based on evaluation information collected from sellers and buyers.

[1369] A "smartphone application" is a software application that is installed on a smartphone and allows users to register products and enter requirements.

[1370] This invention describes a system that utilizes an AI matching algorithm that uses information on sellers and buyers to provide optimal products and services. The following is a specific embodiment of this system.

[1371] System Configuration

[1372] The system mainly consists of the following elements:

[1373] 1. A way for sellers to enter product or service details or upload documents

[1374] Sellers use a smartphone application to input or upload product or service specifications, manuals, brochures, price lists, etc. This data is sent to a server and stored in a database.

[1375] 2. A way for buyers to input or upload documents regarding their desired product or service requirements

[1376] Similarly, buyers use a smartphone application to input or upload detailed requirements for the products or services they want, which is also sent to the server and stored in a database.

[1377] 3. Means of analyzing information

[1378] The server analyzes the uploaded information using a natural language processing engine (e.g., NLTK library) to extract the seller's product characteristics and the buyer's requirements. NLTK is used for tokenizing and analyzing text.

[1379] 4. AI Matching Algorithm

[1380] Based on the extracted features and requirements, the server applies an AI matching algorithm to calculate the match degree, which uses the Scikit-learn library to calculate the TF-IDF vectorizer and cosine similarity.

[1381] 5. Notification of recommended results

[1382] Based on the calculated match score, the server lists the best possible matches between buyers and sellers and notifies the buyer of the recommended results, usually via email or app push notification.

[1383] 6. How reputation information is collected and reputation scores are updated

[1384] The server collects the rating information entered by the seller and buyer after the transaction, and updates each user's reputation score, which is reflected in the next matching algorithm.

[1385] explanation

[1386] The server uses a web framework based on Flask and performs a series of processes, from receiving information to storing, analyzing, and notifying users. Buyers and sellers input and upload information through a smartphone application, enabling fast and accurate matching.

[1387] Specific examples

[1388] Sellers upload detailed information about durable electronic components via a smartphone application. Buyers input requirements such as, "I'm looking for durable electronic components that can be delivered within 30 days." The server analyzes this information and calculates the cosine similarity based on the characteristics and requirements. As a result, it notifies the buyer of products with a high degree of match.

[1389] Prompt Sentence Examples

[1390] Below are some example prompts to input to the AI ​​model:

[1391] "Please enter your ruggedized electronics details."

[1392] Using these prompts, a generative AI model helps users enter the appropriate product information.

[1393] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1394] Step 1:

[1395] Sellers enter product or service details or upload documents through a smartphone application. The entered information can include product specifications, manuals, brochures, and price lists. The input data can be text or PDF, which is then formatted and sent to the server.

[1396] Step 2:

[1397] Through a smartphone application, buyers input detailed requirements for the product or service they are looking for, such as "high-durability parts" or "delivery within 30 days," which are also sent in text format to the server.

[1398] Step 3:

[1399] The server stores the data sent by the seller and buyer in a database. At this stage, the data is stored in its raw form and has not yet been parsed.

[1400] Step 4:

[1401] The server uses a natural language processing engine (NLTK library) to parse the seller's product information and the buyer's requirements, tokenizing the text and extracting important keywords and phrases. The input is the stored text data, and the output is structured characteristics and requirements data.

[1402] Step 5:

[1403] The server uses the structured characteristic and requirement data to apply an AI matching algorithm (Scikit-learn library) to calculate the match degree. The input is characteristic data and requirement data, and the output is a numerical value of the match degree between each seller and buyer. Specifically, the text is vectorized using a TF-IDF vectorizer and cosine similarity is calculated.

[1404] Step 6:

[1405] The server lists the best matches between sellers and buyers based on the degree of match, and the listed matching information is compiled into a ranking of recommended products and their degree of match.

[1406] Step 7:

[1407] The server notifies the buyer of the best match. Notification methods include email and push notifications on smartphone applications. For example, a notification message such as "We've found a product that matches your requirements" is sent.

[1408] Step 8:

[1409] After a transaction, sellers and buyers enter their evaluation information through a smartphone application. The evaluation information includes satisfaction with the transaction and reliability. This information is then sent back to the server.

[1410] Step 9:

[1411] The server updates the user's reputation score based on the collected rating information. The reputation score is reflected in the next matching algorithm to further improve matching accuracy. Specifically, the average rating is calculated and reflected in the user profile.

[1412] 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.

[1413] This invention relates to an electronic commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine perform optimal matching based on that information. An embodiment of this system will be described in detail.

[1414] Input and upload

[1415] Seller Input and Upload

[1416] User (Seller): The seller logs in to the system and opens the "Product / Service Registration" screen. They upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[1417] Server: Uploaded documents and manually entered data are received on the server side and stored in a database. In addition, the emotion engine recognizes and records the seller's emotions when entering or uploading documents and text.

[1418] Buyer Input and Upload

[1419] User (Buyer): Similarly, buyers log in to the system, open the "Requirements Registration" screen, and enter the requirements for the product or service they are looking for. For example, they can enter specific requirements in natural language, such as "highly durable parts" and "delivery within 30 days," or they can upload a table summarizing their requirements.

[1420] Server: The server receives the input requirements and uploaded documents and stores them in a database. The emotion engine recognizes and records the buyer's emotions when they input or upload them from voice or text.

[1421] Data analysis and matching

[1422] Server: The seller and buyer information stored in the database is first analyzed by a natural language processing (NLP) engine, which extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[1423] Server: Next, an AI matching algorithm is applied. It calculates the degree of match between sellers and buyers by taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine. The AI ​​matching algorithm calculates a comprehensive degree of match, taking into account various factors such as performance, quality, price, delivery time, and the user's emotional state.

[1424] Recommendations and Notifications

[1425] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[1426] User (Buyer): The buyer who receives the notification logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[1427] Ratings and Feedback

[1428] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[1429] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[1430] Specific examples

[1431] Example 1 (Seller): Manufacturer A uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed product performance and quality information in a database. The seller's feelings at the time of uploading (e.g., confidence) are recorded.

[1432] Example 2 (Buyer): Manufacturer B enters requirements such as a highly durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language and are also stored in the database. The buyer's emotions (e.g., impatience) at the time of entry are recorded.

[1433] Example 3 (matching): The server analyzes the documents uploaded by Manufacturer A and the requirements of Manufacturer B, and calculates the degree of match based on these characteristics, requirements, and sentiment information. As a result, it is determined that Manufacturer A's electronic components are optimal for Manufacturer B's requirements.

[1434] Example 4 (Notification): The server sends a notification to Manufacturer B recommending electronic parts from Manufacturer A. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. Manufacturer B receives the notification, checks the details, and starts the transaction.

[1435] This embodiment allows for optimal matching that takes into account the multifaceted needs and emotional states of sellers and buyers.

[1436] The processing flow will be explained below.

[1437] Step 1:

[1438] User (Seller): Log in to the system and open the "Product & Service Registration" screen. Upload documents such as product specifications, manuals, brochures, and price lists, or manually enter the details.

[1439] Step 2:

[1440] Server: Receives uploaded documents and manually entered data and stores them in a database. In addition, it uses an emotion engine to analyze the voice and text entered or uploaded by the seller, recognizes and records the seller's emotions.

[1441] Step 3:

[1442] User (Buyer): Log in to the system and open the "Requirements Registration" screen. Enter the requirements for the product or service you want in natural language or upload a table summarizing your requirements.

[1443] Step 4:

[1444] Server: Receives input requirements and uploaded documents and stores them in a database. At this time, the emotion engine analyzes the voice and text input and uploads made by the buyer, recognizes and records the buyer's emotions.

[1445] Step 5:

[1446] Server: The seller and buyer information stored in the database is analyzed using a natural language processing (NLP) engine. The NLP engine extracts elements such as product characteristics, quality, and delivery dates from the document content and stores them as structured data.

[1447] Step 6:

[1448] Server: Next, the AI ​​matching algorithm is applied, including the emotional information recognized by the emotion engine, to calculate the degree of match between sellers and buyers based on the extracted characteristics and requirements, as well as the emotional information.

[1449] Step 7:

[1450] Server: Based on the calculated match score, it generates a list of the best sellers for the buyer. These recommendations are communicated to the buyer via email or app notification.

[1451] Step 8:

[1452] User (Buyer): After receiving the notification, the buyer logs into the system to check the information of the recommended seller and decides whether to proceed with the transaction based on the recommended information.

[1453] Step 9:

[1454] Users (sellers / buyers): After a transaction is completed, sellers and buyers enter their evaluations of each other into the system, and also record their emotional state during the transaction.

[1455] Step 10:

[1456] Server: Receives the input rating and emotion information and updates each user's reputation score. The rating and emotion information is reflected in the next matching algorithm, achieving even more accurate matching.

[1457] Through these steps, the system achieves optimal matching between sellers and buyers, taking into account the multifaceted needs and emotional information of both parties.

[1458] Example 2

[1459] 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."

[1460] Conventional e-commerce systems have difficulty efficiently matching the needs and requirements of sellers and buyers, and do not provide optimal recommendations that take into account the emotional state of the buyer during the transaction. As a result, transaction satisfaction and efficiency are low, and the evaluations of both sellers and buyers are often not reflected. Furthermore, there is a problem in that there are insufficient means to notify both sellers and buyers in a timely manner.

[1461] 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.

[1462] In this invention, the server includes: a means for sellers to input detailed information about products or services or upload documents; a means for buyers to input requirements for desired products or services or upload documents; a means for saving the input or uploaded information in a database and recognizing and recording emotions at the time of input; a means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; a means for applying an AI matching algorithm based on the extracted characteristics and requirements to calculate the degree of match and taking emotional information into consideration; a means for listing optimal matches between sellers and buyers based on the calculated degree of match and notifying the user of recommended results based on the user's emotional state; and a means for collecting evaluation information from sellers and buyers, recording their emotional states during the transaction, and updating the reputation score. This enables optimal matching that takes into consideration the multifaceted needs and emotional states of sellers and buyers.

[1463] "Goods and Services" refers to any physical goods or intangible services provided by the Seller.

[1464] "Seller" refers to a user who provides goods or services.

[1465] "Buyer" refers to a user who is seeking a product or service.

[1466] "Detailed Information" refers to information such as descriptions, specifications, and prices of products or services.

[1467] "Requirements" refers to the conditions and wishes of the buyer regarding the goods or services they are looking for.

[1468] "Document" refers to electronic documents such as PDF, Word, Excel, etc.

[1469] "Database" refers to an electronic structure for systematically storing and managing input or uploaded information.

[1470] "Emotion" refers to the emotional state (e.g., joy, sadness, impatience, confidence, etc.) sensed at the time of user input or upload.

[1471] A "natural language processing engine" is a program that analyzes text data and understands human language.

[1472] "AI matching algorithm" refers to an artificial intelligence program that calculates the optimal combination based on the seller's product characteristics and the buyer's requirements.

[1473] "Match degree" refers to the degree to which the requirements and characteristics of the seller and buyer match.

[1474] "Recommended result" refers to the combination between a seller and a buyer that is determined to be optimal based on the calculated match degree.

[1475] "Notification" refers to the notification of recommended results sent via email or in-app message.

[1476] "Rating Information" refers to the ratings and feedback that sellers and buyers give to each other after a transaction.

[1477] "Reputation score" refers to a numerical value that indicates user reliability and satisfaction, calculated based on collected evaluation information.

[1478] System Overview

[1479] This invention relates to an e-commerce system in which sellers and buyers provide information about products and services, and an AI and emotion engine use this information to perform optimal matching. The program processing of this system is explained in detail below.

[1480] Hardware and Software Used

[1481] Examples of hardware used include servers, terminals, and network devices, while software uses natural language processing engines, AI matching algorithms, emotion engines, and database management systems.

[1482] Specific processing of the program

[1483] Seller login and data entry

[1484] User (Seller): The seller logs into the system using a dedicated terminal or web browser, opens the "Product / Service Registration" screen, and uploads documents such as product specifications, manuals, brochures, and price lists, or manually enters the details.

[1485] Server: The server receives documents uploaded and information entered by sellers and stores them in a database. In addition, an emotion engine recognizes and records the seller's emotions when entering information from voice and text.

[1486] Buyer login and requirements input

[1487] User (Buyer): The buyer also logs in, opens the "Requirements Registration" screen, and enters the requirements for the product or service they are looking for in natural language, or uploads a table summarizing their requirements.

[1488] Server: The server stores the information entered or uploaded by the buyer in a database. At the same time, the emotion engine recognizes and records the buyer's emotions at the time of input.

[1489] Data analysis and matching

[1490] Server: The information stored in the database is analyzed by a natural language processing engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[1491] Next, an AI matching algorithm is applied, taking into account the extracted characteristics and requirements, as well as the emotional information recognized by the emotion engine, to calculate the degree of match between the seller and the buyer. The AI ​​matching algorithm calculates a comprehensive match that takes into account multiple factors such as performance, quality, price, delivery time, and the user's emotional state.

[1492] Recommendations and Notifications

[1493] Server: Based on the calculated match score, a list of optimal sellers for the buyer is generated. These recommendations are communicated to the buyer via email or app notifications. Furthermore, the recommendations are adjusted based on the user's emotional state to provide more appropriate timing and content for notifications.

[1494] User (Buyer): Upon receiving the notification, the buyer logs into the system, checks the information of the recommended seller, and decides whether to proceed with the transaction based on the recommendation.

[1495] Ratings and Feedback

[1496] Users (sellers / buyers): After completing a transaction, sellers and buyers input their ratings of each other. Their emotional state during the transaction is also recorded and added to the ratings.

[1497] Server: The server receives the entered rating information and updates each user's reputation score. The rating information and sentiment information are reflected in the next matching algorithm, resulting in more accurate matching.

[1498] Specific examples

[1499] Example 1 (Seller): A manufacturer uploads the specifications and brochures of a newly developed electronic component to the system. This saves detailed information about the product's performance and quality in a database. The emotion (e.g., confidence) at the time of uploading is recorded.

[1500] Example 2 (Buyer): A manufacturing company enters requirements, such as a durable, heat-resistant electronic component that can be delivered within 30 days. The requirements are entered in natural language, which is also stored in the database. The buyer's emotion (e.g., impatience) is recorded.

[1501] Example 3 (matching): The server analyzes the documents uploaded by the manufacturer and the manufacturer's requirements, and calculates the match degree based on these characteristics, requirements, and sentiment information. As a result, it is determined that the manufacturer's electronic components are the best fit for the manufacturer's requirements.

[1502] Example 4 (Notification): The server sends a notification to a manufacturer recommending the manufacturer's electronic components. Based on the buyer's emotional state, the notification is sent quickly to reduce impatience. The manufacturer receives the notification, checks the details, and starts the transaction.

[1503] Prompt Sentence Examples

[1504] "A manufacturing company is looking for durable electronic components that can be delivered within 30 days. Can you recommend the best seller for the system?"

[1505] This embodiment takes into account the multifaceted needs and emotional states of sellers and buyers to achieve optimal matching.

[1506] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1507] Step 1: Seller login and data entry

[1508] User (seller): The seller logs into the system using a dedicated terminal or web browser. After logging in, the seller opens the "Product / Service Registration" screen. Specific actions include uploading documents such as product specifications, manuals, brochures, and price lists, or manually entering detailed information. Input data includes the product name, description, price, and features.

[1509] Input: Product or service details entered by the seller or documents uploaded.

[1510] Output: Seller offers stored in the database.

[1511] Server: The server receives uploaded documents and manually entered data and stores them in a database. Specifically, the server processes and stores the data it receives. Furthermore, the emotion engine recognizes and records emotions from the voice and text input.

[1512] Input: Data entered or uploaded by the seller.

[1513] Output: Information stored in a database along with emotion recognition.

[1514] Step 2: Buyer login and input requirements

[1515] User (Buyer): Similarly, the buyer logs in to the system and opens the "Requirements Registration" screen. Specifically, the buyer enters the requirements for the desired product or service in natural language or uploads a table summarizing the requirements. The input data includes the desired product's features, delivery date, price range, etc.

[1516] Input: Product or service requirements entered by the buyer or uploaded documents.

[1517] Output: Buyer requirement information stored in the database.

[1518] Server: The server receives input requirements and uploaded documents and stores them in a database. At the same time, the emotion engine recognizes and records emotions from the buyer's voice and text input.

[1519] Input: Data entered or uploaded by the buyer.

[1520] Output: Information stored in a database along with emotion recognition.

[1521] Step 3: Data analysis and matching

[1522] Server: The information stored in the database is analyzed by a natural language processing (NLP) engine. Elements such as product characteristics, quality, and delivery dates are extracted from the document content and stored as structured data.

[1523] Input: Seller and buyer information stored in a database.

[1524] Output: Structured data from the natural language processing engine.

[1525] Specifically, the server calls the NLP engine to extract specific keywords and phrases from the document, such as "high durability" or "delivery within 30 days."

[1526] Server: Based on the extracted characteristics and requirements, an AI matching algorithm is applied, which comprehensively evaluates performance, quality, price, delivery time, and emotional information to calculate the degree of match.

[1527] Input: Data structured by the NLP engine.

[1528] Output: The calculated match score.

[1529] Specifically, the server runs a matching algorithm to compare and evaluate the characteristics and requirements to calculate a score.

[1530] Step 4: Generate and communicate recommendations

[1531] Server: Based on the calculated match score, the server generates a list of optimal sellers for the buyer. The generated recommendation results are communicated to the buyer via email or app notification. The recommendation results are adjusted based on the user's emotional state and notified at the appropriate time.

[1532] Input: The calculated match score.

[1533] Output: Notification of recommendation results sent to buyer.

[1534] Specifically, when the server sends emails or in-app notifications, it takes the user's emotional state into consideration to determine the optimal timing and content.

[1535] Step 5: Rating and feedback

[1536] Users (sellers / buyers): After completing a transaction, sellers and buyers enter their ratings of the other party into the system. Their emotional state during the transaction is also recorded and added to the ratings.

[1537] Input: Rating information from sellers and buyers.

[1538] Output: Rating and sentiment information stored in a database.

[1539] Server: Receives the input rating information and updates each user's reputation score. This rating information and sentiment information are reflected in the next matching algorithm.

[1540] Input: User rating information.

[1541] Output: The updated reputation score.

[1542] Specifically, the server stores the rating information in a database and calculates and updates the reputation score, which improves the accuracy of the next matching process.

[1543] (Application example 2)

[1544] 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."

[1545] Existing e-commerce systems simply match the product or service requirements of sellers and buyers, without taking into account the user's emotional state, making it difficult to achieve optimal matching. Even if an appropriate match is made, if the timing or method of receiving the information is inappropriate, the transaction is unlikely to be successful. Furthermore, feedback from post-transaction evaluations does not take into account emotions, making it difficult to fully utilize the feedback for the next match.

[1546] 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 sellers to input detailed information about products or services or upload documents; means for buyers to input requirements for desired products or services or upload documents; an emotion engine that recognizes emotions from user text or voice; means for analyzing the uploaded information with a natural language processing engine and extracting the seller's product characteristics and the buyer's requirements; means for applying an AI matching algorithm to calculate the degree of match based on the extracted characteristics and requirements; means for listing optimal matches between sellers and buyers based on the calculated degree of match and emotion information; means for notifying the seller and buyer of the recommendation results with optimal timing and content based on the emotion; means for collecting evaluation information and emotion information from sellers and buyers and updating reputation scores; and means for storing the input or uploaded information in a database. This enables optimal matching that takes the user's emotional state into consideration and notification at the appropriate time, improving the success rate of transactions and the accuracy of the next match.

[1547] A "Seller" is any person or company that accesses the system to provide details of a product or service.

[1548] A "buyer" is a person or company that enters requirements for a desired product or service into the system.

[1549] A "natural language processing engine" is a technology that analyzes input or uploaded text data and extracts product characteristics and requirements from the content.

[1550] The "AI matching algorithm" is a technology that calculates the degree of match between sellers and buyers based on extracted product characteristics and requirements, and performs optimal matching.

[1551] An "emotion engine" is a technology that recognizes emotions from text or voice input by the user, and records and uses them as data.

[1552] "Match degree" is an index showing the degree of match between the product characteristics provided by the seller and the requirements of the buyer.

[1553] "Recommended results" are results that list the best matches between sellers and buyers based on match degree and sentiment information.

[1554] A "reputation score" is a trust index calculated based on post-transaction evaluation information and sentiment information of sellers and buyers.

[1555] "Database" means a digital storage for storing information entered or uploaded by sellers and buyers.

[1556] The "notification means" is a technology that notifies sellers and buyers of the recommendation results at an appropriate time based on the calculated match degree and sentiment information.

[1557] System Configuration

[1558] The system for realizing this invention mainly comprises a server, a terminal, and a user. The terminal includes a smartphone, a PC, a tablet, etc., which the user uses to access the system.

[1559] Program processing overview

[1560] Entering seller and buyer information

[1561] Sellers and buyers access the system using terminals. Sellers enter details of goods or services or upload documents. For example, they can upload files such as product specifications or brochures. Buyers enter requirements for the products or services they want or upload documents in the same way. The server receives this information and stores it in a database.

[1562] emotion recognition

[1563] The server uses an emotion engine to recognize emotions from the text or voice input by the user. Specifically, it calls the emotion recognition engine (API) and analyzes the input text or voice data. Through this analysis, the user's emotional information is recorded as data.

[1564] Natural Language Processing

[1565] The server uses a natural language processing engine to analyze uploaded documents and input text data to extract sellers' product characteristics and buyers' requirements. The extracted information is stored in a database as structured data.

[1566] Applying AI matching algorithms

[1567] The server applies an AI matching algorithm based on structured data and sentiment information to calculate the degree of match between sellers and buyers, taking into account multiple factors such as performance, quality, price, delivery time, and sentiment.

[1568] Notification of recommended results

[1569] Based on the calculated match rate and emotion information, the server lists the best recommended results and notifies the seller and buyer. Notifications are given at the appropriate time based on the emotion information. For example, if the buyer is in a hurry, a notification is given immediately.

[1570] Ratings and Feedback

[1571] After the transaction is completed, the seller and buyer input their ratings of each other using their terminals. The server collects these ratings and sentiment information and updates the reputation score. The updated ratings are reflected in the next matching algorithm.

[1572] Hardware and software used

[1573] Emotion Recognition Engine: Emotion Recognition API

[1574] Natural Language Processing Engine: NLP (Natural Language Processing) Engine

[1575] Database: NoSQL or relational database

[1576] Matching algorithm: AI algorithm based on machine learning model

[1577] Specific examples

[1578] A seller uploads specifications and brochures for a newly developed electronic component. The server receives these files and stores them in a database as text information. At the same time, the seller's emotions (e.g., confidence) at the time of uploading are recorded.

[1579] A buyer enters that they are looking for durable, heat-resistant electronic components that can be delivered within 30 days. The server receives the requirements and stores them in a database. The buyer's emotion (e.g., impatience) is recorded.

[1580] The server analyzes the information uploaded by the seller and the requirements entered by the buyer, and calculates the degree of match using an AI matching algorithm, which determines that the seller's electronic components are the best fit for the buyer's requirements.

[1581] Notifications are instantaneous and based on the buyer's emotional state. Buyers receive notifications, review details, and initiate transactions.

[1582] Prompt Sentence Examples

[1583] Please recognize the sentiment in the following documents:

[1584] "It's a high-performance smartphone. It's equipped with the latest technology."

[1585] Express your emotions.

[1586]

[1587] Identify the following requirements and identify the emotions:

[1588] "I'm looking for a durable smartphone that costs under 100,000 yen."

[1589] Please output your requirements and feelings.

[1590] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1591] Step 1: User logs into the system

[1592] A user (seller or buyer) accesses the system from a device (smartphone, PC, etc.) and enters authentication information to log in. The server verifies the authentication information and starts a user session, which allows the seller or buyer to enter information individually.

[1593] Step 2: Seller enters or uploads product information

[1594] Sellers use terminals to upload documents such as product specifications and brochures to the system, or manually enter detailed information. The server receives this information and stores it in a database. In addition, the emotion engine recognizes emotions from the text and voice data uploaded or entered, and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and corresponding emotion data is generated.

[1595] Step 3: Buyer enters or uploads requirements information

[1596] Buyers use a terminal to input their requirements for the desired product or service in natural language, or upload a document summarizing their requirements. The server receives this information and stores it in a database. The emotion engine recognizes emotions from the input text or voice and stores the data together. During the input and output process, the uploaded document is analyzed in text format, and the corresponding emotion data is generated.

[1597] Step 4: Analyze data using natural language processing

[1598] The server uses a natural language processing engine to analyze uploaded documents and entered text data. Through this analysis, the seller's product characteristics and the buyer's requirements are extracted and stored in a database as structured data. Specifically, specific attributes (e.g., high durability, low price, etc.) are extracted through text analysis, and each attribute is recorded in the corresponding field.

[1599] Step 5: Applying AI matching algorithms

[1600] The server applies an AI matching algorithm based on the structured data and emotional data extracted through natural language processing. This calculates the degree of match between the seller and buyer. The calculation process takes into account multiple factors, including performance, quality, price, delivery time, and emotional state. The higher the degree of match, the better the compatibility between the seller and buyer.

[1601] Step 6: List and notify recommended results

[1602] The server lists the best recommended results based on the calculated match rate and emotional information. The recommended results are stored in a database and are notified to sellers and buyers at the appropriate time based on emotional information (e.g., immediate notification if the buyer is in a hurry). A notification is displayed on the terminal, allowing the user to check the details.

[1603] Step 7: Post-trade evaluation and feedback

[1604] After completing a transaction, sellers and buyers use their terminals to input their ratings of the other party into the system. The server receives the entered rating and sentiment information and stores it in a database. Each user's reputation score is updated and reflected in the next matching algorithm. This improves the matching accuracy of the entire system.

[1605] Step 8: Reflecting in the next match

[1606] The server then incorporates the collected evaluation and sentiment information into the next matching algorithm, enabling even more accurate matching based on past transaction data and sentiment data, thereby continuously improving the user experience.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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).

[1614] 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.

[1615] 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."

[1616] 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.

[1617] 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).

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] The following is further disclosed regarding the above embodiment.

[1629] (Claim 1)

[1630] A means for sellers to enter product or service details or upload documents;

[1631] A means for buyers to input or upload documents regarding their desired product or service requirements;

[1632] A means for analyzing the uploaded information using a natural language processing engine to extract the seller's product characteristics and the buyer's requirements;

[1633] A means for calculating the degree of match by applying an AI matching algorithm based on the extracted characteristics and requirements;

[1634] a means for listing optimal matches between buyers and sellers based on the calculated match scores;

[1635] a means for notifying the seller and the buyer of the recommendation;

[1636] a means for collecting rating information from sellers and buyers and updating reputation scores;

[1637] A system including:

[1638] (Claim 2)

[1639] 10. The system of claim 1, further comprising means for storing the entered or uploaded information in a database.

[1640] (Claim 3)

[1641] 10. The system of claim 1, further comprising means for incorporating seller and buyer reputation information into a next matching algorithm.

[1642] "Example 1"

[1643] (Claim 1)

[1644] A means for sellers to enter product or service details or upload documents;

[1645] A means for buyers to input or upload documents regarding their desired product or service requirements;

[1646] a means for storing the uploaded information in a database;

[1647] A means for analyzing the stored information using a natural language processing engine to extract product characteristics and buyer requirements;

[1648] A means for calculating the degree of match by applying an AI matching algorithm based on the extracted characteristics and requirements;

[1649] means for generating a list of optimal sellers for the buyer based on the calculated match degree;

[1650] A means of notifying buyers of the generated listing via email or app notification features;

[1651] a means for collecting rating information from sellers and buyers and updating reputation scores;

[1652] A system including:

[1653] (Claim 2)

[1654] 10. The system of claim 1, further comprising means for incorporating seller and buyer reputation information into a next matching algorithm.

[1655] (Claim 3)

[1656] 10. The system of claim 1, further comprising means for storing product characteristics and buyer requirements as structured data using a natural language processing engine.

[1657] "Application Example 1"

[1658] (Claim 1)

[1659] A means for sellers to enter product or service details or upload documents;

[1660] A means for buyers to input or upload documents regarding their desired product or service requirements;

[1661] A means for analyzing the uploaded information using a natural language processing engine to extract the seller's product characteristics and the buyer's requirements;

[1662] A means for calculating the degree of match by applying an AI matching algorithm based on the extracted characteristics and requirements;

[1663] a means for listing optimal matches between buyers and sellers based on the calculated match scores;

[1664] a means for notifying the seller and the buyer of the recommendation;

[1665] a means for collecting rating information from sellers and buyers and updating reputation scores;

[1666] The application is installed on smartphones, and sellers register product information and buyers input their desired product requirements, and AI recommends the most suitable product.

[1667] A system including:

[1668] (Claim 2)

[1669] 10. The system of claim 1, further comprising means for storing the entered or uploaded information in a database.

[1670] (Claim 3)

[1671] 10. The system of claim 1, further comprising means for incorporating seller and buyer reputation information into a next matching algorithm.

[1672] "Example 2: Combining Emotion Engines"

[1673] (Claim 1)

[1674] A means for sellers to enter details of their products or services or upload documents;

[1675] A means for buyers to input or upload documents regarding ...

Claims

1. A means for sellers to enter product or service details or upload documents; A means for buyers to input or upload documents regarding their desired product or service requirements; A means for analyzing the uploaded information using a natural language processing engine to extract the seller's product characteristics and the buyer's requirements; A means for calculating the degree of match by applying an AI matching algorithm based on the extracted characteristics and requirements; a means for listing optimal matches between buyers and sellers based on the calculated match scores; a means for notifying the seller and the buyer of the recommendation; a means for collecting rating information from sellers and buyers and updating reputation scores; A system including:

2. 10. The system of claim 1, further comprising means for storing the entered or uploaded information in a database.

3. 2. The system according to claim 1, further comprising means for reflecting seller and buyer reputation information in a next matching algorithm.

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